LMU BioDB 2017 - User contributions [en]

Page Desiigner

2017-12-15T06:44:30Z

ArashLari: /* Week 14 Team Progress Reflections */

[[Category:Page Desiigner]]
[[Category:Group Projects]]

==Page Design Team Members==
Coder: [[User:ArashLari|Arash Lari]]
 
Project Manager/ Quality Assurance: [[User:hhinsch|Hayden Hinsch]]
 
Data Analyst: [[User:Mbalducc|Mary Balducci]]
 
Designer: [[User:Nicolekalcic|Nicole Kalcic]]

==Page Desiigner Deliverables Page==

[[Page Desiigner Deliverables Page]]

==Team Schedule==
[[Page Desiigner Schedule]]

==Page Desiigner Uploaded Files==

[[Media:MaryHaydenWeek12JournalClubPresentation.pptx ‎]]
 
[[File:Data Driven Design.zip]]
 
[[File:Pagedesignprototype.zip]]
 
[[File:BiodbPageDesignFinalPresentation_.pptx]]

==Page Layout==
*<code>Below are a series of rough ideas we will use to create the layout of the page</code>
[[File:ACT1screenshot.png|500px]]
*We will be using a layout similar to the ACT1 gene page.
[[File:Sketchofpagedesign.png]]
*This is a rough sketch of what the page will look like.
*The section labeled '''description''' on the sketch will include the following:
**Gene ID from each database
**Description/Function (ensembl)
*Within the dropdown menus we will be incorporating the following:
**DNA Sequence (ensembl)
**Protein Sequence (UniProt)
**Locus tag (NCBI)
**Also Known As (NCBI)
**Consensus Sequence (JASPAR)
**Regulation (SGD)
**Interaction (SGD)
**Similar Proteins (UniProt)
**Gene Ontology (SGD)
**JASPAR
***Matrix ID
***Class Heat shock factors
***Family HSF factors
***Sequence Logo
***Frequency Matrix
*A zip file containing the html file of prototype page can be downloaded by clicking the <code>pagedesignprototype.zip</code> link below.
[[File:Pagedesignprototype.zip]]

==Annotated Bibliography==
===Mary's contribution:===
Pizzaro, Jewett, Nielson, Agosin. (2008) Growth Temperature Exerts Differential Physiological and Transcriptional Responses in Laboratory and Wine Strains of Saccharomyces Cerevisiae. "Applied and Environmental Microbiology", 74, 6358-6368, doi:10.1128/AEM.00602-08
*PubMed Abstract: https://www.ncbi.nlm.nih.gov/pubmed/18723660
*PubMed Central: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2570279/
*HTML format: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2570279/
*PDF format: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2570279/pdf/0602-08.pdf
*Copyright: 2008 American Society for Microbiology. It is open access.
*Publisher: American Society for Microbiology. This is a scientific society.
*Availability: Online and in print
*Did LMU pay a fee for this article: No
*I could not find the data associated with this article.

Homma, Iwahashi, Komatsu. (2003) Yeast Gene Expression During Growth at Low Temperature. ''Cryobiology'', 46, doi: 10.1016/S0011-2240(03)00028-2
*PubMed abstract: https://www.ncbi.nlm.nih.gov/pubmed/12818212
*PubMed Central:
*HTML format: http://www.sciencedirect.com/science/article/pii/S0011224003000282?via%3Dihub
*PDF format: https://ac.els-cdn.com/S0011224003000282/1-s2.0-S0011224003000282-main.pdf?_tid=aa7fa05e-c5d2-11e7-88ae-00000aacb361&acdnat=1510289609_6ec25501ce7af9dcc6b521ef8a024164
*Copyright 2003 Elsevier Science (USA). It is open access.
*Publisher: Elsevier. This is a for-profit publisher
*Availability: Online and in print
*Did LMU pay a fee for this article: No
*I could not find the data associated with this article.

===Hayden's Contribution===

Dahlquist, K. D., Fitzpatrick, B. G., Camacho, E. T., Entzminger, S. D., & Wanner, N. C. (2015). Parameter Estimation for Gene Regulatory Networks from Microarray Data: Cold Shock Response in Saccharomyces cerevisiae. Bulletin of Mathematical Biology, 77(8), 1457–1492. http://doi.org/10.1007/s11538-015-0092-6
* PubMed Abstract: https://www.ncbi.nlm.nih.gov/pubmed/26420504
* PubMed Central: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4636536/
* Publisher Full Text (HTML): https://link.springer.com/article/10.1007/s11538-015-0092-6
* Publisher Full Text (PDF): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4636536/pdf/11538_2015_Article_92.pdf
* Copyright: The Author(s) 2015. Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
* Publisher: Bulletin of Mathematical Biology(scientific society)
* Availability: Online
* Did LMU pay a fee for this article: No
* Data: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4636536/bin/11538_2015_92_MOESM1_ESM.zip

Ando, A., Nakamura, T., Murata, Y., Takagi, H. and Shima, J. (2007), Identification and classification of genes required for tolerance to freeze–thaw stress revealed by genome-wide screening of Saccharomyces cerevisiae deletion strains. FEMS Yeast Research, 7: 244–253. doi:10.1111/j.1567-1364.2006.00162.x
* PubMed Abstract: https://www.ncbi.nlm.nih.gov/pubmed/16989656
* PubMed Central: N/A
* Publisher Full Text (HTML): https://academic.oup.com/femsyr/article/7/2/244/616825?searchresult=1
* Publisher Full Text (PDF): http://onlinelibrary.wiley.com/doi/10.1111/j.1567-1364.2006.00162.x/epdf
* Copyright: Copyright © 2007, Oxford University Press; Open Access after time has elapsed: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. You must ask permission from the publisher to use the article.
* Publisher: Published by Oxford University Press(respected open access organization)
* Availability: Online and in print
* Did LMU pay a fee for this article: No
* Data: I couldn't find any supplementary data other than the data provided in the article.

==Journal Club Presentations==
===Powerpoint Slides and Respective Articles===
[[File:Data Driven Design.zip]]
* Liikkanen, L. (2017). The Data-Driven Design Era in Professional Web Design. Accessed November 13, 2017, from http://delivery.acm.org/10.1145/3130000/3121355/p52-liikkanen.pdf?ip=157.242.223.254&id=3121355&acc=ACTIVE%20SERVICE&key=80B0E63637265656%2E958C566062FD1C18%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=829067610&CFTOKEN=20249523&__acm__=1510614806_1e8b314e23f771f9b139d99596801365
 
[[Media:MaryHaydenWeek12JournalClubPresentation.pptx ‎]]
*Pizzaro, Jewett, Nielson, Agosin. (2008) Growth Temperature Exerts Differential Physiological and Transcriptional Responses in Laboratory and Wine Strains of Saccharomyces Cerevisiae. "Applied and Environmental Microbiology", 74, 6358-6368, doi:10.1128/AEM.00602-08

==Executive Summaries==

===[[Week 11]] Executive Summaries===
====Arash====
This week, I worked on researching the given article and the development terms within it as well as prepare a presentation on the article. I also began thinking of some page layout ideas with Nicole to prepare to implement in the coming weeks. All of this information could be found on my Week 11 page: [[ArashLari_Week_11|ArashLari Week 11]].
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 02:09, 14 November 2017 (PST)
====Mary====
This week, I worked on creating an annotated bibliography. The articles I found were primary sources about experiments done involving cold shock on yeast, and the use of microarrays to show results. I used multiple databases to find these articles, and while doing so I learned what key words got me the results closest to what I wanted. While writing the annotated bibliography, I also learned more about the articles, such as when they were written, who published them, what kind of publisher they are, and other information. All of this is recorded in my [[mbalducc Week 11| Week 11]] page.

[[User:Mbalducc|Mbalducc]] ([[User talk:Mbalducc|talk]]) 23:03, 13 November 2017 (PST)

====Nicole====
This past week, I read the article ''The Data-Driven Design Era in Professional Web Design'' by L.A. Liikkanen. This allowed me to complete my individual journal: an informative outline of the article and list of ten definitions. Arash and I worked together to complete a 15 minute presentation that we are going to give on Tuesday. The presentation gives an overview of our individual journals and what we each learned. You can access more information on what I did this week here: [[Nicolekalcic Week 11]].
[[User:Nicolekalcic|Nicolekalcic]] ([[User talk:Nicolekalcic|talk]]) 01:27, 14 November 2017 (PST)
====Hayden====
This week I created part of the annotated bibliography we will be using for our final project. I conducted searched for "yeast cold shock microarray" articles and found which two articles I was going to use for the bibliography. The majority of the time this week was spent on finding the information needed for the annotated bibliography which can be found below on this page. More detailed information can be found on my [[hhinsch Week 11]] page. This week I became proficient in finding reliable article to use for our final project. This week we also created the group's template and wiki page. [[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 22:43, 13 November 2017 (PST)

===[[Week 12]] Executive Summaries===
====Arash====
This week was rather light compared to the previous week. We mostly set up the files on our personal computers, which was convenient because as a computer science major I already had most of the necessary files. Nicole and I talked about some preliminary designs and set up a rough schedule that we'll make concrete after the break. This can be found on my week 12 page [[ArashLari Week 12] | Arash Lari Week 12]. [[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 20:43, 20 November 2017 (PST)

====Mary====
This week I read an article about an experiment done on wine and lab strains of yeast in different temperatures. The experimenters used DNA microarrays to determine which genes were affected by the change in temperatures and which were different based on the different strains of yeast. While reading the article, I can across words I had never seen before, so I also found and recorded the definitions of these words. I then made an outline of the article, to use as a basis for my presentation with our project manager, [[user:hhinsch| Hayden]]. All of this is on my [[mbalducc Week 12| Week 12]] page.

[[User:Mbalducc|Mbalducc]] ([[User talk:Mbalducc|talk]]) 19:51, 19 November 2017 (PST)

====Nicole====
Because Arash and I presented last week, we didn't have a heavy work load this week. Arash explained a lot of technical things to me, and we completed the first coding milestones and talked about design possibilities. I officially have a GitHub (nicolekal). I also downloaded the software I needed but didn't have onto my laptop. A full update can be found on: [[Nicolekalcic Week 12] | Nicole Kalcic Week 12]. [[User:Nicolekalcic|Nicolekalcic]] ([[User talk:Nicolekalcic|talk]]) 23:45, 20 November 2017 (PST)

====Hayden====
This week I worked with Mary specifically in order to complete our journal club presentation. As a group we worked together to make corrections to the [[Page Desiigner]] group page in order to complete the deliverables for this week. Mary and I created a schedule for our group along with helpful links. As a class we decided we will be using WhatsApp to communicate. I spent most of this week outlining the article (it was a pretty difficult read) and defining terms on my [[hhinsch Week 12]] page. [[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 17:42, 20 November 2017 (PST)

===[[Week 14]] Executive Summaries===
====Arash====
This week I worked on implementing our designs for the web page. Nicole and Hayden helped come up with some design ideas. We ended up deciding that ACT1 was a very well designed page and that it would be best to follow the format of that page. There's only a few things left to do but our portion of the project is nearly complete, the generic version of every gene's page needs just a few finishing touches. [[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 18:19, 4 December 2017 (PST)

====Mary====
This week I finished the Week 10 assignment, using YEASTRACT and GRNsight. I specifically chose one profile from the STEM results to use in YEASTRACT to find transcription factors that regulate a cluster of genes. First I uploaded the genes from profile #22 into YEASTRACT to find 20 significant transcription factors that regulate them. Then, I used Excel to get rid of the transcription factors that were not related to other transcription factors. Once I had those, I imported them into GRNsight to create a map of the transcription factors and their relations to each other. I took a screenshot of this map and uploaded it to my [[mbalducc Week 14| Week 14]] page.

[[User:Mbalducc|Mbalducc]] ([[User talk:Mbalducc|talk]]) 19:14, 4 December 2017 (PST)

====Nicole====

====Hayden====
This week I served as a "floater". I worked with Nicole and Arash in order to come up with how the page design prototype would look. We decided to use the ACT1 design along with Nicole's sketch in order to find a balance between aesthetic and function. The design we used is downloadable from this page and my [[hhinsch Week 14]] page. My [[hhinsch Week 14]] page has some more information regarding why we chose our current prototype design. I also spoke with Mary to make sure she knew I was there to help with her deliverables if needed. I also spent time with the other QA's this week in order to familiarize myself with the deliverables of the other groups. This was necessary for me to make sure we added everything to the page that is required. I uploaded images to my [hhinsch Week 14]] page as well as this page in order to further clarify our thought process behind the page prototype. [[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 20:16, 4 December 2017 (PST)

==Team Progress Reflections==

===[[Week 11]] Team Progress Reflections===

====Nicole====
# What worked?
#* We all work well in our positions. Arash and I worked together recently, so it was easy to coordinate what we are going to do as coder and designer. Everyone was open to opinions on the page and team name. Also, everyone communicated what they were having trouble finishing.
# What didn't work?
#* Like Mary said, we weren't able to meet in person. We planned out our meetup during class, but Friday ended up being a busy day for everyone.
# What will I do next to fix what didn't work?
#* Next week I will make sure to check in more with my partners. Even if we don't meet up, it is important to make sure everyone is caught up on the assignment and their individual parts.
====Mary====
# What worked?
#*I think as a team, we did a good job communicating and figuring out what needed to be done, and who would do each part.
# What didn't work?
#*We did not meet up outside of class. We had planned to, but we all ended up having conflicts so we couldn't meet.
# What will I do next to fix what didn't work?
#*I think this can be fixed just by making sure we all communicate in other ways, which is what we did. Through text messages we were able to talk about the assignment and help each other.
====Hayden====
# What worked?
#*We are all doing well communicating and completing our deliverables as individuals and as a group.
# What didn't work?
#*Everything worked well in regards to the group, but I did have some trouble finding all the information needed to create an annotated bibliography for the articles I used. Google Scholar gave me way too many results, and PubMed did not have a lot of articles that were either free or LMU had paid for.
# What will I do next to fix what didn't work?
#*I will continue to ask questions in class. I also feel more confident about finding useful articles after the completion of this assignment, so it shouldn't be a problem.
[[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 22:44, 13 November 2017 (PST)

====Arash====

# What worked?
#*We were good at maintaining our own individual tasks and keeping each other updated via text message.

# What didn't work?
#*We were unable to schedule time to meet and work effectively.

# What will I do next to fix what didn't work?
#*Hopefully we will have lighter course loads and more free time for the next assignment, but besides that we will begin working sooner and try to work in person more than digitally.

===[[Week 12]] Team Progress Reflections===

====Arash====
# What worked?
#*I think we all got a better grasp of the assignment and time management for the assignment. We have a clear idea where we're headed.
# What didn't work?
#* I wish we had more time to dedicate to this class, as often our schedules don't line up.
# What will I do next to fix what didn't work?
#*We'll try to be more proactive about all our tasks so they don't pile up. I'm planning on starting some work during the thanksgiving break to lighten the load afterwards.
====Mary====
# What worked?
#*I think that this week I felt more comfortable with the project as a whole. I also liked presenting after the other guilds because I got to see their presentations to get a better idea of what we should include in ours.
# What didn't work?
#*I wish that I had more time to read the article and make the presentation. I felt that I didn't understand the article completely, and had to read it a few times to be happy with the way that I understood it and formed my outline of it. Because of this, I couldn't spend as much time as I would've wanted on making the presentation.
# What will I do next to fix what didn't work?
#*I will try to spend more time on the entire project, instead of just one part.

====Nicole====
#What Worked?
#*Arash and I communicated via text and had a better idea of what we needed to do earlier on in the week, which made it easier to progress and do quality, time efficient work.
#What didn't work?
#*We weren't all able to meet in person. I don't know if this was a huge issue this week, but scheduling conflicts might become more of a problem in December.
#What will I do next week to fix what didn't work?
#*Next week I will try to make plans with the group on Tuesday. I am assuming everyone is going out of town for Thanksgiving, but perhaps we can make certain dates within the week to have things done by so the rest of the group can look over the work.

====Hayden====
#What Worked?
#*This week we were a little more prepared to work together and complete the deliverables. As time goes by the end goal is becoming more apparent.
#What didn't work?
#*I spent a lot of time reading the article multiple times and it is still a bit confusing to me. A lot of the vocabulary wasn't even found in the dictionaries we provided and a lot of the information needed a lot of background knowledge to comprehend.
#What will I do next week to fix what didn't work?
#*Next week I will try and do the bulk of the assignment earlier in the week rather than on the weekend, so I may have more time to understand the material.[[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 21:09, 20 November 2017 (PST)

===[[Week 14]] Team Progress Reflections===

====Arash====
# What worked?
#* Our design idea turned out pretty nicely, and was not that difficult to implement.
# What didn't work?
#* Coming up with what the final aesthetics will look like.
# What will I do next to fix what didn't work?
#* We will discuss the matter in class with the professors and our fellow students to see what works best for everyone.

====Mary====
#What worked?
#*Using YEASTRACT, Excel, and GRNsight to create a map of significant transcription factors in the profile I chose.
#What didn't work?
#*I have not fixed Weeks 8 and 10 as much as I would have wanted to.
#What will I do next to fix what didn't work?
#*Take more time, make sure I do things right the first time so I don't have to back and fix them later.

====Hayden====
# What worked?
#* We did a pretty good job getting everything done we needed to get done this week in a timely fashion while dealing with adversity.
# What didn't work?
#* We still need to complete the prototype and add the other groups' deliverables to our page.
# What will I do next to fix what didn't work?
#* We just simply need to take a final assessment of the deliverables as well as gather feedback from the entire class regarding the final layout of the page.

====Nicole====
# What worked?
#* We completed the basic structure of the page, and everyone was able to finish their work in time despite my absence on Thursday.
# What didn't work?
#* We haven't finalized anything yet, or implemented any information into the page.
# What will I do next to fix what didn't work?
#* We will talk with everyone in class, including the Dahlquist and Dionisio in order to make final decisions moving into Week 15.

===[[Week 15]] Team Progress Reflections===
====Arash====
This week I worked on adding the finishing touches to our info.html file in order to make the information easier to digest and nicer to look at. I also worked on my parts of the group presentation and report, which basically entailed me writing out, in detail, everything I did for the GRNsight project. I also wrote an individual statement of work that I emailed to the professors.
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 22:44, 14 December 2017 (PST)
[[ArashLari_Week_15|ArashLari Week 15]]
====Hayden====
====Mary====
====Nicole====
{{Pagedesiigner}}

Page Desiigner

2017-12-15T06:41:54Z

ArashLari: /* Week 14 Team Progress Reflections */

[[Category:Page Desiigner]]
[[Category:Group Projects]]

==Page Design Team Members==
Coder: [[User:ArashLari|Arash Lari]]
 
Project Manager/ Quality Assurance: [[User:hhinsch|Hayden Hinsch]]
 
Data Analyst: [[User:Mbalducc|Mary Balducci]]
 
Designer: [[User:Nicolekalcic|Nicole Kalcic]]

==Page Desiigner Deliverables Page==

[[Page Desiigner Deliverables Page]]

==Team Schedule==
[[Page Desiigner Schedule]]

==Page Desiigner Uploaded Files==

[[Media:MaryHaydenWeek12JournalClubPresentation.pptx ‎]]
 
[[File:Data Driven Design.zip]]
 
[[File:Pagedesignprototype.zip]]
 
[[File:BiodbPageDesignFinalPresentation_.pptx]]

==Page Layout==
*<code>Below are a series of rough ideas we will use to create the layout of the page</code>
[[File:ACT1screenshot.png|500px]]
*We will be using a layout similar to the ACT1 gene page.
[[File:Sketchofpagedesign.png]]
*This is a rough sketch of what the page will look like.
*The section labeled '''description''' on the sketch will include the following:
**Gene ID from each database
**Description/Function (ensembl)
*Within the dropdown menus we will be incorporating the following:
**DNA Sequence (ensembl)
**Protein Sequence (UniProt)
**Locus tag (NCBI)
**Also Known As (NCBI)
**Consensus Sequence (JASPAR)
**Regulation (SGD)
**Interaction (SGD)
**Similar Proteins (UniProt)
**Gene Ontology (SGD)
**JASPAR
***Matrix ID
***Class Heat shock factors
***Family HSF factors
***Sequence Logo
***Frequency Matrix
*A zip file containing the html file of prototype page can be downloaded by clicking the <code>pagedesignprototype.zip</code> link below.
[[File:Pagedesignprototype.zip]]

==Annotated Bibliography==
===Mary's contribution:===
Pizzaro, Jewett, Nielson, Agosin. (2008) Growth Temperature Exerts Differential Physiological and Transcriptional Responses in Laboratory and Wine Strains of Saccharomyces Cerevisiae. "Applied and Environmental Microbiology", 74, 6358-6368, doi:10.1128/AEM.00602-08
*PubMed Abstract: https://www.ncbi.nlm.nih.gov/pubmed/18723660
*PubMed Central: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2570279/
*HTML format: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2570279/
*PDF format: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2570279/pdf/0602-08.pdf
*Copyright: 2008 American Society for Microbiology. It is open access.
*Publisher: American Society for Microbiology. This is a scientific society.
*Availability: Online and in print
*Did LMU pay a fee for this article: No
*I could not find the data associated with this article.

Homma, Iwahashi, Komatsu. (2003) Yeast Gene Expression During Growth at Low Temperature. ''Cryobiology'', 46, doi: 10.1016/S0011-2240(03)00028-2
*PubMed abstract: https://www.ncbi.nlm.nih.gov/pubmed/12818212
*PubMed Central:
*HTML format: http://www.sciencedirect.com/science/article/pii/S0011224003000282?via%3Dihub
*PDF format: https://ac.els-cdn.com/S0011224003000282/1-s2.0-S0011224003000282-main.pdf?_tid=aa7fa05e-c5d2-11e7-88ae-00000aacb361&acdnat=1510289609_6ec25501ce7af9dcc6b521ef8a024164
*Copyright 2003 Elsevier Science (USA). It is open access.
*Publisher: Elsevier. This is a for-profit publisher
*Availability: Online and in print
*Did LMU pay a fee for this article: No
*I could not find the data associated with this article.

===Hayden's Contribution===

Dahlquist, K. D., Fitzpatrick, B. G., Camacho, E. T., Entzminger, S. D., & Wanner, N. C. (2015). Parameter Estimation for Gene Regulatory Networks from Microarray Data: Cold Shock Response in Saccharomyces cerevisiae. Bulletin of Mathematical Biology, 77(8), 1457–1492. http://doi.org/10.1007/s11538-015-0092-6
* PubMed Abstract: https://www.ncbi.nlm.nih.gov/pubmed/26420504
* PubMed Central: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4636536/
* Publisher Full Text (HTML): https://link.springer.com/article/10.1007/s11538-015-0092-6
* Publisher Full Text (PDF): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4636536/pdf/11538_2015_Article_92.pdf
* Copyright: The Author(s) 2015. Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
* Publisher: Bulletin of Mathematical Biology(scientific society)
* Availability: Online
* Did LMU pay a fee for this article: No
* Data: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4636536/bin/11538_2015_92_MOESM1_ESM.zip

Ando, A., Nakamura, T., Murata, Y., Takagi, H. and Shima, J. (2007), Identification and classification of genes required for tolerance to freeze–thaw stress revealed by genome-wide screening of Saccharomyces cerevisiae deletion strains. FEMS Yeast Research, 7: 244–253. doi:10.1111/j.1567-1364.2006.00162.x
* PubMed Abstract: https://www.ncbi.nlm.nih.gov/pubmed/16989656
* PubMed Central: N/A
* Publisher Full Text (HTML): https://academic.oup.com/femsyr/article/7/2/244/616825?searchresult=1
* Publisher Full Text (PDF): http://onlinelibrary.wiley.com/doi/10.1111/j.1567-1364.2006.00162.x/epdf
* Copyright: Copyright © 2007, Oxford University Press; Open Access after time has elapsed: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. You must ask permission from the publisher to use the article.
* Publisher: Published by Oxford University Press(respected open access organization)
* Availability: Online and in print
* Did LMU pay a fee for this article: No
* Data: I couldn't find any supplementary data other than the data provided in the article.

==Journal Club Presentations==
===Powerpoint Slides and Respective Articles===
[[File:Data Driven Design.zip]]
* Liikkanen, L. (2017). The Data-Driven Design Era in Professional Web Design. Accessed November 13, 2017, from http://delivery.acm.org/10.1145/3130000/3121355/p52-liikkanen.pdf?ip=157.242.223.254&id=3121355&acc=ACTIVE%20SERVICE&key=80B0E63637265656%2E958C566062FD1C18%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=829067610&CFTOKEN=20249523&__acm__=1510614806_1e8b314e23f771f9b139d99596801365
 
[[Media:MaryHaydenWeek12JournalClubPresentation.pptx ‎]]
*Pizzaro, Jewett, Nielson, Agosin. (2008) Growth Temperature Exerts Differential Physiological and Transcriptional Responses in Laboratory and Wine Strains of Saccharomyces Cerevisiae. "Applied and Environmental Microbiology", 74, 6358-6368, doi:10.1128/AEM.00602-08

==Executive Summaries==

===[[Week 11]] Executive Summaries===
====Arash====
This week, I worked on researching the given article and the development terms within it as well as prepare a presentation on the article. I also began thinking of some page layout ideas with Nicole to prepare to implement in the coming weeks. All of this information could be found on my Week 11 page: [[ArashLari_Week_11|ArashLari Week 11]].
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 02:09, 14 November 2017 (PST)
====Mary====
This week, I worked on creating an annotated bibliography. The articles I found were primary sources about experiments done involving cold shock on yeast, and the use of microarrays to show results. I used multiple databases to find these articles, and while doing so I learned what key words got me the results closest to what I wanted. While writing the annotated bibliography, I also learned more about the articles, such as when they were written, who published them, what kind of publisher they are, and other information. All of this is recorded in my [[mbalducc Week 11| Week 11]] page.

[[User:Mbalducc|Mbalducc]] ([[User talk:Mbalducc|talk]]) 23:03, 13 November 2017 (PST)

====Nicole====
This past week, I read the article ''The Data-Driven Design Era in Professional Web Design'' by L.A. Liikkanen. This allowed me to complete my individual journal: an informative outline of the article and list of ten definitions. Arash and I worked together to complete a 15 minute presentation that we are going to give on Tuesday. The presentation gives an overview of our individual journals and what we each learned. You can access more information on what I did this week here: [[Nicolekalcic Week 11]].
[[User:Nicolekalcic|Nicolekalcic]] ([[User talk:Nicolekalcic|talk]]) 01:27, 14 November 2017 (PST)
====Hayden====
This week I created part of the annotated bibliography we will be using for our final project. I conducted searched for "yeast cold shock microarray" articles and found which two articles I was going to use for the bibliography. The majority of the time this week was spent on finding the information needed for the annotated bibliography which can be found below on this page. More detailed information can be found on my [[hhinsch Week 11]] page. This week I became proficient in finding reliable article to use for our final project. This week we also created the group's template and wiki page. [[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 22:43, 13 November 2017 (PST)

===[[Week 12]] Executive Summaries===
====Arash====
This week was rather light compared to the previous week. We mostly set up the files on our personal computers, which was convenient because as a computer science major I already had most of the necessary files. Nicole and I talked about some preliminary designs and set up a rough schedule that we'll make concrete after the break. This can be found on my week 12 page [[ArashLari Week 12] | Arash Lari Week 12]. [[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 20:43, 20 November 2017 (PST)

====Mary====
This week I read an article about an experiment done on wine and lab strains of yeast in different temperatures. The experimenters used DNA microarrays to determine which genes were affected by the change in temperatures and which were different based on the different strains of yeast. While reading the article, I can across words I had never seen before, so I also found and recorded the definitions of these words. I then made an outline of the article, to use as a basis for my presentation with our project manager, [[user:hhinsch| Hayden]]. All of this is on my [[mbalducc Week 12| Week 12]] page.

[[User:Mbalducc|Mbalducc]] ([[User talk:Mbalducc|talk]]) 19:51, 19 November 2017 (PST)

====Nicole====
Because Arash and I presented last week, we didn't have a heavy work load this week. Arash explained a lot of technical things to me, and we completed the first coding milestones and talked about design possibilities. I officially have a GitHub (nicolekal). I also downloaded the software I needed but didn't have onto my laptop. A full update can be found on: [[Nicolekalcic Week 12] | Nicole Kalcic Week 12]. [[User:Nicolekalcic|Nicolekalcic]] ([[User talk:Nicolekalcic|talk]]) 23:45, 20 November 2017 (PST)

====Hayden====
This week I worked with Mary specifically in order to complete our journal club presentation. As a group we worked together to make corrections to the [[Page Desiigner]] group page in order to complete the deliverables for this week. Mary and I created a schedule for our group along with helpful links. As a class we decided we will be using WhatsApp to communicate. I spent most of this week outlining the article (it was a pretty difficult read) and defining terms on my [[hhinsch Week 12]] page. [[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 17:42, 20 November 2017 (PST)

===[[Week 14]] Executive Summaries===
====Arash====
This week I worked on implementing our designs for the web page. Nicole and Hayden helped come up with some design ideas. We ended up deciding that ACT1 was a very well designed page and that it would be best to follow the format of that page. There's only a few things left to do but our portion of the project is nearly complete, the generic version of every gene's page needs just a few finishing touches. [[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 18:19, 4 December 2017 (PST)

====Mary====
This week I finished the Week 10 assignment, using YEASTRACT and GRNsight. I specifically chose one profile from the STEM results to use in YEASTRACT to find transcription factors that regulate a cluster of genes. First I uploaded the genes from profile #22 into YEASTRACT to find 20 significant transcription factors that regulate them. Then, I used Excel to get rid of the transcription factors that were not related to other transcription factors. Once I had those, I imported them into GRNsight to create a map of the transcription factors and their relations to each other. I took a screenshot of this map and uploaded it to my [[mbalducc Week 14| Week 14]] page.

[[User:Mbalducc|Mbalducc]] ([[User talk:Mbalducc|talk]]) 19:14, 4 December 2017 (PST)

====Nicole====

====Hayden====
This week I served as a "floater". I worked with Nicole and Arash in order to come up with how the page design prototype would look. We decided to use the ACT1 design along with Nicole's sketch in order to find a balance between aesthetic and function. The design we used is downloadable from this page and my [[hhinsch Week 14]] page. My [[hhinsch Week 14]] page has some more information regarding why we chose our current prototype design. I also spoke with Mary to make sure she knew I was there to help with her deliverables if needed. I also spent time with the other QA's this week in order to familiarize myself with the deliverables of the other groups. This was necessary for me to make sure we added everything to the page that is required. I uploaded images to my [hhinsch Week 14]] page as well as this page in order to further clarify our thought process behind the page prototype. [[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 20:16, 4 December 2017 (PST)

==Team Progress Reflections==

===[[Week 11]] Team Progress Reflections===

====Nicole====
# What worked?
#* We all work well in our positions. Arash and I worked together recently, so it was easy to coordinate what we are going to do as coder and designer. Everyone was open to opinions on the page and team name. Also, everyone communicated what they were having trouble finishing.
# What didn't work?
#* Like Mary said, we weren't able to meet in person. We planned out our meetup during class, but Friday ended up being a busy day for everyone.
# What will I do next to fix what didn't work?
#* Next week I will make sure to check in more with my partners. Even if we don't meet up, it is important to make sure everyone is caught up on the assignment and their individual parts.
====Mary====
# What worked?
#*I think as a team, we did a good job communicating and figuring out what needed to be done, and who would do each part.
# What didn't work?
#*We did not meet up outside of class. We had planned to, but we all ended up having conflicts so we couldn't meet.
# What will I do next to fix what didn't work?
#*I think this can be fixed just by making sure we all communicate in other ways, which is what we did. Through text messages we were able to talk about the assignment and help each other.
====Hayden====
# What worked?
#*We are all doing well communicating and completing our deliverables as individuals and as a group.
# What didn't work?
#*Everything worked well in regards to the group, but I did have some trouble finding all the information needed to create an annotated bibliography for the articles I used. Google Scholar gave me way too many results, and PubMed did not have a lot of articles that were either free or LMU had paid for.
# What will I do next to fix what didn't work?
#*I will continue to ask questions in class. I also feel more confident about finding useful articles after the completion of this assignment, so it shouldn't be a problem.
[[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 22:44, 13 November 2017 (PST)

====Arash====

# What worked?
#*We were good at maintaining our own individual tasks and keeping each other updated via text message.

# What didn't work?
#*We were unable to schedule time to meet and work effectively.

# What will I do next to fix what didn't work?
#*Hopefully we will have lighter course loads and more free time for the next assignment, but besides that we will begin working sooner and try to work in person more than digitally.

===[[Week 12]] Team Progress Reflections===

====Arash====
# What worked?
#*I think we all got a better grasp of the assignment and time management for the assignment. We have a clear idea where we're headed.
# What didn't work?
#* I wish we had more time to dedicate to this class, as often our schedules don't line up.
# What will I do next to fix what didn't work?
#*We'll try to be more proactive about all our tasks so they don't pile up. I'm planning on starting some work during the thanksgiving break to lighten the load afterwards.
====Mary====
# What worked?
#*I think that this week I felt more comfortable with the project as a whole. I also liked presenting after the other guilds because I got to see their presentations to get a better idea of what we should include in ours.
# What didn't work?
#*I wish that I had more time to read the article and make the presentation. I felt that I didn't understand the article completely, and had to read it a few times to be happy with the way that I understood it and formed my outline of it. Because of this, I couldn't spend as much time as I would've wanted on making the presentation.
# What will I do next to fix what didn't work?
#*I will try to spend more time on the entire project, instead of just one part.

====Nicole====
#What Worked?
#*Arash and I communicated via text and had a better idea of what we needed to do earlier on in the week, which made it easier to progress and do quality, time efficient work.
#What didn't work?
#*We weren't all able to meet in person. I don't know if this was a huge issue this week, but scheduling conflicts might become more of a problem in December.
#What will I do next week to fix what didn't work?
#*Next week I will try to make plans with the group on Tuesday. I am assuming everyone is going out of town for Thanksgiving, but perhaps we can make certain dates within the week to have things done by so the rest of the group can look over the work.

====Hayden====
#What Worked?
#*This week we were a little more prepared to work together and complete the deliverables. As time goes by the end goal is becoming more apparent.
#What didn't work?
#*I spent a lot of time reading the article multiple times and it is still a bit confusing to me. A lot of the vocabulary wasn't even found in the dictionaries we provided and a lot of the information needed a lot of background knowledge to comprehend.
#What will I do next week to fix what didn't work?
#*Next week I will try and do the bulk of the assignment earlier in the week rather than on the weekend, so I may have more time to understand the material.[[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 21:09, 20 November 2017 (PST)

===[[Week 14]] Team Progress Reflections===

====Arash====
# What worked?
#* Our design idea turned out pretty nicely, and was not that difficult to implement.
# What didn't work?
#* Coming up with what the final aesthetics will look like.
# What will I do next to fix what didn't work?
#* We will discuss the matter in class with the professors and our fellow students to see what works best for everyone.

====Mary====
#What worked?
#*Using YEASTRACT, Excel, and GRNsight to create a map of significant transcription factors in the profile I chose.
#What didn't work?
#*I have not fixed Weeks 8 and 10 as much as I would have wanted to.
#What will I do next to fix what didn't work?
#*Take more time, make sure I do things right the first time so I don't have to back and fix them later.

====Hayden====
# What worked?
#* We did a pretty good job getting everything done we needed to get done this week in a timely fashion while dealing with adversity.
# What didn't work?
#* We still need to complete the prototype and add the other groups' deliverables to our page.
# What will I do next to fix what didn't work?
#* We just simply need to take a final assessment of the deliverables as well as gather feedback from the entire class regarding the final layout of the page.

====Nicole====
# What worked?
#* We completed the basic structure of the page, and everyone was able to finish their work in time despite my absence on Thursday.
# What didn't work?
#* We haven't finalized anything yet, or implemented any information into the page.
# What will I do next to fix what didn't work?
#* We will talk with everyone in class, including the Dahlquist and Dionisio in order to make final decisions moving into Week 15.
{{Pagedesiigner}}

===[[Week 15] Team Progress Reflections===
====Arash====
This week I worked on adding the finishing touches to our info.html file in order to make the information easier to digest and nicer to look at. I also worked on my parts of the group presentation and report, which basically entailed me writing out, in detail, everything I did for the GRNsight project. I also wrote an individual statement of work that I emailed to the professors.

Class Journal Week 15

2017-12-15T06:38:53Z

ArashLari: Created page with "= Electronic Lab Notebook = * This week was the week where we finalized our contributions * I finished the styling of the page, and detailed out the process in our group repo..."

= Electronic Lab Notebook =

* This week was the week where we finalized our contributions
* I finished the styling of the page, and detailed out the process in our group report and our group presentation
* Our group made & gave our presentation
* Our group also worked on the report
** We kept in pretty regular contact to ensure we all did our parts.

= Acknowledgements and References =
* I worked with Nicole, Hayden, and Mary for our Page Desiigner Deliverables.
* While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.

=Journal Entry=
{{Template:ArashLari}}
*Group Projects: https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Page_Desiigner
*Team Name: Page Desiigner
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 22:38, 14 December 2017 (PST)

Page Desiigner

2017-12-05T02:21:45Z

ArashLari: /* Team Progress Reflections */

[[Category:Page Desiigner]]
[[Category:Group Projects]]

==Page Design Team Members==
Coder: [[User:ArashLari|Arash Lari]]
 
Project Manager/ Quality Assurance: [[User:hhinsch|Hayden Hinsch]]
 
Data Analyst: [[User:Mbalducc|Mary Balducci]]
 
Designer: [[User:Nicolekalcic|Nicole Kalcic]]

==Team Schedule==
[[Page Desiigner Schedule]]

==Page Desiigner Uploaded Files==

[[Media:MaryHaydenWeek12JournalClubPresentation.pptx ‎]]
 
[[File:Data Driven Design.zip]]

==Page Layout==
*<code>Below are a series of rough ideas we will use to create the layout of the page</code>
[[File:ACT1screenshot.png|500px]]
*We will be using a layout similar to the ACT1 gene page.
[[File:Sketchofpagedesign.png]]
*This is a rough sketch of what the page will look like.
*The section labeled '''description''' on the sketch will include the following:
**Gene ID from each database
**Description/Function (ensembl)
*Within the dropdown menus we will be incorporating the following:
**DNA Sequence (ensembl)
**Protein Sequence (UniProt)
**Locus tag (NCBI)
**Also Known As (NCBI)
**Consensus Sequence (JASPAR)
**Regulation (SGD)
**Interaction (SGD)
**Similar Proteins (UniProt)
**Gene Ontology (SGD)
**JASPAR
***Matrix ID
***Class Heat shock factors
***Family HSF factors
***Sequence Logo
***Frequency Matrix

==Annotated Bibliography==
===Mary's contribution:===
Pizzaro, Jewett, Nielson, Agosin. (2008) Growth Temperature Exerts Differential Physiological and Transcriptional Responses in Laboratory and Wine Strains of Saccharomyces Cerevisiae. "Applied and Environmental Microbiology", 74, 6358-6368, doi:10.1128/AEM.00602-08
*PubMed Abstract: https://www.ncbi.nlm.nih.gov/pubmed/18723660
*PubMed Central: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2570279/
*HTML format: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2570279/
*PDF format: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2570279/pdf/0602-08.pdf
*Copyright: 2008 American Society for Microbiology. It is open access.
*Publisher: American Society for Microbiology. This is a scientific society.
*Availability: Online and in print
*Did LMU pay a fee for this article: No
*I could not find the data associated with this article.

Homma, Iwahashi, Komatsu. (2003) Yeast Gene Expression During Growth at Low Temperature. ''Cryobiology'', 46, doi: 10.1016/S0011-2240(03)00028-2
*PubMed abstract: https://www.ncbi.nlm.nih.gov/pubmed/12818212
*PubMed Central:
*HTML format: http://www.sciencedirect.com/science/article/pii/S0011224003000282?via%3Dihub
*PDF format: https://ac.els-cdn.com/S0011224003000282/1-s2.0-S0011224003000282-main.pdf?_tid=aa7fa05e-c5d2-11e7-88ae-00000aacb361&acdnat=1510289609_6ec25501ce7af9dcc6b521ef8a024164
*Copyright 2003 Elsevier Science (USA). It is open access.
*Publisher: Elsevier. This is a for-profit publisher
*Availability: Online and in print
*Did LMU pay a fee for this article: No
*I could not find the data associated with this article.

===Hayden's Contribution===

Dahlquist, K. D., Fitzpatrick, B. G., Camacho, E. T., Entzminger, S. D., & Wanner, N. C. (2015). Parameter Estimation for Gene Regulatory Networks from Microarray Data: Cold Shock Response in Saccharomyces cerevisiae. Bulletin of Mathematical Biology, 77(8), 1457–1492. http://doi.org/10.1007/s11538-015-0092-6
* PubMed Abstract: https://www.ncbi.nlm.nih.gov/pubmed/26420504
* PubMed Central: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4636536/
* Publisher Full Text (HTML): https://link.springer.com/article/10.1007/s11538-015-0092-6
* Publisher Full Text (PDF): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4636536/pdf/11538_2015_Article_92.pdf
* Copyright: The Author(s) 2015. Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
* Publisher: Bulletin of Mathematical Biology(scientific society)
* Availability: Online
* Did LMU pay a fee for this article: No
* Data: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4636536/bin/11538_2015_92_MOESM1_ESM.zip

Ando, A., Nakamura, T., Murata, Y., Takagi, H. and Shima, J. (2007), Identification and classification of genes required for tolerance to freeze–thaw stress revealed by genome-wide screening of Saccharomyces cerevisiae deletion strains. FEMS Yeast Research, 7: 244–253. doi:10.1111/j.1567-1364.2006.00162.x
* PubMed Abstract: https://www.ncbi.nlm.nih.gov/pubmed/16989656
* PubMed Central: N/A
* Publisher Full Text (HTML): https://academic.oup.com/femsyr/article/7/2/244/616825?searchresult=1
* Publisher Full Text (PDF): http://onlinelibrary.wiley.com/doi/10.1111/j.1567-1364.2006.00162.x/epdf
* Copyright: Copyright © 2007, Oxford University Press; Open Access after time has elapsed: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. You must ask permission from the publisher to use the article.
* Publisher: Published by Oxford University Press(respected open access organization)
* Availability: Online and in print
* Did LMU pay a fee for this article: No
* Data: I couldn't find any supplementary data other than the data provided in the article.

==Journal Club Presentations==
===Powerpoint Slides and Respective Articles===
[[File:Data Driven Design.zip]]
* Liikkanen, L. (2017). The Data-Driven Design Era in Professional Web Design. Accessed November 13, 2017, from http://delivery.acm.org/10.1145/3130000/3121355/p52-liikkanen.pdf?ip=157.242.223.254&id=3121355&acc=ACTIVE%20SERVICE&key=80B0E63637265656%2E958C566062FD1C18%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=829067610&CFTOKEN=20249523&__acm__=1510614806_1e8b314e23f771f9b139d99596801365
 
[[Media:MaryHaydenWeek12JournalClubPresentation.pptx ‎]]
*Pizzaro, Jewett, Nielson, Agosin. (2008) Growth Temperature Exerts Differential Physiological and Transcriptional Responses in Laboratory and Wine Strains of Saccharomyces Cerevisiae. "Applied and Environmental Microbiology", 74, 6358-6368, doi:10.1128/AEM.00602-08

==Executive Summaries==

===[[Week 11]] Executive Summaries===
====Arash====
This week, I worked on researching the given article and the development terms within it as well as prepare a presentation on the article. I also began thinking of some page layout ideas with Nicole to prepare to implement in the coming weeks. All of this information could be found on my Week 11 page: [[ArashLari_Week_11|ArashLari Week 11]].
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 02:09, 14 November 2017 (PST)
====Mary====
This week, I worked on creating an annotated bibliography. The articles I found were primary sources about experiments done involving cold shock on yeast, and the use of microarrays to show results. I used multiple databases to find these articles, and while doing so I learned what key words got me the results closest to what I wanted. While writing the annotated bibliography, I also learned more about the articles, such as when they were written, who published them, what kind of publisher they are, and other information. All of this is recorded in my [[mbalducc Week 11| Week 11]] page.

[[User:Mbalducc|Mbalducc]] ([[User talk:Mbalducc|talk]]) 23:03, 13 November 2017 (PST)

====Nicole====
This past week, I read the article ''The Data-Driven Design Era in Professional Web Design'' by L.A. Liikkanen. This allowed me to complete my individual journal: an informative outline of the article and list of ten definitions. Arash and I worked together to complete a 15 minute presentation that we are going to give on Tuesday. The presentation gives an overview of our individual journals and what we each learned. You can access more information on what I did this week here: [[Nicolekalcic Week 11]].
[[User:Nicolekalcic|Nicolekalcic]] ([[User talk:Nicolekalcic|talk]]) 01:27, 14 November 2017 (PST)
====Hayden====
This week I created part of the annotated bibliography we will be using for our final project. I conducted searched for "yeast cold shock microarray" articles and found which two articles I was going to use for the bibliography. The majority of the time this week was spent on finding the information needed for the annotated bibliography which can be found below on this page. More detailed information can be found on my [[hhinsch Week 11]] page. This week I became proficient in finding reliable article to use for our final project. This week we also created the group's template and wiki page. [[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 22:43, 13 November 2017 (PST)

===[[Week 12]] Executive Summaries===
====Arash====
This week was rather light compared to the previous week. We mostly set up the files on our personal computers, which was convenient because as a computer science major I already had most of the necessary files. Nicole and I talked about some preliminary designs and set up a rough schedule that we'll make concrete after the break. This can be found on my week 12 page [[ArashLari Week 12] | Arash Lari Week 12]. [[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 20:43, 20 November 2017 (PST)

====Mary====
This week I read an article about an experiment done on wine and lab strains of yeast in different temperatures. The experimenters used DNA microarrays to determine which genes were affected by the change in temperatures and which were different based on the different strains of yeast. While reading the article, I can across words I had never seen before, so I also found and recorded the definitions of these words. I then made an outline of the article, to use as a basis for my presentation with our project manager, [[user:hhinsch| Hayden]]. All of this is on my [[mbalducc Week 12| Week 12]] page.

[[User:Mbalducc|Mbalducc]] ([[User talk:Mbalducc|talk]]) 19:51, 19 November 2017 (PST)

====Nicole====
Because Arash and I presented last week, we didn't have a heavy work load this week. Arash explained a lot of technical things to me, and we completed the first coding milestones and talked about design possibilities. I officially have a GitHub (nicolekal). I also downloaded the software I needed but didn't have onto my laptop. A full update can be found on: [[Nicolekalcic Week 12] | Nicole Kalcic Week 12]. [[User:Nicolekalcic|Nicolekalcic]] ([[User talk:Nicolekalcic|talk]]) 23:45, 20 November 2017 (PST)

====Hayden====
This week I worked with Mary specifically in order to complete our journal club presentation. As a group we worked together to make corrections to the [[Page Desiigner]] group page in order to complete the deliverables for this week. Mary and I created a schedule for our group along with helpful links. As a class we decided we will be using WhatsApp to communicate. I spent most of this week outlining the article (it was a pretty difficult read) and defining terms on my [[hhinsch Week 12]] page. [[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 17:42, 20 November 2017 (PST)

===[[Week 14]] Executive Summaries===
====Arash====
This week I worked on implementing our designs for the web page. Nicole and Hayden helped come up with some design ideas. We ended up deciding that ACT1 was a very well designed page and that it would be best to follow the format of that page. There's only a few things left to do but our portion of the project is nearly complete, the generic version of every gene's page needs just a few finishing touches. [[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 18:19, 4 December 2017 (PST)

====Mary====

====Nicole====

====Hayden====

==Team Progress Reflections==

===[[Week 11]] Team Progress Reflections===

====Nicole====
# What worked?
#* We all work well in our positions. Arash and I worked together recently, so it was easy to coordinate what we are going to do as coder and designer. Everyone was open to opinions on the page and team name. Also, everyone communicated what they were having trouble finishing.
# What didn't work?
#* Like Mary said, we weren't able to meet in person. We planned out our meetup during class, but Friday ended up being a busy day for everyone.
# What will I do next to fix what didn't work?
#* Next week I will make sure to check in more with my partners. Even if we don't meet up, it is important to make sure everyone is caught up on the assignment and their individual parts.
====Mary====
# What worked?
#*I think as a team, we did a good job communicating and figuring out what needed to be done, and who would do each part.
# What didn't work?
#*We did not meet up outside of class. We had planned to, but we all ended up having conflicts so we couldn't meet.
# What will I do next to fix what didn't work?
#*I think this can be fixed just by making sure we all communicate in other ways, which is what we did. Through text messages we were able to talk about the assignment and help each other.
====Hayden====
# What worked?
#*We are all doing well communicating and completing our deliverables as individuals and as a group.
# What didn't work?
#*Everything worked well in regards to the group, but I did have some trouble finding all the information needed to create an annotated bibliography for the articles I used. Google Scholar gave me way too many results, and PubMed did not have a lot of articles that were either free or LMU had paid for.
# What will I do next to fix what didn't work?
#*I will continue to ask questions in class. I also feel more confident about finding useful articles after the completion of this assignment, so it shouldn't be a problem.
[[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 22:44, 13 November 2017 (PST)

====Arash====

# What worked?
#*We were good at maintaining our own individual tasks and keeping each other updated via text message.

# What didn't work?
#*We were unable to schedule time to meet and work effectively.

# What will I do next to fix what didn't work?
#*Hopefully we will have lighter course loads and more free time for the next assignment, but besides that we will begin working sooner and try to work in person more than digitally.

===[[Week 12]] Team Progress Reflections===

====Arash====
# What worked?
#*I think we all got a better grasp of the assignment and time management for the assignment. We have a clear idea where we're headed.
# What didn't work?
#* I wish we had more time to dedicate to this class, as often our schedules don't line up.
# What will I do next to fix what didn't work?
#*We'll try to be more proactive about all our tasks so they don't pile up. I'm planning on starting some work during the thanksgiving break to lighten the load afterwards.
====Mary====
# What worked?
#*I think that this week I felt more comfortable with the project as a whole. I also liked presenting after the other guilds because I got to see their presentations to get a better idea of what we should include in ours.
# What didn't work?
#*I wish that I had more time to read the article and make the presentation. I felt that I didn't understand the article completely, and had to read it a few times to be happy with the way that I understood it and formed my outline of it. Because of this, I couldn't spend as much time as I would've wanted on making the presentation.
# What will I do next to fix what didn't work?
#*I will try to spend more time on the entire project, instead of just one part.

====Nicole====
#What Worked?
#*Arash and I communicated via text and had a better idea of what we needed to do earlier on in the week, which made it easier to progress and do quality, time efficient work.
#What didn't work?
#*We weren't all able to meet in person. I don't know if this was a huge issue this week, but scheduling conflicts might become more of a problem in December.
#What will I do next week to fix what didn't work?
#*Next week I will try to make plans with the group on Tuesday. I am assuming everyone is going out of town for Thanksgiving, but perhaps we can make certain dates within the week to have things done by so the rest of the group can look over the work.

====Hayden====
#What Worked?
#*This week we were a little more prepared to work together and complete the deliverables. As time goes by the end goal is becoming more apparent.
#What didn't work?
#*I spent a lot of time reading the article multiple times and it is still a bit confusing to me. A lot of the vocabulary wasn't even found in the dictionaries we provided and a lot of the information needed a lot of background knowledge to comprehend.
#What will I do next week to fix what didn't work?
#*Next week I will try and do the bulk of the assignment earlier in the week rather than on the weekend, so I may have more time to understand the material.[[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 21:09, 20 November 2017 (PST)

===[[Week 14]] Team Progress Reflections===

====Arash====
# What worked?
#* Our design idea turned out pretty nicely, and was not that difficult to implement.
# What didn't work?
#* Coming up with what the final aesthetics will look like.
# What will I do next to fix what didn't work?
#* We will discuss the matter in class with the professors and our fellow students to see what works best for everyone.
{{Pagedesiigner}}

Page Desiigner

2017-12-05T02:19:34Z

ArashLari: /* Executive Summaries */

[[Category:Page Desiigner]]
[[Category:Group Projects]]

==Page Design Team Members==
Coder: [[User:ArashLari|Arash Lari]]
 
Project Manager/ Quality Assurance: [[User:hhinsch|Hayden Hinsch]]
 
Data Analyst: [[User:Mbalducc|Mary Balducci]]
 
Designer: [[User:Nicolekalcic|Nicole Kalcic]]

==Team Schedule==
[[Page Desiigner Schedule]]

==Page Desiigner Uploaded Files==

[[Media:MaryHaydenWeek12JournalClubPresentation.pptx ‎]]
 
[[File:Data Driven Design.zip]]

==Page Layout==
*<code>Below are a series of rough ideas we will use to create the layout of the page</code>
[[File:ACT1screenshot.png|500px]]
*We will be using a layout similar to the ACT1 gene page.
[[File:Sketchofpagedesign.png]]
*This is a rough sketch of what the page will look like.
*The section labeled '''description''' on the sketch will include the following:
**Gene ID from each database
**Description/Function (ensembl)
*Within the dropdown menus we will be incorporating the following:
**DNA Sequence (ensembl)
**Protein Sequence (UniProt)
**Locus tag (NCBI)
**Also Known As (NCBI)
**Consensus Sequence (JASPAR)
**Regulation (SGD)
**Interaction (SGD)
**Similar Proteins (UniProt)
**Gene Ontology (SGD)
**JASPAR
***Matrix ID
***Class Heat shock factors
***Family HSF factors
***Sequence Logo
***Frequency Matrix

==Annotated Bibliography==
===Mary's contribution:===
Pizzaro, Jewett, Nielson, Agosin. (2008) Growth Temperature Exerts Differential Physiological and Transcriptional Responses in Laboratory and Wine Strains of Saccharomyces Cerevisiae. "Applied and Environmental Microbiology", 74, 6358-6368, doi:10.1128/AEM.00602-08
*PubMed Abstract: https://www.ncbi.nlm.nih.gov/pubmed/18723660
*PubMed Central: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2570279/
*HTML format: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2570279/
*PDF format: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2570279/pdf/0602-08.pdf
*Copyright: 2008 American Society for Microbiology. It is open access.
*Publisher: American Society for Microbiology. This is a scientific society.
*Availability: Online and in print
*Did LMU pay a fee for this article: No
*I could not find the data associated with this article.

Homma, Iwahashi, Komatsu. (2003) Yeast Gene Expression During Growth at Low Temperature. ''Cryobiology'', 46, doi: 10.1016/S0011-2240(03)00028-2
*PubMed abstract: https://www.ncbi.nlm.nih.gov/pubmed/12818212
*PubMed Central:
*HTML format: http://www.sciencedirect.com/science/article/pii/S0011224003000282?via%3Dihub
*PDF format: https://ac.els-cdn.com/S0011224003000282/1-s2.0-S0011224003000282-main.pdf?_tid=aa7fa05e-c5d2-11e7-88ae-00000aacb361&acdnat=1510289609_6ec25501ce7af9dcc6b521ef8a024164
*Copyright 2003 Elsevier Science (USA). It is open access.
*Publisher: Elsevier. This is a for-profit publisher
*Availability: Online and in print
*Did LMU pay a fee for this article: No
*I could not find the data associated with this article.

===Hayden's Contribution===

Dahlquist, K. D., Fitzpatrick, B. G., Camacho, E. T., Entzminger, S. D., & Wanner, N. C. (2015). Parameter Estimation for Gene Regulatory Networks from Microarray Data: Cold Shock Response in Saccharomyces cerevisiae. Bulletin of Mathematical Biology, 77(8), 1457–1492. http://doi.org/10.1007/s11538-015-0092-6
* PubMed Abstract: https://www.ncbi.nlm.nih.gov/pubmed/26420504
* PubMed Central: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4636536/
* Publisher Full Text (HTML): https://link.springer.com/article/10.1007/s11538-015-0092-6
* Publisher Full Text (PDF): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4636536/pdf/11538_2015_Article_92.pdf
* Copyright: The Author(s) 2015. Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
* Publisher: Bulletin of Mathematical Biology(scientific society)
* Availability: Online
* Did LMU pay a fee for this article: No
* Data: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4636536/bin/11538_2015_92_MOESM1_ESM.zip

Ando, A., Nakamura, T., Murata, Y., Takagi, H. and Shima, J. (2007), Identification and classification of genes required for tolerance to freeze–thaw stress revealed by genome-wide screening of Saccharomyces cerevisiae deletion strains. FEMS Yeast Research, 7: 244–253. doi:10.1111/j.1567-1364.2006.00162.x
* PubMed Abstract: https://www.ncbi.nlm.nih.gov/pubmed/16989656
* PubMed Central: N/A
* Publisher Full Text (HTML): https://academic.oup.com/femsyr/article/7/2/244/616825?searchresult=1
* Publisher Full Text (PDF): http://onlinelibrary.wiley.com/doi/10.1111/j.1567-1364.2006.00162.x/epdf
* Copyright: Copyright © 2007, Oxford University Press; Open Access after time has elapsed: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. You must ask permission from the publisher to use the article.
* Publisher: Published by Oxford University Press(respected open access organization)
* Availability: Online and in print
* Did LMU pay a fee for this article: No
* Data: I couldn't find any supplementary data other than the data provided in the article.

==Journal Club Presentations==
===Powerpoint Slides and Respective Articles===
[[File:Data Driven Design.zip]]
* Liikkanen, L. (2017). The Data-Driven Design Era in Professional Web Design. Accessed November 13, 2017, from http://delivery.acm.org/10.1145/3130000/3121355/p52-liikkanen.pdf?ip=157.242.223.254&id=3121355&acc=ACTIVE%20SERVICE&key=80B0E63637265656%2E958C566062FD1C18%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=829067610&CFTOKEN=20249523&__acm__=1510614806_1e8b314e23f771f9b139d99596801365
 
[[Media:MaryHaydenWeek12JournalClubPresentation.pptx ‎]]
*Pizzaro, Jewett, Nielson, Agosin. (2008) Growth Temperature Exerts Differential Physiological and Transcriptional Responses in Laboratory and Wine Strains of Saccharomyces Cerevisiae. "Applied and Environmental Microbiology", 74, 6358-6368, doi:10.1128/AEM.00602-08

==Executive Summaries==

===[[Week 11]] Executive Summaries===
====Arash====
This week, I worked on researching the given article and the development terms within it as well as prepare a presentation on the article. I also began thinking of some page layout ideas with Nicole to prepare to implement in the coming weeks. All of this information could be found on my Week 11 page: [[ArashLari_Week_11|ArashLari Week 11]].
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 02:09, 14 November 2017 (PST)
====Mary====
This week, I worked on creating an annotated bibliography. The articles I found were primary sources about experiments done involving cold shock on yeast, and the use of microarrays to show results. I used multiple databases to find these articles, and while doing so I learned what key words got me the results closest to what I wanted. While writing the annotated bibliography, I also learned more about the articles, such as when they were written, who published them, what kind of publisher they are, and other information. All of this is recorded in my [[mbalducc Week 11| Week 11]] page.

[[User:Mbalducc|Mbalducc]] ([[User talk:Mbalducc|talk]]) 23:03, 13 November 2017 (PST)

====Nicole====
This past week, I read the article ''The Data-Driven Design Era in Professional Web Design'' by L.A. Liikkanen. This allowed me to complete my individual journal: an informative outline of the article and list of ten definitions. Arash and I worked together to complete a 15 minute presentation that we are going to give on Tuesday. The presentation gives an overview of our individual journals and what we each learned. You can access more information on what I did this week here: [[Nicolekalcic Week 11]].
[[User:Nicolekalcic|Nicolekalcic]] ([[User talk:Nicolekalcic|talk]]) 01:27, 14 November 2017 (PST)
====Hayden====
This week I created part of the annotated bibliography we will be using for our final project. I conducted searched for "yeast cold shock microarray" articles and found which two articles I was going to use for the bibliography. The majority of the time this week was spent on finding the information needed for the annotated bibliography which can be found below on this page. More detailed information can be found on my [[hhinsch Week 11]] page. This week I became proficient in finding reliable article to use for our final project. This week we also created the group's template and wiki page. [[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 22:43, 13 November 2017 (PST)

===[[Week 12]] Executive Summaries===
====Arash====
This week was rather light compared to the previous week. We mostly set up the files on our personal computers, which was convenient because as a computer science major I already had most of the necessary files. Nicole and I talked about some preliminary designs and set up a rough schedule that we'll make concrete after the break. This can be found on my week 12 page [[ArashLari Week 12] | Arash Lari Week 12]. [[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 20:43, 20 November 2017 (PST)

====Mary====
This week I read an article about an experiment done on wine and lab strains of yeast in different temperatures. The experimenters used DNA microarrays to determine which genes were affected by the change in temperatures and which were different based on the different strains of yeast. While reading the article, I can across words I had never seen before, so I also found and recorded the definitions of these words. I then made an outline of the article, to use as a basis for my presentation with our project manager, [[user:hhinsch| Hayden]]. All of this is on my [[mbalducc Week 12| Week 12]] page.

[[User:Mbalducc|Mbalducc]] ([[User talk:Mbalducc|talk]]) 19:51, 19 November 2017 (PST)

====Nicole====
Because Arash and I presented last week, we didn't have a heavy work load this week. Arash explained a lot of technical things to me, and we completed the first coding milestones and talked about design possibilities. I officially have a GitHub (nicolekal). I also downloaded the software I needed but didn't have onto my laptop. A full update can be found on: [[Nicolekalcic Week 12] | Nicole Kalcic Week 12]. [[User:Nicolekalcic|Nicolekalcic]] ([[User talk:Nicolekalcic|talk]]) 23:45, 20 November 2017 (PST)

====Hayden====
This week I worked with Mary specifically in order to complete our journal club presentation. As a group we worked together to make corrections to the [[Page Desiigner]] group page in order to complete the deliverables for this week. Mary and I created a schedule for our group along with helpful links. As a class we decided we will be using WhatsApp to communicate. I spent most of this week outlining the article (it was a pretty difficult read) and defining terms on my [[hhinsch Week 12]] page. [[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 17:42, 20 November 2017 (PST)

===[[Week 14]] Executive Summaries===
====Arash====
This week I worked on implementing our designs for the web page. Nicole and Hayden helped come up with some design ideas. We ended up deciding that ACT1 was a very well designed page and that it would be best to follow the format of that page. There's only a few things left to do but our portion of the project is nearly complete, the generic version of every gene's page needs just a few finishing touches. [[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 18:19, 4 December 2017 (PST)

====Mary====

====Nicole====

====Hayden====

==Team Progress Reflections==

===[[Week 11]] Team Progress Reflections===

====Nicole====
# What worked?
#* We all work well in our positions. Arash and I worked together recently, so it was easy to coordinate what we are going to do as coder and designer. Everyone was open to opinions on the page and team name. Also, everyone communicated what they were having trouble finishing.
# What didn't work?
#* Like Mary said, we weren't able to meet in person. We planned out our meetup during class, but Friday ended up being a busy day for everyone.
# What will I do next to fix what didn't work?
#* Next week I will make sure to check in more with my partners. Even if we don't meet up, it is important to make sure everyone is caught up on the assignment and their individual parts.
====Mary====
# What worked?
#*I think as a team, we did a good job communicating and figuring out what needed to be done, and who would do each part.
# What didn't work?
#*We did not meet up outside of class. We had planned to, but we all ended up having conflicts so we couldn't meet.
# What will I do next to fix what didn't work?
#*I think this can be fixed just by making sure we all communicate in other ways, which is what we did. Through text messages we were able to talk about the assignment and help each other.
====Hayden====
# What worked?
#*We are all doing well communicating and completing our deliverables as individuals and as a group.
# What didn't work?
#*Everything worked well in regards to the group, but I did have some trouble finding all the information needed to create an annotated bibliography for the articles I used. Google Scholar gave me way too many results, and PubMed did not have a lot of articles that were either free or LMU had paid for.
# What will I do next to fix what didn't work?
#*I will continue to ask questions in class. I also feel more confident about finding useful articles after the completion of this assignment, so it shouldn't be a problem.
[[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 22:44, 13 November 2017 (PST)

====Arash====

# What worked?
#*We were good at maintaining our own individual tasks and keeping each other updated via text message.

# What didn't work?
#*We were unable to schedule time to meet and work effectively.

# What will I do next to fix what didn't work?
#*Hopefully we will have lighter course loads and more free time for the next assignment, but besides that we will begin working sooner and try to work in person more than digitally.

===[[Week 12]] Team Progress Reflections===

====Arash====
# What worked?
#*I think we all got a better grasp of the assignment and time management for the assignment. We have a clear idea where we're headed.
# What didn't work?
#* I wish we had more time to dedicate to this class, as often our schedules don't line up.
# What will I do next to fix what didn't work?
#*We'll try to be more proactive about all our tasks so they don't pile up. I'm planning on starting some work during the thanksgiving break to lighten the load afterwards.
====Mary====
# What worked?
#*I think that this week I felt more comfortable with the project as a whole. I also liked presenting after the other guilds because I got to see their presentations to get a better idea of what we should include in ours.
# What didn't work?
#*I wish that I had more time to read the article and make the presentation. I felt that I didn't understand the article completely, and had to read it a few times to be happy with the way that I understood it and formed my outline of it. Because of this, I couldn't spend as much time as I would've wanted on making the presentation.
# What will I do next to fix what didn't work?
#*I will try to spend more time on the entire project, instead of just one part.

====Nicole====
#What Worked?
#*Arash and I communicated via text and had a better idea of what we needed to do earlier on in the week, which made it easier to progress and do quality, time efficient work.
#What didn't work?
#*We weren't all able to meet in person. I don't know if this was a huge issue this week, but scheduling conflicts might become more of a problem in December.
#What will I do next week to fix what didn't work?
#*Next week I will try to make plans with the group on Tuesday. I am assuming everyone is going out of town for Thanksgiving, but perhaps we can make certain dates within the week to have things done by so the rest of the group can look over the work.

====Hayden====
#What Worked?
#*This week we were a little more prepared to work together and complete the deliverables. As time goes by the end goal is becoming more apparent.
#What didn't work?
#*I spent a lot of time reading the article multiple times and it is still a bit confusing to me. A lot of the vocabulary wasn't even found in the dictionaries we provided and a lot of the information needed a lot of background knowledge to comprehend.
#What will I do next week to fix what didn't work?
#*Next week I will try and do the bulk of the assignment earlier in the week rather than on the weekend, so I may have more time to understand the material.[[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 21:09, 20 November 2017 (PST)

{{Pagedesiigner}}

ArashLari Week 14

2017-12-05T02:15:26Z

ArashLari: /* Acknowledgements and References */

= Electronic Lab Notebook =

* This week we began moving towards the final design for the gene page for GRNSight.
* With some discussion and guidance from professors Dahlquist and Dionisio we came up with enough material for the integration team to not get slowed down
* Nicole drafted a rough design of what our page should look like, based off of the reception of ACT1, the rest of the team agreed with her design
* I implemented the card elements in the same style as the ACT 1 page
** We need to figure out a way to make the links to the gene ID's generic so they can easily be retrieved and updated for every gene
** Would also be ideal to make the formatting of the content in each card clear
* The only thing left to do is finalize design choices and make everything ready for the integration team.

= Acknowledgements and References =
* I worked with Nicole, Hayden, and Mary to discuss what the page will look like and how we should implement everything
* While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.

{{Template:ArashLari}}

ArashLari Week 14

2017-12-05T02:14:55Z

ArashLari: Created page with "= Electronic Lab Notebook = * This week we began moving towards the final design for the gene page for GRNSight. * With some discussion and guidance from professors Dahlquist..."

Page Desiigner

2017-11-21T05:02:14Z

ArashLari: /* Page Desiigner Uploaded Files */

Page Desiigner

2017-11-21T04:48:48Z

ArashLari: /* Week 12 Team Progress Reflections */

Page Desiigner

2017-11-21T04:44:22Z

ArashLari: /* Arash */

Page Desiigner

2017-11-21T04:44:11Z

ArashLari: /* Arash */

Page Desiigner

2017-11-21T04:43:32Z

ArashLari: /* Arash */

Page Desiigner

2017-11-21T04:43:04Z

ArashLari: /* Week 12 Executive Summaries */

ArashLari Week 12

2017-11-21T04:23:42Z

ArashLari: Created page with "==Electronic Notebook== For this week's assignment Nicole Kalcic and I worked on the Coder Milestones 0-4. We set up all the necessary files on our..."

==Electronic Notebook==
For this week's assignment [[User:Nicolekalcic|Nicole Kalcic]] and I worked on the [[Coder]] Milestones 0-4. We set up all the necessary files on our computers as according to milestones 0-3, then Nicole and I communicated briefly over some ideas for the website design. We plan to do some preliminary work over the thanksgiving break. [[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 20:23, 20 November 2017 (PST)

===Milestone 0===
#Our [[Week 11]] journal club presentation. The powerpoint can be found here:
#*[[File: Data Driven Design.zip | Arash and Nicole's presentaton]

===Milestone 1===
#This portion required our individual computers had the correct software to complete the project.
# As a computer science student I already had the following files, but we also installed them on Nicole's computer.
* Node.js 8.4.0
* Code-savvy editor (Atom)
* Web browser with developer tools (Chrome)
* '''git''' version control software
* '''curl''' command

===Milestone 2===
#This portion required us to fork the open source [https://github.com/dondi/GRNsight GRNsight project].
#*This is found in the top right corner of the GRNsight GitHub project page.
#I was added to the branch by Blair, and our fork is called page-design.
#The fork is currently under my GitHub account: arashlari.

===Milestone 3===
#For this part of the assignment, we needed to download the project code from GitHub.
#We followed these instructions: [https://github.com/dondi/GRNsight/wiki/Running-the-Applications instructions in the GRNsight wiki]
# We performed "git clone" to acquire the files, "cd" into the appropriate folder on our personal computers, and "git checkout" to the branch we were assigned to.
# I personally used GitKraken as I am more comfortable with it, it's simply a Git tool that has a GUI which I find enjoyable.

===Milestone 4===
#We looked at what the most liked pages were and why, and began sketching out some ideas as for layouts and color schemes.
#Nicole being the one with the artistic background was focused on the aesthetic elements, while I focused on how I would use bootstrap to implement everything while still relaying the relevant technical information to Nicole.

==Acknowledgments==
#For this weeks assignment, my group didn't meet in person all together as it was not necessary but we all kept each other updated through text messaging.
#Nicole and I discussed design ideas over the phone.
#'''While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.'''
==References==
LMU BioDB 2017. (2017). Week 12. Retrieved November 15, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_12
{{Template:ArashLari}}
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 20:23, 20 November 2017 (PST)

Template:ArashLari

2017-11-21T04:12:27Z

ArashLari:

Template:Pagedesiigner

2017-11-21T04:10:32Z

ArashLari: /* Uploaded Files */

[[Category:Page Desiigner]]
[[Category:Group Projects]]

{| class="wikitable" style="width: 100%; text-align: center"
! colspan="8"|GRNsight Gene Page Project Links
|-
! rowspan="2"|[[GRNsight Gene Page Project|Overview]]
! rowspan="2"| [[GRNsight Gene Page Project Deliverables|Deliverables]]
| rowspan="2"|[https://peerj.com/about/author-instructions/#reference-format Reference Format]
! Guilds
| [[Project Manager]]
| [[Quality Assurance]]
| [[Data Analysis]]
| [[Coder]]
|-
! Teams
| [[Page Desiigner]]
| [[Lights, Camera, InterACTION!]]
| [[White Chicks]]
| [[JASPAR the Friendly Ghost]]
|}

===Page Desiigner Schedule===
*[[Page Desiigner Schedule]]
===Uploaded Files===
[[File:Data Driven Design.zip]]

===Individual Contributer Pages===
[[User:ArashLari|Arash Lari]]
 
[[User:hhinsch|Hayden Hinsch]]
 
[[User:Mbalducc|Mary Balducci]]
 
[[User:Nicolekalcic|Nicole Kalcic]]

Page Desiigner

2017-11-21T04:09:35Z

ArashLari: /* Week 11 Executive Summaries */

Template:ArashLari

2017-11-21T04:08:46Z

ArashLari:

Page Desiigner

2017-11-14T10:09:13Z

ArashLari:

[[Category:Assignment]]
[[Category:Group Projects]]

==Page Design Team Members==
Coder: [[User:ArashLari|Arash Lari]]
 
Project Manager/ Quality Assurance: [[User:hhinsch|Hayden Hinsch]]
 
Data Analyst: [[User:Mbalducc|Mary Balducci]]
 
Designer: [[User:Nicolekalcic|Nicole Kalcic]]

==[[Week 11]] Executive Summaries==
===Arash===
This week, I worked on researching the given article and the development terms within it as well as prepare a presentation on the article. I also began thinking of some page layout ideas with Nicole to prepare to implement in the coming weeks.
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 02:09, 14 November 2017 (PST)
===Mary===
This week, I worked on creating an annotated bibliography. The articles I found were primary sources about experiments done involving cold shock on yeast, and the use of microarrays to show results. I used multiple databases to find these articles, and while doing so I learned what key words got me the results closest to what I wanted. While writing the annotated bibliography, I also learned more about the articles, such as when they were written, who published them, what kind of publisher they are, and other information. All of this is recorded in my [[mbalducc Week 11| Week 11]] page.

[[User:Mbalducc|Mbalducc]] ([[User talk:Mbalducc|talk]]) 23:03, 13 November 2017 (PST)

===Nicole===
This past week, I read the article ''The Data-Driven Design Era in Professional Web Design'' by L.A. Liikkanen. This allowed me to complete my individual journal: an informative outline of the article and list of ten definitions. Arash and I worked together to complete a 15 minute presentation that we are going to give on Tuesday. The presentation gives an overview of our individual journals and what we each learned. You can access more information on what I did this week here: [[Nicolekalcic Week 11]].
[[User:Nicolekalcic|Nicolekalcic]] ([[User talk:Nicolekalcic|talk]]) 01:27, 14 November 2017 (PST)
===Hayden===
This week I created part of the annotated bibliography we will be using for our final project. I conducted searched for "yeast cold shock microarray" articles and found which two articles I was going to use for the bibliography. The majority of the time this week was spent on finding the information needed for the annotated bibliography which can be found below on this page. More detailed information can be found on my [[hhinsch Week 11]] page. This week I became proficient in finding reliable article to use for our final project. This week we also created the group's template and wiki page. [[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 22:43, 13 November 2017 (PST)

==[[Week 11]] Team Progress Reflection==
===Nicole===
# What worked?
#* We all work well in our positions. Arash and I worked together recently, so it was easy to coordinate what we are going to do as coder and designer. Everyone was open to opinions on the page and team name. Also, everyone communicated what they were having trouble finishing.
# What didn't work?
#* Like Mary said, we weren't able to meet in person. We planned out our meetup during class, but Friday ended up being a busy day for everyone.
# What will I do next to fix what didn't work?
#* Next week I will make sure to check in more with my partners. Even if we don't meet up, it is important to make sure everyone is caught up on the assignment and their individual parts.
===Mary===
# What worked?
#*I think as a team, we did a good job communicating and figuring out what needed to be done, and who would do each part.
# What didn't work?
#*We did not meet up outside of class. We had planned to, but we all ended up having conflicts so we couldn't meet.
# What will I do next to fix what didn't work?
#*I think this can be fixed just by making sure we all communicate in other ways, which is what we did. Through text messages we were able to talk about the assignment and help each other.
===Hayden===
# What worked?
*We are all doing well communicating and completing our deliverables as individuals and as a group.
# What didn't work?
*Everything worked well in regards to the group, but I did have some trouble finding all the information needed to create an annotated bibliography for the articles I used. Google Scholar gave me way too many results, and PubMed did not have a lot of articles that were either free or LMU had paid for.
# What will I do next to fix what didn't work?
*I will continue to ask questions in class. I also feel more confident about finding useful articles after the completion of this assignment, so it shouldn't be a problem.
[[User:Hhinsch|Hhinsch]] ([[User talk:Hhinsch|talk]]) 22:44, 13 November 2017 (PST)

===Arash===
# What worked?
We were good at maintaining our own individual tasks and keeping each other updated via text message.
# What didn't work?
We were unable to schedule time to meet and work effectively.
# What will I do next to fix what didn't work?
Hopefully we will have lighter course loads and more free time for the next assignment, but besides that we will begin working sooner and try to work in person more than digitally.
==Annotated Bibliography==
===Mary's contribution:===
Pizzaro, Jewett, Nielson, Agosin. (2008) Growth Temperature Exerts Differential Physiological and Transcriptional Responses in Laboratory and Wine Strains of Saccharomyces Cerevisiae. "Applied and Environmental Microbiology", 74, 6358-6368, doi:10.1128/AEM.00602-08
*PubMed Abstract: https://www.ncbi.nlm.nih.gov/pubmed/18723660
*PubMed Central: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2570279/
*HTML format: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2570279/
*PDF format: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2570279/pdf/0602-08.pdf
*Copyright: 2008 American Society for Microbiology. It is open access.
*The publisher is American Society for Microbiology.
**This is a scientific society.
*This article is available both online and in print.
**LMU has not paid a fee for my access to this article.
*I could not find the data associated with this article.

Homma, Iwahashi, Komatsu. (2003) Yeast Gene Expression During Growth at Low Temperature. ''Cryobiology'', 46, doi: 10.1016/S0011-2240(03)00028-2
*PubMed abstract: https://www.ncbi.nlm.nih.gov/pubmed/12818212
*PubMed Central:
*HTML format: http://www.sciencedirect.com/science/article/pii/S0011224003000282?via%3Dihub
*PDF format: https://ac.els-cdn.com/S0011224003000282/1-s2.0-S0011224003000282-main.pdf?_tid=aa7fa05e-c5d2-11e7-88ae-00000aacb361&acdnat=1510289609_6ec25501ce7af9dcc6b521ef8a024164
*Copyright 2003 Elsevier Science (USA). It is open access.
*The publisher is Elsevier.
**This is a for-profit publisher
*This article is available both online and in print.
**LMU has not paid a fee for my access to this article.
*I could not find the data associated with this article.

===Hayden's Contribution===

Dahlquist, K. D., Fitzpatrick, B. G., Camacho, E. T., Entzminger, S. D., & Wanner, N. C. (2015). Parameter Estimation for Gene Regulatory Networks from Microarray Data: Cold Shock Response in Saccharomyces cerevisiae. Bulletin of Mathematical Biology, 77(8), 1457–1492. http://doi.org/10.1007/s11538-015-0092-6
* PubMed Abstract: https://www.ncbi.nlm.nih.gov/pubmed/26420504
* PubMed Central: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4636536/
* Publisher Full Text (HTML): https://link.springer.com/article/10.1007/s11538-015-0092-6
* Publisher Full Text (PDF): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4636536/pdf/11538_2015_Article_92.pdf
* Copyright: The Author(s) 2015. Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
* Publisher: Bulletin of Mathematical Biology(scientific society)
* Availability: Online
* Did LMU pay a fee for this article: No
* Data: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4636536/bin/11538_2015_92_MOESM1_ESM.zip

Ando, A., Nakamura, T., Murata, Y., Takagi, H. and Shima, J. (2007), Identification and classification of genes required for tolerance to freeze–thaw stress revealed by genome-wide screening of Saccharomyces cerevisiae deletion strains. FEMS Yeast Research, 7: 244–253. doi:10.1111/j.1567-1364.2006.00162.x
* PubMed Abstract: https://www.ncbi.nlm.nih.gov/pubmed/16989656
* PubMed Central: N/A
* Publisher Full Text (HTML): https://academic.oup.com/femsyr/article/7/2/244/616825?searchresult=1
* Publisher Full Text (PDF): http://onlinelibrary.wiley.com/doi/10.1111/j.1567-1364.2006.00162.x/epdf
* Copyright: Copyright © 2007, Oxford University Press; Open Access after time has elapsed: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. You must ask permission from the publisher to use the article.
* Publisher: Published by Oxford University Press(respected open access organization)
* Availability: Online and in print
* Did LMU pay a fee for this article: No
* Data: I couldn't find any supplementary data other than the data provided in the article.
{{Pagedesiigner}}

ArashLari Week 11

2017-11-14T10:03:59Z

ArashLari: /* Outline and Presentation */

==Development Terms==
*Read: ''The Data-Driven Design Era in Professional Web Design''
*By: Lassi A. Liikkanen
This article introduced me to many terms I hadn't heard in respect to design before, but the author did a good job of explaining them so I didn't need to consult outside sources very much. I had heard of most of the analytics products before but there were a few I wasn't familiar with. The following terms are defined according to my understanding after reading their definitions.

*Generative Design
**A development process that is akin to evolution. The process is to set out design goals and an initial schema, and then go through several permutations of variation until arriving at the best option. (Flake, 1998)
*Heat Maps
**Heat maps in regards to design are basically the same as heat maps in terms of population movement. It is a visual representation of where the average users cursor hovers and clicks and for what amount of time. (Liikkanen, 2017)
*User Screen Recordings
**A user screen recording is a literal video of the individual user's screen during their session on the application. (Liikkanen, 2017)
*A/B Tests
** A controlled test with two variants. This is used to compare different potential design layouts and choose the most effective elements from them. (Kohavi, Longbotham 2015)
*Behavioural Analytics
**The contextual information around a users session on an application, including information such as the usage log, click-stream data, average time spent on each page, etc. (Liikkanen, 2017)
*Assistive Tools
** Tools that make tasks easier for humans but still require a human interaction. (Liikkanen, 2017)
*Agentive Tools
** Tools that do tasks for humans and eliminate the need for the humans to do anything related to the task. (Liikkanen, 2017)
*Net Promoter Score
** A measure from -100 to 100 of a customer's willingness to refer a product or company to a peer. (NetPromoter, 2017)
*KISSmetrics
** A behavioural analytics service that provides automated tools for interpreting data from customers. (Kissmetrics, 2017).
*IBM TeaLeaf
**A company that IBM acquired that is focused on bettering customer experience through behavioural analytics. (IBM, 2017).

==Outline and Presentation==
[[File:Data Driven Design.zip]]
===What is Data Driven Design?===
*Traditionally, products were designed by the designers intuition and opinion
**This is because collecting information about user activity has been difficult until recently
*The rise of technology and internet based services has led to evidenced-based design
**Companies and developers want to know that their intended users will be able to easily use their service before it affects sales
*Designing based off of results empirical user evidence is called data driven design
**There are assistive and agentive generative tools to help creative tasks
***A generative design process basically means designing and redesigning based on how the previous iteration was received
**It’s basically an assortment of tools at the disposal of the designers and the developers that they can use in a multitude of ways to gauge how effective their design is.
**The basic idea of this design philosophy is to collect data, combine it with other data, and analyze it for implementation in the final design
=== Data Driven Design of Web Services ===
*Web interaction design is a field that utilizes data design driven solutions frequently
**Major internet companies often build their own experiment-management platforms so they can run many different experiments in parallel with automation
*Data-collection can be separated into two types of solutions: Active and Passive
**Active data collection is reliant on user input, and as such it’s considered qualitative
***It doesn’t really fit the mold of DDD very well if the scale is large
***Surveys are often plagued with many issues such as reluctance to participate and low quality answers that don’t provide very much insight
***In smaller cases it is very useful, such as using an online focus group to get detailed reactions and responses
****This is costly, which is why large corporations don’t often do this
**Passive data collection, or passive tracking is any kind of recording of user activity
***Screen recordings, heat maps, experiment management, and descriptive behavioral analytics are some passive tracking tools
****Screen recordings are full recordings of the users screen for later analysis.
****Provides a very detailed view and a lot of useful implementation
****However, raises privacy and ethical concerns and can’t really be automated to gain insight
**Heat maps are a way to visualize the most frequently clicked on and hovered on spots of a page
***It isn’t always very clear because some pages might be context specific, but it still can provide enough information to help optimize the design of the page
**Experiment management is basically having 2 or more types of designs be tested on to find which features and design choices should be kept or removed
***It allows developers to find fail new ideas quickly so they don’t have to waste time with subpar designs
***They can be automated but their shortcoming is the numbers aren’t always that insightful
**Behavioral analytics are the precursor to all current DDD solutions
***It’s all the contextual data related to the visit, such as the amount of time spent and on what pages, what elements were clicked and in what order, etc.
***This information is more useful for business and marketing decisions rather than design decisions.
****Many companies offer analytics solutions for developers
*****Google is the biggest by far, but there are others such as Adobe Analytics, KISSMetrics, and IBM Tealeaf
=== Conclusion ===
*Data driven design provides designers a strong incentive to adopt a hypothesis-driven approach to design research
**Having a clear hypothesis is important as it will affect the way one interprets data and identify which results are the most significant
**The age of Data Driven Design draws heavily upon designer moral, integrity, and candor.
***Ethics of how data is collected and used is very important, and consent is key in ethical data acquisition.
*The future of data driven design will be with tools that couple seamlessly with design processes, be adaptive to change, and show maturity in addressing ethical questions.
**Designers will embrace more quantitative data as tools that allow for scalability become available
**Design will introduce assistive and agentive tools that do things with us and for us
*What will we use for our project?
**We won’t use any of the passive tools directly
***Although useful, we do not have the means to get enough different users to get fruitful information using these methods
***Although we might implement experiment-management within our own small group and see what we collectively thinks works best
**We will be using active data collection, and ask our fellow classmates to browse our site and give us feedback as they use it
***This level of interactivity with the end-user provide useful insight

==Electronic Notebook==
Our group met thursday after class to schedule a time to meet over the weekend. Unfortunately we didn't meet in person over the weekend due to scheduling conflicts. We updated each other over the phone and Nicole and I went over what the article and presentation where about over the weekend and worked on it digitally on monday night. I also kept in contact with Hayden and he and Mary filled me in on their part and helped Nicole and I out for the presentation.

== Acknowledgements ==
*I worked with my homework partners Hayden, Mary, and Nicole. We mostly communicated via phone call or text message.

While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 01:21, 14 November 2017 (PST)

== References ==
* Liikkanen, L. (2017). The Data-Driven Design Era in Professional Web Design. Accessed November 13, 2017, from http://delivery.acm.org/10.1145/3130000/3121355/p52-liikkanen.pdf?ip=157.242.223.254&id=3121355&acc=ACTIVE%20SERVICE&key=80B0E63637265656%2E958C566062FD1C18%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=829067610&CFTOKEN=20249523&__acm__=1510614806_1e8b314e23f771f9b139d99596801365
* LMU BioDB 2017. (2017). Week 11. Retrieved November 13, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_11
* Kohavi, Ron; Longbotham, Roger (2015). "Online Controlled Experiments and A/B Tests". Accessed November 13, 2017, from http://www.exp-platform.com/Documents/2015%20Online%20Controlled%20Experiments_EncyclopediaOfMLDM.pdf
*Gary William Flake: The Computational Beauty of Nature: Computer Explorations of Fractals, Chaos, Complex Systems, and Adaptation. MIT Press 1998, ISBN 978-0-262-56127-3
*IBM (2017), IBM TeaLeaf, Accessed on November 13, 2017 from https://www.ibm.com/customer-engagement/tealeaf
*Net Promoter (2017), Net Promoter, accessed on November 13, 2017 from https://www.netpromoter.com/know/

==Links==
{{Template:ArashLari}}

File:Data Driven Design.zip

2017-11-14T10:01:49Z

ArashLari: Arash and Nicole's presentation for Page Desiigner

Arash and Nicole's presentation for Page Desiigner

ArashLari Week 11

2017-11-14T10:00:30Z

ArashLari: /* Electronic Notebook */

==Development Terms==
*Read: ''The Data-Driven Design Era in Professional Web Design''
*By: Lassi A. Liikkanen
This article introduced me to many terms I hadn't heard in respect to design before, but the author did a good job of explaining them so I didn't need to consult outside sources very much. I had heard of most of the analytics products before but there were a few I wasn't familiar with. The following terms are defined according to my understanding after reading their definitions.

*Generative Design
**A development process that is akin to evolution. The process is to set out design goals and an initial schema, and then go through several permutations of variation until arriving at the best option. (Flake, 1998)
*Heat Maps
**Heat maps in regards to design are basically the same as heat maps in terms of population movement. It is a visual representation of where the average users cursor hovers and clicks and for what amount of time. (Liikkanen, 2017)
*User Screen Recordings
**A user screen recording is a literal video of the individual user's screen during their session on the application. (Liikkanen, 2017)
*A/B Tests
** A controlled test with two variants. This is used to compare different potential design layouts and choose the most effective elements from them. (Kohavi, Longbotham 2015)
*Behavioural Analytics
**The contextual information around a users session on an application, including information such as the usage log, click-stream data, average time spent on each page, etc. (Liikkanen, 2017)
*Assistive Tools
** Tools that make tasks easier for humans but still require a human interaction. (Liikkanen, 2017)
*Agentive Tools
** Tools that do tasks for humans and eliminate the need for the humans to do anything related to the task. (Liikkanen, 2017)
*Net Promoter Score
** A measure from -100 to 100 of a customer's willingness to refer a product or company to a peer. (NetPromoter, 2017)
*KISSmetrics
** A behavioural analytics service that provides automated tools for interpreting data from customers. (Kissmetrics, 2017).
*IBM TeaLeaf
**A company that IBM acquired that is focused on bettering customer experience through behavioural analytics. (IBM, 2017).

==Outline and Presentation==
===What is Data Driven Design?===
*Traditionally, products were designed by the designers intuition and opinion
**This is because collecting information about user activity has been difficult until recently
*The rise of technology and internet based services has led to evidenced-based design
**Companies and developers want to know that their intended users will be able to easily use their service before it affects sales
*Designing based off of results empirical user evidence is called data driven design
**There are assistive and agentive generative tools to help creative tasks
***A generative design process basically means designing and redesigning based on how the previous iteration was received
**It’s basically an assortment of tools at the disposal of the designers and the developers that they can use in a multitude of ways to gauge how effective their design is.
**The basic idea of this design philosophy is to collect data, combine it with other data, and analyze it for implementation in the final design
=== Data Driven Design of Web Services ===
*Web interaction design is a field that utilizes data design driven solutions frequently
**Major internet companies often build their own experiment-management platforms so they can run many different experiments in parallel with automation
*Data-collection can be separated into two types of solutions: Active and Passive
**Active data collection is reliant on user input, and as such it’s considered qualitative
***It doesn’t really fit the mold of DDD very well if the scale is large
***Surveys are often plagued with many issues such as reluctance to participate and low quality answers that don’t provide very much insight
***In smaller cases it is very useful, such as using an online focus group to get detailed reactions and responses
****This is costly, which is why large corporations don’t often do this
**Passive data collection, or passive tracking is any kind of recording of user activity
***Screen recordings, heat maps, experiment management, and descriptive behavioral analytics are some passive tracking tools
****Screen recordings are full recordings of the users screen for later analysis.
****Provides a very detailed view and a lot of useful implementation
****However, raises privacy and ethical concerns and can’t really be automated to gain insight
**Heat maps are a way to visualize the most frequently clicked on and hovered on spots of a page
***It isn’t always very clear because some pages might be context specific, but it still can provide enough information to help optimize the design of the page
**Experiment management is basically having 2 or more types of designs be tested on to find which features and design choices should be kept or removed
***It allows developers to find fail new ideas quickly so they don’t have to waste time with subpar designs
***They can be automated but their shortcoming is the numbers aren’t always that insightful
**Behavioral analytics are the precursor to all current DDD solutions
***It’s all the contextual data related to the visit, such as the amount of time spent and on what pages, what elements were clicked and in what order, etc.
***This information is more useful for business and marketing decisions rather than design decisions.
****Many companies offer analytics solutions for developers
*****Google is the biggest by far, but there are others such as Adobe Analytics, KISSMetrics, and IBM Tealeaf
=== Conclusion ===
*Data driven design provides designers a strong incentive to adopt a hypothesis-driven approach to design research
**Having a clear hypothesis is important as it will affect the way one interprets data and identify which results are the most significant
**The age of Data Driven Design draws heavily upon designer moral, integrity, and candor.
***Ethics of how data is collected and used is very important, and consent is key in ethical data acquisition.
*The future of data driven design will be with tools that couple seamlessly with design processes, be adaptive to change, and show maturity in addressing ethical questions.
**Designers will embrace more quantitative data as tools that allow for scalability become available
**Design will introduce assistive and agentive tools that do things with us and for us
*What will we use for our project?
**We won’t use any of the passive tools directly
***Although useful, we do not have the means to get enough different users to get fruitful information using these methods
***Although we might implement experiment-management within our own small group and see what we collectively thinks works best
**We will be using active data collection, and ask our fellow classmates to browse our site and give us feedback as they use it
***This level of interactivity with the end-user provide useful insight

==Electronic Notebook==
Our group met thursday after class to schedule a time to meet over the weekend. Unfortunately we didn't meet in person over the weekend due to scheduling conflicts. We updated each other over the phone and Nicole and I went over what the article and presentation where about over the weekend and worked on it digitally on monday night. I also kept in contact with Hayden and he and Mary filled me in on their part and helped Nicole and I out for the presentation.

== Acknowledgements ==
*I worked with my homework partners Hayden, Mary, and Nicole. We mostly communicated via phone call or text message.

While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 01:21, 14 November 2017 (PST)

== References ==
* Liikkanen, L. (2017). The Data-Driven Design Era in Professional Web Design. Accessed November 13, 2017, from http://delivery.acm.org/10.1145/3130000/3121355/p52-liikkanen.pdf?ip=157.242.223.254&id=3121355&acc=ACTIVE%20SERVICE&key=80B0E63637265656%2E958C566062FD1C18%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=829067610&CFTOKEN=20249523&__acm__=1510614806_1e8b314e23f771f9b139d99596801365
* LMU BioDB 2017. (2017). Week 11. Retrieved November 13, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_11
* Kohavi, Ron; Longbotham, Roger (2015). "Online Controlled Experiments and A/B Tests". Accessed November 13, 2017, from http://www.exp-platform.com/Documents/2015%20Online%20Controlled%20Experiments_EncyclopediaOfMLDM.pdf
*Gary William Flake: The Computational Beauty of Nature: Computer Explorations of Fractals, Chaos, Complex Systems, and Adaptation. MIT Press 1998, ISBN 978-0-262-56127-3
*IBM (2017), IBM TeaLeaf, Accessed on November 13, 2017 from https://www.ibm.com/customer-engagement/tealeaf
*Net Promoter (2017), Net Promoter, accessed on November 13, 2017 from https://www.netpromoter.com/know/

==Links==
{{Template:ArashLari}}

ArashLari Week 11

2017-11-14T09:57:31Z

ArashLari: /* References */

==Development Terms==
*Read: ''The Data-Driven Design Era in Professional Web Design''
*By: Lassi A. Liikkanen
This article introduced me to many terms I hadn't heard in respect to design before, but the author did a good job of explaining them so I didn't need to consult outside sources very much. I had heard of most of the analytics products before but there were a few I wasn't familiar with. The following terms are defined according to my understanding after reading their definitions.

*Generative Design
**A development process that is akin to evolution. The process is to set out design goals and an initial schema, and then go through several permutations of variation until arriving at the best option. (Flake, 1998)
*Heat Maps
**Heat maps in regards to design are basically the same as heat maps in terms of population movement. It is a visual representation of where the average users cursor hovers and clicks and for what amount of time. (Liikkanen, 2017)
*User Screen Recordings
**A user screen recording is a literal video of the individual user's screen during their session on the application. (Liikkanen, 2017)
*A/B Tests
** A controlled test with two variants. This is used to compare different potential design layouts and choose the most effective elements from them. (Kohavi, Longbotham 2015)
*Behavioural Analytics
**The contextual information around a users session on an application, including information such as the usage log, click-stream data, average time spent on each page, etc. (Liikkanen, 2017)
*Assistive Tools
** Tools that make tasks easier for humans but still require a human interaction. (Liikkanen, 2017)
*Agentive Tools
** Tools that do tasks for humans and eliminate the need for the humans to do anything related to the task. (Liikkanen, 2017)
*Net Promoter Score
** A measure from -100 to 100 of a customer's willingness to refer a product or company to a peer. (NetPromoter, 2017)
*KISSmetrics
** A behavioural analytics service that provides automated tools for interpreting data from customers. (Kissmetrics, 2017).
*IBM TeaLeaf
**A company that IBM acquired that is focused on bettering customer experience through behavioural analytics. (IBM, 2017).

==Outline and Presentation==
===What is Data Driven Design?===
*Traditionally, products were designed by the designers intuition and opinion
**This is because collecting information about user activity has been difficult until recently
*The rise of technology and internet based services has led to evidenced-based design
**Companies and developers want to know that their intended users will be able to easily use their service before it affects sales
*Designing based off of results empirical user evidence is called data driven design
**There are assistive and agentive generative tools to help creative tasks
***A generative design process basically means designing and redesigning based on how the previous iteration was received
**It’s basically an assortment of tools at the disposal of the designers and the developers that they can use in a multitude of ways to gauge how effective their design is.
**The basic idea of this design philosophy is to collect data, combine it with other data, and analyze it for implementation in the final design
=== Data Driven Design of Web Services ===
*Web interaction design is a field that utilizes data design driven solutions frequently
**Major internet companies often build their own experiment-management platforms so they can run many different experiments in parallel with automation
*Data-collection can be separated into two types of solutions: Active and Passive
**Active data collection is reliant on user input, and as such it’s considered qualitative
***It doesn’t really fit the mold of DDD very well if the scale is large
***Surveys are often plagued with many issues such as reluctance to participate and low quality answers that don’t provide very much insight
***In smaller cases it is very useful, such as using an online focus group to get detailed reactions and responses
****This is costly, which is why large corporations don’t often do this
**Passive data collection, or passive tracking is any kind of recording of user activity
***Screen recordings, heat maps, experiment management, and descriptive behavioral analytics are some passive tracking tools
****Screen recordings are full recordings of the users screen for later analysis.
****Provides a very detailed view and a lot of useful implementation
****However, raises privacy and ethical concerns and can’t really be automated to gain insight
**Heat maps are a way to visualize the most frequently clicked on and hovered on spots of a page
***It isn’t always very clear because some pages might be context specific, but it still can provide enough information to help optimize the design of the page
**Experiment management is basically having 2 or more types of designs be tested on to find which features and design choices should be kept or removed
***It allows developers to find fail new ideas quickly so they don’t have to waste time with subpar designs
***They can be automated but their shortcoming is the numbers aren’t always that insightful
**Behavioral analytics are the precursor to all current DDD solutions
***It’s all the contextual data related to the visit, such as the amount of time spent and on what pages, what elements were clicked and in what order, etc.
***This information is more useful for business and marketing decisions rather than design decisions.
****Many companies offer analytics solutions for developers
*****Google is the biggest by far, but there are others such as Adobe Analytics, KISSMetrics, and IBM Tealeaf
=== Conclusion ===
*Data driven design provides designers a strong incentive to adopt a hypothesis-driven approach to design research
**Having a clear hypothesis is important as it will affect the way one interprets data and identify which results are the most significant
**The age of Data Driven Design draws heavily upon designer moral, integrity, and candor.
***Ethics of how data is collected and used is very important, and consent is key in ethical data acquisition.
*The future of data driven design will be with tools that couple seamlessly with design processes, be adaptive to change, and show maturity in addressing ethical questions.
**Designers will embrace more quantitative data as tools that allow for scalability become available
**Design will introduce assistive and agentive tools that do things with us and for us
*What will we use for our project?
**We won’t use any of the passive tools directly
***Although useful, we do not have the means to get enough different users to get fruitful information using these methods
***Although we might implement experiment-management within our own small group and see what we collectively thinks works best
**We will be using active data collection, and ask our fellow classmates to browse our site and give us feedback as they use it
***This level of interactivity with the end-user provide useful insight

==Electronic Notebook==

== Acknowledgements ==
*I worked with my homework partners Hayden, Mary, and Nicole. We mostly communicated via phone call or text message.

While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 01:21, 14 November 2017 (PST)

== References ==
* Liikkanen, L. (2017). The Data-Driven Design Era in Professional Web Design. Accessed November 13, 2017, from http://delivery.acm.org/10.1145/3130000/3121355/p52-liikkanen.pdf?ip=157.242.223.254&id=3121355&acc=ACTIVE%20SERVICE&key=80B0E63637265656%2E958C566062FD1C18%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=829067610&CFTOKEN=20249523&__acm__=1510614806_1e8b314e23f771f9b139d99596801365
* LMU BioDB 2017. (2017). Week 11. Retrieved November 13, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_11
* Kohavi, Ron; Longbotham, Roger (2015). "Online Controlled Experiments and A/B Tests". Accessed November 13, 2017, from http://www.exp-platform.com/Documents/2015%20Online%20Controlled%20Experiments_EncyclopediaOfMLDM.pdf
*Gary William Flake: The Computational Beauty of Nature: Computer Explorations of Fractals, Chaos, Complex Systems, and Adaptation. MIT Press 1998, ISBN 978-0-262-56127-3
*IBM (2017), IBM TeaLeaf, Accessed on November 13, 2017 from https://www.ibm.com/customer-engagement/tealeaf
*Net Promoter (2017), Net Promoter, accessed on November 13, 2017 from https://www.netpromoter.com/know/

==Links==
{{Template:ArashLari}}

ArashLari Week 11

2017-11-14T09:53:45Z

ArashLari: /* Development Terms */

==Development Terms==
*Read: ''The Data-Driven Design Era in Professional Web Design''
*By: Lassi A. Liikkanen
This article introduced me to many terms I hadn't heard in respect to design before, but the author did a good job of explaining them so I didn't need to consult outside sources very much. I had heard of most of the analytics products before but there were a few I wasn't familiar with. The following terms are defined according to my understanding after reading their definitions.

*Generative Design
**A development process that is akin to evolution. The process is to set out design goals and an initial schema, and then go through several permutations of variation until arriving at the best option. (Flake, 1998)
*Heat Maps
**Heat maps in regards to design are basically the same as heat maps in terms of population movement. It is a visual representation of where the average users cursor hovers and clicks and for what amount of time. (Liikkanen, 2017)
*User Screen Recordings
**A user screen recording is a literal video of the individual user's screen during their session on the application. (Liikkanen, 2017)
*A/B Tests
** A controlled test with two variants. This is used to compare different potential design layouts and choose the most effective elements from them. (Kohavi, Longbotham 2015)
*Behavioural Analytics
**The contextual information around a users session on an application, including information such as the usage log, click-stream data, average time spent on each page, etc. (Liikkanen, 2017)
*Assistive Tools
** Tools that make tasks easier for humans but still require a human interaction. (Liikkanen, 2017)
*Agentive Tools
** Tools that do tasks for humans and eliminate the need for the humans to do anything related to the task. (Liikkanen, 2017)
*Net Promoter Score
** A measure from -100 to 100 of a customer's willingness to refer a product or company to a peer. (NetPromoter, 2017)
*KISSmetrics
** A behavioural analytics service that provides automated tools for interpreting data from customers. (Kissmetrics, 2017).
*IBM TeaLeaf
**A company that IBM acquired that is focused on bettering customer experience through behavioural analytics. (IBM, 2017).

==Outline and Presentation==
===What is Data Driven Design?===
*Traditionally, products were designed by the designers intuition and opinion
**This is because collecting information about user activity has been difficult until recently
*The rise of technology and internet based services has led to evidenced-based design
**Companies and developers want to know that their intended users will be able to easily use their service before it affects sales
*Designing based off of results empirical user evidence is called data driven design
**There are assistive and agentive generative tools to help creative tasks
***A generative design process basically means designing and redesigning based on how the previous iteration was received
**It’s basically an assortment of tools at the disposal of the designers and the developers that they can use in a multitude of ways to gauge how effective their design is.
**The basic idea of this design philosophy is to collect data, combine it with other data, and analyze it for implementation in the final design
=== Data Driven Design of Web Services ===
*Web interaction design is a field that utilizes data design driven solutions frequently
**Major internet companies often build their own experiment-management platforms so they can run many different experiments in parallel with automation
*Data-collection can be separated into two types of solutions: Active and Passive
**Active data collection is reliant on user input, and as such it’s considered qualitative
***It doesn’t really fit the mold of DDD very well if the scale is large
***Surveys are often plagued with many issues such as reluctance to participate and low quality answers that don’t provide very much insight
***In smaller cases it is very useful, such as using an online focus group to get detailed reactions and responses
****This is costly, which is why large corporations don’t often do this
**Passive data collection, or passive tracking is any kind of recording of user activity
***Screen recordings, heat maps, experiment management, and descriptive behavioral analytics are some passive tracking tools
****Screen recordings are full recordings of the users screen for later analysis.
****Provides a very detailed view and a lot of useful implementation
****However, raises privacy and ethical concerns and can’t really be automated to gain insight
**Heat maps are a way to visualize the most frequently clicked on and hovered on spots of a page
***It isn’t always very clear because some pages might be context specific, but it still can provide enough information to help optimize the design of the page
**Experiment management is basically having 2 or more types of designs be tested on to find which features and design choices should be kept or removed
***It allows developers to find fail new ideas quickly so they don’t have to waste time with subpar designs
***They can be automated but their shortcoming is the numbers aren’t always that insightful
**Behavioral analytics are the precursor to all current DDD solutions
***It’s all the contextual data related to the visit, such as the amount of time spent and on what pages, what elements were clicked and in what order, etc.
***This information is more useful for business and marketing decisions rather than design decisions.
****Many companies offer analytics solutions for developers
*****Google is the biggest by far, but there are others such as Adobe Analytics, KISSMetrics, and IBM Tealeaf
=== Conclusion ===
*Data driven design provides designers a strong incentive to adopt a hypothesis-driven approach to design research
**Having a clear hypothesis is important as it will affect the way one interprets data and identify which results are the most significant
**The age of Data Driven Design draws heavily upon designer moral, integrity, and candor.
***Ethics of how data is collected and used is very important, and consent is key in ethical data acquisition.
*The future of data driven design will be with tools that couple seamlessly with design processes, be adaptive to change, and show maturity in addressing ethical questions.
**Designers will embrace more quantitative data as tools that allow for scalability become available
**Design will introduce assistive and agentive tools that do things with us and for us
*What will we use for our project?
**We won’t use any of the passive tools directly
***Although useful, we do not have the means to get enough different users to get fruitful information using these methods
***Although we might implement experiment-management within our own small group and see what we collectively thinks works best
**We will be using active data collection, and ask our fellow classmates to browse our site and give us feedback as they use it
***This level of interactivity with the end-user provide useful insight

==Electronic Notebook==

== Acknowledgements ==
*I worked with my homework partners Hayden, Mary, and Nicole. We mostly communicated via phone call or text message.

While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 01:21, 14 November 2017 (PST)

== References ==
* Liikkanen, L. (2017). The Data-Driven Design Era in Professional Web Design. Accessed November 13, 2017, from http://delivery.acm.org/10.1145/3130000/3121355/p52-liikkanen.pdf?ip=157.242.223.254&id=3121355&acc=ACTIVE%20SERVICE&key=80B0E63637265656%2E958C566062FD1C18%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=829067610&CFTOKEN=20249523&__acm__=1510614806_1e8b314e23f771f9b139d99596801365
* LMU BioDB 2017. (2017). Week 11. Retrieved November 13, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_11

==Links==
{{Template:ArashLari}}

ArashLari Week 11

2017-11-14T09:29:11Z

ArashLari: /* Outline and Presentation */

==Development Terms==
*Read: ''The Data-Driven Design Era in Professional Web Design''
*By: Lassi A. Liikkanen

==Outline and Presentation==
===What is Data Driven Design?===
*Traditionally, products were designed by the designers intuition and opinion
**This is because collecting information about user activity has been difficult until recently
*The rise of technology and internet based services has led to evidenced-based design
**Companies and developers want to know that their intended users will be able to easily use their service before it affects sales
*Designing based off of results empirical user evidence is called data driven design
**There are assistive and agentive generative tools to help creative tasks
***A generative design process basically means designing and redesigning based on how the previous iteration was received
**It’s basically an assortment of tools at the disposal of the designers and the developers that they can use in a multitude of ways to gauge how effective their design is.
**The basic idea of this design philosophy is to collect data, combine it with other data, and analyze it for implementation in the final design
=== Data Driven Design of Web Services ===
*Web interaction design is a field that utilizes data design driven solutions frequently
**Major internet companies often build their own experiment-management platforms so they can run many different experiments in parallel with automation
*Data-collection can be separated into two types of solutions: Active and Passive
**Active data collection is reliant on user input, and as such it’s considered qualitative
***It doesn’t really fit the mold of DDD very well if the scale is large
***Surveys are often plagued with many issues such as reluctance to participate and low quality answers that don’t provide very much insight
***In smaller cases it is very useful, such as using an online focus group to get detailed reactions and responses
****This is costly, which is why large corporations don’t often do this
**Passive data collection, or passive tracking is any kind of recording of user activity
***Screen recordings, heat maps, experiment management, and descriptive behavioral analytics are some passive tracking tools
****Screen recordings are full recordings of the users screen for later analysis.
****Provides a very detailed view and a lot of useful implementation
****However, raises privacy and ethical concerns and can’t really be automated to gain insight
**Heat maps are a way to visualize the most frequently clicked on and hovered on spots of a page
***It isn’t always very clear because some pages might be context specific, but it still can provide enough information to help optimize the design of the page
**Experiment management is basically having 2 or more types of designs be tested on to find which features and design choices should be kept or removed
***It allows developers to find fail new ideas quickly so they don’t have to waste time with subpar designs
***They can be automated but their shortcoming is the numbers aren’t always that insightful
**Behavioral analytics are the precursor to all current DDD solutions
***It’s all the contextual data related to the visit, such as the amount of time spent and on what pages, what elements were clicked and in what order, etc.
***This information is more useful for business and marketing decisions rather than design decisions.
****Many companies offer analytics solutions for developers
*****Google is the biggest by far, but there are others such as Adobe Analytics, KISSMetrics, and IBM Tealeaf
=== Conclusion ===
*Data driven design provides designers a strong incentive to adopt a hypothesis-driven approach to design research
**Having a clear hypothesis is important as it will affect the way one interprets data and identify which results are the most significant
**The age of Data Driven Design draws heavily upon designer moral, integrity, and candor.
***Ethics of how data is collected and used is very important, and consent is key in ethical data acquisition.
*The future of data driven design will be with tools that couple seamlessly with design processes, be adaptive to change, and show maturity in addressing ethical questions.
**Designers will embrace more quantitative data as tools that allow for scalability become available
**Design will introduce assistive and agentive tools that do things with us and for us
*What will we use for our project?
**We won’t use any of the passive tools directly
***Although useful, we do not have the means to get enough different users to get fruitful information using these methods
***Although we might implement experiment-management within our own small group and see what we collectively thinks works best
**We will be using active data collection, and ask our fellow classmates to browse our site and give us feedback as they use it
***This level of interactivity with the end-user provide useful insight

==Electronic Notebook==

== Acknowledgements ==
*I worked with my homework partners Hayden, Mary, and Nicole. We mostly communicated via phone call or text message.

While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 01:21, 14 November 2017 (PST)

== References ==
* Liikkanen, L. (2017). The Data-Driven Design Era in Professional Web Design. Accessed November 13, 2017, from http://delivery.acm.org/10.1145/3130000/3121355/p52-liikkanen.pdf?ip=157.242.223.254&id=3121355&acc=ACTIVE%20SERVICE&key=80B0E63637265656%2E958C566062FD1C18%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=829067610&CFTOKEN=20249523&__acm__=1510614806_1e8b314e23f771f9b139d99596801365
* LMU BioDB 2017. (2017). Week 11. Retrieved November 13, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_11

==Links==
{{Template:ArashLari}}

ArashLari Week 11

2017-11-14T09:21:52Z

ArashLari: preparing page

==Development Terms==
*Read: ''The Data-Driven Design Era in Professional Web Design''
*By: Lassi A. Liikkanen

==Outline and Presentation==

==Electronic Notebook==

== Acknowledgements ==
*I worked with my homework partners Hayden, Mary, and Nicole. We mostly communicated via phone call or text message.

While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 01:21, 14 November 2017 (PST)

== References ==
* Liikkanen, L. (2017). The Data-Driven Design Era in Professional Web Design. Accessed November 13, 2017, from http://delivery.acm.org/10.1145/3130000/3121355/p52-liikkanen.pdf?ip=157.242.223.254&id=3121355&acc=ACTIVE%20SERVICE&key=80B0E63637265656%2E958C566062FD1C18%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=829067610&CFTOKEN=20249523&__acm__=1510614806_1e8b314e23f771f9b139d99596801365
* LMU BioDB 2017. (2017). Week 11. Retrieved November 13, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_11

==Links==
{{Template:ArashLari}}

Template:ArashLari

2017-11-14T09:17:31Z

ArashLari:

Class Journal Week 10

2017-11-07T04:11:35Z

ArashLari: /* QLanners Responses */

=Class Journal Week 10=
==Hayden Hinsch's Responses==

{{hhinsch}}

==Mary Balducci's Responses==
#I would want for my teammates to be available if I have questions about the work, and also to feel like they can ask me questions. I would want us all to share equal amounts of the work, and to be able to get in touch with each other if we need help with something.
#I think communication makes teamwork go smoothly. If we are all able to tell each other what we're working on and how we're doing it, I think it helps everyone better understand the project being done. This also includes being responsive to questions and figuring out who is responsible for each aspect of the project together.
#I think teamwork does not go smoothly when members of the team are not responsive. I think it's harder when I'm not entirely sure what another person in my group is doing, or if I have a question and they are not available to answer it.

[[User:Mbalducc|Mbalducc]] ([[User talk:Mbalducc|talk]]) 07:44, 3 November 2017 (PDT)

==Zachary Van Ysseldyk's Responses==
#As far as characteristics for a teammate, I value those who do not procrastinate and plan out how the assignment will get done to avoid unnecessary stress. Stringing along the same thought, I think that communication is crucial so that every person on the team is on the same page. I would also value those who assume the same amount of work as everybody else so that we all get a fair and well deserved grade.
#Bleeding in from question 1, I think that solid communication and a laid out plan will help the project go smoothly. Knowing when things will get done and how long they will take will help for smooth project completion.
#On the other hand, poor communication will greatly negatively affect a project. People might be doing the same things, people might not understand what they are doing which leads to frustration and division among the team. Also if somebody does not put in a sufficient amount of effort, this will anger others as the person who didn't do the the work should not receive the same letter grade.
 
[[User:Zvanysse|Zvanysse]] ([[User talk:Zvanysse|talk]]) 13:36, 5 November 2017 (PST)
 
{{Template:Zvanysse}}

==Dina Bashoura's Responses==
#I would like a partner who is well equipped in computer science, since I am lagging in that department. I also would like my partner to be good at communicating so that we can distinguish who should do what task and work together to make the overall project flow.
# Things like communication, trust, reliability, and good team chemistry make teamwork go smoothly.
#Things like lack of communication, doing sections not assigned to you or not assigning sections at all, not showing up to group meetings, and not being prepared when group meetings take place are things that make teamwork fail.
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 14:45, 6 November 2017 (PST)
{{template:dbashour}}

==Eddie Azinge's Responses==
# I don't find myself requiring very much from my teammates, just that the work that they hold themselves accountable for gets done at some point or another, and that inconsistencies in their work do not end up propogating upwards and affecting the status of the entire group, specifically because everyone has their own circumstances and priorities; as long as they deliver on their promises they're good in my book.
# Communication is key to teamwork; but trust and flexibility also play huge roles as well.
# Somewhat antithetical to the last question, a lack of trust and need to keep constant tabs on the members of the project often leads to rushed work, resentment, and dissidence within the team.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 15:46, 6 November 2017 (PST)

==Edward Bachoura's Response==
# I am looking for teammates that are flexible with their schedules so that we can actually find time to meet. Also, looking for teammates that are accountable for themselves.
# I find it important that whenever you work on a team, there should be some fun involved with the assignment, no matter the topic. When everybody is happy, it is usually easier to do the work at hand.
# Teamwork doesn't go smoothly when someone is unaccountable and makes themselves become more a negative weight rather than a help on the team.
[[User:Ebachour|Ebachour]] ([[User talk:Ebachour|talk]]) 16:54, 6 November 2017 (PST)

==Antonio Porras's Responses==
# '''What kinds of characteristics do you want in your teammates, and why?''' I look for teammates who are willing to take initiative and are able to communicate effectively about what needs to get done and what needs to be changed. Team members who aren't willing to speak their minds makes the process difficult and I look for these qualities because I want our group to achieve our goal and do so effectively.
# '''What kinds of things make teamwork go smoothly?''' Communication is extremely important in my opinion and there has to be someone taking charge with respect to delegating some tasks. I actually prefer not to be in this position but instead delegated a task because once I have some responsibility I take it seriously as it represents my work ethic.
# '''What kinds of things make teamwork not go so smoothly?''' Team members who put off work put a lot of pressure on the entire team. In addition, teams that don't have a uniform direction can be misdirected and may even miss the target in terms of goals.

[[User:Aporras1|Aporras1]] ([[User talk:Aporras1|talk]]) 17:44, 6 November 2017 (PST)

{{Template:Aporras1Links}}

==Emma Tyrnauer's Responses==
#The kind of characteristics that I want in my teammates are good communication skills and responsibility over their contributions (in terms of meeting deadlines and asking for help when it is needed). Furthermore, I think being detail oriented is important in group setting to ensure everything is being completed properly.
#Good communication and collaboration makes teamwork go smoothly. The ability to divide and conquer is very important so that while the assignment is completed in a group setting and collaboration occurs consistently throughout the process, the strengths of each team member are being used and emphasized.
#Bad communication and a lack of responsibility make teamwork go not so smoothly. Also, not being able to trust your teammates and ask for help when it is needed represents poor teamwork.

[[User:Emmatyrnauer|Emmatyrnauer]] ([[User talk:Emmatyrnauer|talk]]) 17:55, 6 November 2017 (PST)

{{Emmatyrnauer}}

[[Category:Shared]]
[[Category:Journal Entry]]

==Blair Hamilton's Response==
#What kinds of characteristics do you want in your teammates, and why?
#*For a teammate I would like organization when it comes to deadlines, but also able to work independently of the group. Being able to disperse tasks is very helpful when it comes to big projects, so having a teammate who appreciated independent work, with good communication can be super helpful. I also would love a teammate who can critique my work as well in order to make sure the project as a whole gets done well and accurately.
#What kinds of things make teamwork go smoothly?
#*In order for the project to smoothly, communication is a big part. If a partner is being silent or not asking questions when he/she is confused, then the project suffers.
#What kinds of things make teamwork not go so smoothly?
#*Partners who don't contribute, don't ask questions and don't follow up make projects go not-so-smoothly. Deadlines and helping the entire group project makes it go quicker and much easier!

[[User:Bhamilton18|Bhamilton18]] ([[User talk:Bhamilton18|talk]]) 18:49, 6 November 2017 (PST)
{{Bhamilton18}}

==QLanners Responses==
#There are a number of characteristics that I want in a good teammate, but among the most important ones are accountability and good communication. Group projects are much more effective when all of the partners in the group do the tasks assigned to them when they say they will. Furthermore, when problems do arise (as they always do), good communication skills can help limit these problems and make them much more manageable.
#Perhaps the most important thing to make teamwork go smoothly is effective planning. If all of the tasks can be mapped out, and everyone is given a job to complete by a certain deadline, the overall project will progress much faster and with fewer complications.
#There are several things that can make teamwork not go smoothly, but poor communication is definitely the number one culprit. Without communication, it is impossible to even set times to meet and work on the project. And without meetings and communication, team members become confused as to what they should be doing and that results in partners either not doing their work, or doing a poor job of it.

[[User:Qlanners|Qlanners]] ([[User talk:Qlanners|talk]]) 19:51, 6 November 2017 (PST)
{{Template:QLannersLinks}}

== Arash Lari's Responses==
# I want my teammate and I to be able to have open, honest communication with each other and both of a drive and motivation to submit quality work. If we agree on the latter, and according to the former, then any other issues that come up would be resolved easily.
# As mentioned previously, communication is vital. To add to that, I would say reaching an agreement on the work dynamic is important as well. It's important that each member of the team agrees on what their responsibilities are in order to achieve optimal performance.
# Again, if teammates don't communicate with each other and try to organize time well it can lead to a lot of stressed out last minute work.
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 20:11, 6 November 2017 (PST)
{{Template:ArashLari}}

File:Work.zip

2017-11-07T02:20:03Z

ArashLari: ArashLari uploaded a new version of File:Work.zip

All the work files for the Week 10 assignment

ArashLari Week 10

2017-11-07T02:18:00Z

ArashLari: summary paragraph

==File==
[[File:Work.zip]]

==Electronic Notebook==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* Insert a new worksheet into your Excel workbook, and name it "dHMO1_stem".
#* Select all of the data from your "dHMO1_ANOVA" worksheet and Paste special > paste values into your "dHMO1_stem" worksheet.
#** Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** '''It should be noted that because dHMO1 is a special case, we will use the regular p-value instead of the B-H corrected value.'''
#*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. '''''Record the number of genes left in your electronic notebook.'''''
#** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
#** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
#** Save your work. Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Now download and extract the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* Click on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
##* Click on the radio button ''No normalization/add 0''.
##* Check the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
##* '''Note: for this to work, you must replace all instances of "#DIV/0" with empty text, or else the program will not run properly.'''
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
##*Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. '''''Each member of your group should choose a different profile.'''''
For this section, I chose '''Profile 2'''
Answer the following:
##* '''''Why did you select this profile? In other words, why was it interesting to you?'''''
##* I chose this profile because it seemed to have an interesting reaction to cold shock. It showed a strong negative expression change at minute 30, but at minute 90 it seems that the genes seemed to have "bounced back" and expressed a similar (but less extreme) change in the positive range. This is shown by the profile type of "(0, -2, -2, 0, 1 0)"
##* '''''How many genes belong to this profile?'''''
##* There are 49 genes assigned to this profile.
##* '''''How many genes were expected to belong to this profile?'''''
##* There were 18.7 genes expected, so more genes than expected were effected.
##* '''''What is the p value for the enrichment of genes in this profile?'''''
##* The p value for the enrichment of genes in this profile is 2.1 E-9, so very small.
Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
##* Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''How many GO terms are associated with this profile at p < 0.05?'''''
There were 9 genes associated with this profile at p <0.05.
The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''
There were actually NO GO terms associated with a corrected p value < 0.05. This makes sense as dHMO1 is a special case.
##* Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
##** Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
##**'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org]. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor that was deleted from your strain?'''''
##** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
##** Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
##** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].
==== My GO Terms defined ====
All of this information was taken directly from [http://geneontology.org http://geneontology.org].

'''Accession: GO:0031399'''
Name: regulation of protein modification process
Ontology: biological_process
Definition: Any process that modulates the frequency, rate or extent of the covalent alteration of one or more amino acid residues within a protein.

'''Accession: GO:0050794'''
Name: regulation of cellular process
Ontology: biological_process
Definition: Any process that modulates the frequency, rate or extent of a cellular process, any of those that are carried out at the cellular level, but are not necessarily restricted to a single cell. For example, cell communication occurs among more than one cell, but occurs at the cellular level.

'''Accession: GO:0098588'''
Name: bounding membrane of organelle
Ontology: cellular_component
Definition: The lipid bilayer that forms the outer-most layer of an organelle.

'''Accession: GO:0050789'''
Name: regulation of biological process
Ontology: biological_process
Definition: Any process that modulates the frequency, rate or extent of a biological process. Biological processes are regulated by many means; examples include the control of gene expression, protein modification or interaction with a protein or substrate molecule.

'''Accession: GO:0006796'''
Name: phosphate-containing compound metabolic process
Ontology: biological_process
Definition: The chemical reactions and pathways involving the phosphate group, the anion or salt of any phosphoric acid.

'''Accession: GO:0005794'''
Name: Golgi apparatus
Ontology: cellular_component
Definition: A compound membranous cytoplasmic organelle of eukaryotic cells, consisting of flattened, ribosome-free vesicles arranged in a more or less regular stack. The Golgi apparatus differs from the endoplasmic reticulum in often having slightly thicker membranes, appearing in sections as a characteristic shallow semicircle so that the convex side (cis or entry face) abuts the endoplasmic reticulum, secretory vesicles emerging from the concave side (trans or exit face). In vertebrate cells there is usually one such organelle, while in invertebrates and plants, where they are known usually as dictyosomes, there may be several scattered in the cytoplasm. The Golgi apparatus processes proteins produced on the ribosomes of the rough endoplasmic reticulum; such processing includes modification of the core oligosaccharides of glycoproteins, and the sorting and packaging of proteins for transport to a variety of cellular locations. Three different regions of the Golgi are now recognized both in terms of structure and function: cis, in the vicinity of the cis face, trans, in the vicinity of the trans face, and medial, lying between the cis and trans regions.
'''''This is the stopping point for the Week 10 Assignment.''''' We will pick up the next steps in the analysis in subsequent weeks.
== Summary of Knowledge ==
After processing all of the data from the spreadsheets of dHMO1, and reading the definitions of the genes most effected in profile #2, I learned some interesting information. It appears that dHMO1 "turns off" genes related to protein inscription when it's cold in order to conserve energy, and when it warms up again it seems to kick it into full gear in order to compensate for when it was too cold. Of course my rudimentary, limited knowledge of biology limits my understanding of this process, but this seems to be the main idea.
==Acknowledgements==
Hayden and I worked on this assignment together, we compare results together in class and texted each other if we had any questions.
While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
==References==
#LMU BioDB 2017. (2017). Week 10. Retrieved November 2, 2017, from
https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 19:04, 5 November 2017 (PST)

ArashLari Week 10

2017-11-07T02:08:38Z

ArashLari: /* Electronic Notebook */ added GO terms

==File==
[[File:Work.zip]]

==Electronic Notebook==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* Insert a new worksheet into your Excel workbook, and name it "dHMO1_stem".
#* Select all of the data from your "dHMO1_ANOVA" worksheet and Paste special > paste values into your "dHMO1_stem" worksheet.
#** Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** '''It should be noted that because dHMO1 is a special case, we will use the regular p-value instead of the B-H corrected value.'''
#*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. '''''Record the number of genes left in your electronic notebook.'''''
#** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
#** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
#** Save your work. Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Now download and extract the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* Click on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
##* Click on the radio button ''No normalization/add 0''.
##* Check the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
##* '''Note: for this to work, you must replace all instances of "#DIV/0" with empty text, or else the program will not run properly.'''
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
##*Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. '''''Each member of your group should choose a different profile.'''''
For this section, I chose '''Profile 2'''
Answer the following:
##* '''''Why did you select this profile? In other words, why was it interesting to you?'''''
##* I chose this profile because it seemed to have an interesting reaction to cold shock. It showed a strong negative expression change at minute 30, but at minute 90 it seems that the genes seemed to have "bounced back" and expressed a similar (but less extreme) change in the positive range. This is shown by the profile type of "(0, -2, -2, 0, 1 0)"
##* '''''How many genes belong to this profile?'''''
##* There are 49 genes assigned to this profile.
##* '''''How many genes were expected to belong to this profile?'''''
##* There were 18.7 genes expected, so more genes than expected were effected.
##* '''''What is the p value for the enrichment of genes in this profile?'''''
##* The p value for the enrichment of genes in this profile is 2.1 E-9, so very small.
Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
##* Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''How many GO terms are associated with this profile at p < 0.05?'''''
There were 9 genes associated with this profile at p <0.05.
The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''
There were actually NO GO terms associated with a corrected p value < 0.05. This makes sense as dHMO1 is a special case.
##* Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
##** Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
##**'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org]. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor that was deleted from your strain?'''''
##** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
##** Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
##** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].
==== My GO Terms defined ====
All of this information was taken directly from [http://geneontology.org http://geneontology.org].

'''Accession: GO:0031399'''
Name: regulation of protein modification process
Ontology: biological_process
Definition: Any process that modulates the frequency, rate or extent of the covalent alteration of one or more amino acid residues within a protein.

'''Accession: GO:0050794'''
Name: regulation of cellular process
Ontology: biological_process
Definition: Any process that modulates the frequency, rate or extent of a cellular process, any of those that are carried out at the cellular level, but are not necessarily restricted to a single cell. For example, cell communication occurs among more than one cell, but occurs at the cellular level.

'''Accession: GO:0098588'''
Name: bounding membrane of organelle
Ontology: cellular_component
Definition: The lipid bilayer that forms the outer-most layer of an organelle.

'''Accession: GO:0050789'''
Name: regulation of biological process
Ontology: biological_process
Definition: Any process that modulates the frequency, rate or extent of a biological process. Biological processes are regulated by many means; examples include the control of gene expression, protein modification or interaction with a protein or substrate molecule.

'''Accession: GO:0006796'''
Name: phosphate-containing compound metabolic process
Ontology: biological_process
Definition: The chemical reactions and pathways involving the phosphate group, the anion or salt of any phosphoric acid.

'''Accession: GO:0005794'''
Name: Golgi apparatus
Ontology: cellular_component
Definition: A compound membranous cytoplasmic organelle of eukaryotic cells, consisting of flattened, ribosome-free vesicles arranged in a more or less regular stack. The Golgi apparatus differs from the endoplasmic reticulum in often having slightly thicker membranes, appearing in sections as a characteristic shallow semicircle so that the convex side (cis or entry face) abuts the endoplasmic reticulum, secretory vesicles emerging from the concave side (trans or exit face). In vertebrate cells there is usually one such organelle, while in invertebrates and plants, where they are known usually as dictyosomes, there may be several scattered in the cytoplasm. The Golgi apparatus processes proteins produced on the ribosomes of the rough endoplasmic reticulum; such processing includes modification of the core oligosaccharides of glycoproteins, and the sorting and packaging of proteins for transport to a variety of cellular locations. Three different regions of the Golgi are now recognized both in terms of structure and function: cis, in the vicinity of the cis face, trans, in the vicinity of the trans face, and medial, lying between the cis and trans regions.
'''''This is the stopping point for the Week 10 Assignment.''''' We will pick up the next steps in the analysis in subsequent weeks.

==Acknowledgements==
Hayden and I worked on this assignment together, we compare results together in class and texted each other if we had any questions.
While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
==References==
#LMU BioDB 2017. (2017). Week 10. Retrieved November 2, 2017, from
https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 19:04, 5 November 2017 (PST)

ArashLari Week 10

2017-11-07T01:45:07Z

ArashLari: /* Electronic Notebook */ updated answers

==File==
[[File:Work.zip]]

==Electronic Notebook==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* Insert a new worksheet into your Excel workbook, and name it "dHMO1_stem".
#* Select all of the data from your "dHMO1_ANOVA" worksheet and Paste special > paste values into your "dHMO1_stem" worksheet.
#** Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** '''It should be noted that because dHMO1 is a special case, we will use the regular p-value instead of the B-H corrected value.'''
#*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. '''''Record the number of genes left in your electronic notebook.'''''
#** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
#** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
#** Save your work. Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Now download and extract the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* Click on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
##* Click on the radio button ''No normalization/add 0''.
##* Check the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
##* '''Note: for this to work, you must replace all instances of "#DIV/0" with empty text, or else the program will not run properly.'''
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
##*Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. '''''Each member of your group should choose a different profile.'''''
For this section, I chose '''Profile 2'''
Answer the following:
##* '''''Why did you select this profile? In other words, why was it interesting to you?'''''
##* I chose this profile because it seemed to have an interesting reaction to cold shock. It showed a strong negative expression change at minute 30, but at minute 90 it seems that the genes seemed to have "bounced back" and expressed a similar (but less extreme) change in the positive range. This is shown by the profile type of "(0, -2, -2, 0, 1 0)"
##* '''''How many genes belong to this profile?'''''
##* There are 49 genes assigned to this profile.
##* '''''How many genes were expected to belong to this profile?'''''
##* There were 18.7 genes expected, so more genes than expected were effected.
##* '''''What is the p value for the enrichment of genes in this profile?'''''
##* The p value for the enrichment of genes in this profile is 2.1 E-9, so very small.
Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
##* Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''How many GO terms are associated with this profile at p < 0.05?'''''
There were 9 genes associated with this profile at p <0.05.
The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''
There were actually NO GO terms associated with a corrected p value < 0.05. This makes sense as dHMO1 is a special case.
##* Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
##** Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
##**'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org]. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor that was deleted from your strain?'''''
##** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
##** Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
##** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].

'''''This is the stopping point for the Week 10 Assignment.''''' We will pick up the next steps in the analysis in subsequent weeks.

==Acknowledgements==
Hayden and I worked on this assignment together, we compare results together in class and texted each other if we had any questions.
While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
==References==
#LMU BioDB 2017. (2017). Week 10. Retrieved November 2, 2017, from
https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 19:04, 5 November 2017 (PST)

ArashLari Week 10

2017-11-06T03:33:54Z

ArashLari: /* Electronic Notebook */ answering more questions from part 5

==File==
[[File:Work.zip]]

==Electronic Notebook==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* Insert a new worksheet into your Excel workbook, and name it "dHMO1_stem".
#* Select all of the data from your "dHMO1_ANOVA" worksheet and Paste special > paste values into your "dHMO1_stem" worksheet.
#** Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** '''It should be noted that because dHMO1 is a special case, we will use the regular p-value instead of the B-H corrected value.'''
#*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. '''''Record the number of genes left in your electronic notebook.'''''
#** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
#** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
#** Save your work. Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Now download and extract the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* Click on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
##* Click on the radio button ''No normalization/add 0''.
##* Check the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
##* '''Note: for this to work, you must replace all instances of "#DIV/0" with empty text, or else the program will not run properly.'''
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
##*Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. '''''Each member of your group should choose a different profile.'''''
For this section, I chose '''Profile 2'''
Answer the following:
##* '''''Why did you select this profile? In other words, why was it interesting to you?'''''
##* I chose this profile because it seemed to have an interesting reaction to cold shock. It showed a strong negative expression change at minute 30, but at minute 90 it seems that the genes seemed to have "bounced back" and expressed a similar (but less extreme) change in the positive range.
##* '''''How many genes belong to this profile?'''''
##* There are 157 genes that belong to this profile.
##* '''''How many genes were expected to belong to this profile?'''''
##* There were around a 1000 genes that passed the p-value filter, so around that number of genes.
##* '''''What is the p value for the enrichment of genes in this profile?'''''
##* TODO
Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
##* Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''How many GO terms are associated with this profile at p < 0.05?'''''
There were 9 genes associated with this profile at p <0.05.
The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''
There were actually NO GO terms associated with a corrected p value < 0.05. This makes sense as dHMO1 is a special case.
##* Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
##** Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
##**'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org]. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor that was deleted from your strain?'''''
##** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
##** Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
##** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].

'''''This is the stopping point for the Week 10 Assignment.''''' We will pick up the next steps in the analysis in subsequent weeks.

==Acknowledgements==
Hayden and I worked on this assignment together, we compare results together in class and texted each other if we had any questions.
While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
==References==
#LMU BioDB 2017. (2017). Week 10. Retrieved November 2, 2017, from
https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 19:04, 5 November 2017 (PST)

ArashLari Week 10

2017-11-06T03:27:28Z

ArashLari: /* Electronic Notebook */ Answering first questions about profile 2

==File==
[[File:Work.zip]]

==Electronic Notebook==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* Insert a new worksheet into your Excel workbook, and name it "dHMO1_stem".
#* Select all of the data from your "dHMO1_ANOVA" worksheet and Paste special > paste values into your "dHMO1_stem" worksheet.
#** Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** '''It should be noted that because dHMO1 is a special case, we will use the regular p-value instead of the B-H corrected value.'''
#*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. '''''Record the number of genes left in your electronic notebook.'''''
#** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
#** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
#** Save your work. Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Now download and extract the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* Click on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
##* Click on the radio button ''No normalization/add 0''.
##* Check the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
##* '''Note: for this to work, you must replace all instances of "#DIV/0" with empty text, or else the program will not run properly.'''
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
##*Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. '''''Each member of your group should choose a different profile.'''''
For this section, I chose '''Profile 2'''
Answer the following:
##* '''''Why did you select this profile? In other words, why was it interesting to you?'''''
##* I chose this profile because it seemed to have an interesting reaction to cold shock. It showed a strong negative expression change at minute 30, but at minute 90 it seems that the genes seemed to have "bounced back" and expressed a similar (but less extreme) change in the positive range.
##* '''''How many genes belong to this profile?'''''
##* There are 157 genes that belong to this profile.
##* '''''How many genes were expected to belong to this profile?'''''
##* There were around a 1000 genes that passed the p-value filter, so around that number of genes.
##* '''''What is the p value for the enrichment of genes in this profile?'''''
##* TODO
Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
##* Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''How many GO terms are associated with this profile at p < 0.05?''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''
##* Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
##** Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
##**'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org]. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor that was deleted from your strain?'''''
##** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
##** Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
##** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].

'''''This is the stopping point for the Week 10 Assignment.''''' We will pick up the next steps in the analysis in subsequent weeks.

==Acknowledgements==
Hayden and I worked on this assignment together, we compare results together in class and texted each other if we had any questions.
While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
==References==
#LMU BioDB 2017. (2017). Week 10. Retrieved November 2, 2017, from
https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 19:04, 5 November 2017 (PST)

ArashLari Week 10

2017-11-06T03:13:15Z

ArashLari: /* File */ this is a link to all the files used for this assignment

==File==
[[File:Work.zip]]

==Electronic Notebook==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* Insert a new worksheet into your Excel workbook, and name it "dHMO1_stem".
#* Select all of the data from your "dHMO1_ANOVA" worksheet and Paste special > paste values into your "dHMO1_stem" worksheet.
#** Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** '''It should be noted that because dHMO1 is a special case, we will use the regular p-value instead of the B-H corrected value.'''
#*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. '''''Record the number of genes left in your electronic notebook.'''''
#** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
#** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
#** Save your work. Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Now download and extract the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* Click on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
##* Click on the radio button ''No normalization/add 0''.
##* Check the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
##* '''Note: for this to work, you must replace all instances of "#DIV/0" with empty text, or else the program will not run properly.'''
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
##*Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. '''''Each member of your group should choose a different profile.''''' Answer the following:
##* '''''Why did you select this profile? In other words, why was it interesting to you?'''''
##* '''''How many genes belong to this profile?'''''
##* '''''How many genes were expected to belong to this profile?'''''
##* '''''What is the p value for the enrichment of genes in this profile?''''' Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
##* Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''How many GO terms are associated with this profile at p < 0.05?''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''
##* Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
##** Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
##**'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org]. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor that was deleted from your strain?'''''
##** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
##** Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
##** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].

'''''This is the stopping point for the Week 10 Assignment.''''' We will pick up the next steps in the analysis in subsequent weeks.

==Acknowledgements==
Hayden and I worked on this assignment together, we compare results together in class and texted each other if we had any questions.
While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
==References==
#LMU BioDB 2017. (2017). Week 10. Retrieved November 2, 2017, from
https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 19:04, 5 November 2017 (PST)

ArashLari Week 10

2017-11-06T03:12:05Z

ArashLari: /* File */ added my zip file

==File==
[[File:Work.zip] This is a link to all the files used for this assignment]

==Electronic Notebook==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* Insert a new worksheet into your Excel workbook, and name it "dHMO1_stem".
#* Select all of the data from your "dHMO1_ANOVA" worksheet and Paste special > paste values into your "dHMO1_stem" worksheet.
#** Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** '''It should be noted that because dHMO1 is a special case, we will use the regular p-value instead of the B-H corrected value.'''
#*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. '''''Record the number of genes left in your electronic notebook.'''''
#** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
#** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
#** Save your work. Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Now download and extract the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* Click on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
##* Click on the radio button ''No normalization/add 0''.
##* Check the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
##* '''Note: for this to work, you must replace all instances of "#DIV/0" with empty text, or else the program will not run properly.'''
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
##*Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. '''''Each member of your group should choose a different profile.''''' Answer the following:
##* '''''Why did you select this profile? In other words, why was it interesting to you?'''''
##* '''''How many genes belong to this profile?'''''
##* '''''How many genes were expected to belong to this profile?'''''
##* '''''What is the p value for the enrichment of genes in this profile?''''' Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
##* Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''How many GO terms are associated with this profile at p < 0.05?''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''
##* Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
##** Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
##**'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org]. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor that was deleted from your strain?'''''
##** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
##** Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
##** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].

'''''This is the stopping point for the Week 10 Assignment.''''' We will pick up the next steps in the analysis in subsequent weeks.

==Acknowledgements==
Hayden and I worked on this assignment together, we compare results together in class and texted each other if we had any questions.
While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
==References==
#LMU BioDB 2017. (2017). Week 10. Retrieved November 2, 2017, from
https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 19:04, 5 November 2017 (PST)

ArashLari Week 10

2017-11-06T03:10:56Z

ArashLari: /* Electronic Notebook */ added a special case for #DIV/0

==File==

==Electronic Notebook==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* Insert a new worksheet into your Excel workbook, and name it "dHMO1_stem".
#* Select all of the data from your "dHMO1_ANOVA" worksheet and Paste special > paste values into your "dHMO1_stem" worksheet.
#** Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** '''It should be noted that because dHMO1 is a special case, we will use the regular p-value instead of the B-H corrected value.'''
#*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. '''''Record the number of genes left in your electronic notebook.'''''
#** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
#** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
#** Save your work. Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Now download and extract the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* Click on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
##* Click on the radio button ''No normalization/add 0''.
##* Check the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
##* '''Note: for this to work, you must replace all instances of "#DIV/0" with empty text, or else the program will not run properly.'''
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
##*Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. '''''Each member of your group should choose a different profile.''''' Answer the following:
##* '''''Why did you select this profile? In other words, why was it interesting to you?'''''
##* '''''How many genes belong to this profile?'''''
##* '''''How many genes were expected to belong to this profile?'''''
##* '''''What is the p value for the enrichment of genes in this profile?''''' Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
##* Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''How many GO terms are associated with this profile at p < 0.05?''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''
##* Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
##** Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
##**'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org]. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor that was deleted from your strain?'''''
##** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
##** Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
##** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].

'''''This is the stopping point for the Week 10 Assignment.''''' We will pick up the next steps in the analysis in subsequent weeks.

==Acknowledgements==
Hayden and I worked on this assignment together, we compare results together in class and texted each other if we had any questions.
While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
==References==
#LMU BioDB 2017. (2017). Week 10. Retrieved November 2, 2017, from
https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 19:04, 5 November 2017 (PST)

ArashLari Week 10

2017-11-06T03:08:03Z

ArashLari: /* Clustering and GO Term Enrichment with stem */ added our special case

==File==

==Electronic Notebook==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* Insert a new worksheet into your Excel workbook, and name it "dHMO1_stem".
#* Select all of the data from your "dHMO1_ANOVA" worksheet and Paste special > paste values into your "dHMO1_stem" worksheet.
#** Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** '''It should be noted that because dHMO1 is a special case, we will use the regular p-value instead of the B-H corrected value.'''
#*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. '''''Record the number of genes left in your electronic notebook.'''''
#** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
#** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
#** Save your work. Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Now download and extract the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* Click on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
##* Click on the radio button ''No normalization/add 0''.
##* Check the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
##*Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. '''''Each member of your group should choose a different profile.''''' Answer the following:
##* '''''Why did you select this profile? In other words, why was it interesting to you?'''''
##* '''''How many genes belong to this profile?'''''
##* '''''How many genes were expected to belong to this profile?'''''
##* '''''What is the p value for the enrichment of genes in this profile?''''' Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
##* Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''How many GO terms are associated with this profile at p < 0.05?''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''
##* Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
##** Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
##**'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org]. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor that was deleted from your strain?'''''
##** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
##** Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
##** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].

'''''This is the stopping point for the Week 10 Assignment.''''' We will pick up the next steps in the analysis in subsequent weeks.

==Acknowledgements==
Hayden and I worked on this assignment together, we compare results together in class and texted each other if we had any questions.
While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
==References==
#LMU BioDB 2017. (2017). Week 10. Retrieved November 2, 2017, from
https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 19:04, 5 November 2017 (PST)

ArashLari Week 10

2017-11-06T03:06:41Z

ArashLari: /* Electronic Notebook */ replaced all instances of "(STRAIN)" with "dHMO1"

==File==

==Electronic Notebook==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* Insert a new worksheet into your Excel workbook, and name it "dHMO1_stem".
#* Select all of the data from your "dHMO1_ANOVA" worksheet and Paste special > paste values into your "dHMO1_stem" worksheet.
#** Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. '''''Record the number of genes left in your electronic notebook.'''''
#** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
#** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
#** Save your work. Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Now download and extract the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* Click on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
##* Click on the radio button ''No normalization/add 0''.
##* Check the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
##*Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. '''''Each member of your group should choose a different profile.''''' Answer the following:
##* '''''Why did you select this profile? In other words, why was it interesting to you?'''''
##* '''''How many genes belong to this profile?'''''
##* '''''How many genes were expected to belong to this profile?'''''
##* '''''What is the p value for the enrichment of genes in this profile?''''' Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
##* Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''How many GO terms are associated with this profile at p < 0.05?''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''
##* Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
##** Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
##**'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org]. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor that was deleted from your strain?'''''
##** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
##** Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
##** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].

'''''This is the stopping point for the Week 10 Assignment.''''' We will pick up the next steps in the analysis in subsequent weeks.

==Acknowledgements==
Hayden and I worked on this assignment together, we compare results together in class and texted each other if we had any questions.
While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
==References==
#LMU BioDB 2017. (2017). Week 10. Retrieved November 2, 2017, from
https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 19:04, 5 November 2017 (PST)

ArashLari Week 10

2017-11-06T03:04:55Z

ArashLari: references addition

==File==

==Electronic Notebook==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* Insert a new worksheet into your Excel workbook, and name it "(STRAIN)_stem".
#* Select all of the data from your "(STRAIN)_ANOVA" worksheet and Paste special > paste values into your "(STRAIN)_stem" worksheet.
#** Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. '''''Record the number of genes left in your electronic notebook.'''''
#** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
#** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
#** Save your work. Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Now download and extract the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* Click on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
##* Click on the radio button ''No normalization/add 0''.
##* Check the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
##*Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. '''''Each member of your group should choose a different profile.''''' Answer the following:
##* '''''Why did you select this profile? In other words, why was it interesting to you?'''''
##* '''''How many genes belong to this profile?'''''
##* '''''How many genes were expected to belong to this profile?'''''
##* '''''What is the p value for the enrichment of genes in this profile?''''' Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
##* Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''How many GO terms are associated with this profile at p < 0.05?''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''
##* Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
##** Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
##**'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org]. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor that was deleted from your strain?'''''
##** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
##** Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
##** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].

'''''This is the stopping point for the Week 10 Assignment.''''' We will pick up the next steps in the analysis in subsequent weeks.
==Acknowledgements==
Hayden and I worked on this assignment together, we compare results together in class and texted each other if we had any questions.
While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
==References==
#LMU BioDB 2017. (2017). Week 10. Retrieved November 2, 2017, from
https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 19:04, 5 November 2017 (PST)

ArashLari Week 10

2017-11-06T03:03:16Z

ArashLari: /* Acknowledgements */

==File==

==Electronic Notebook==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* Insert a new worksheet into your Excel workbook, and name it "(STRAIN)_stem".
#* Select all of the data from your "(STRAIN)_ANOVA" worksheet and Paste special > paste values into your "(STRAIN)_stem" worksheet.
#** Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. '''''Record the number of genes left in your electronic notebook.'''''
#** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
#** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
#** Save your work. Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Now download and extract the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* Click on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
##* Click on the radio button ''No normalization/add 0''.
##* Check the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
##*Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. '''''Each member of your group should choose a different profile.''''' Answer the following:
##* '''''Why did you select this profile? In other words, why was it interesting to you?'''''
##* '''''How many genes belong to this profile?'''''
##* '''''How many genes were expected to belong to this profile?'''''
##* '''''What is the p value for the enrichment of genes in this profile?''''' Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
##* Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''How many GO terms are associated with this profile at p < 0.05?''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''
##* Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
##** Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
##**'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org]. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor that was deleted from your strain?'''''
##** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
##** Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
##** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].

'''''This is the stopping point for the Week 10 Assignment.''''' We will pick up the next steps in the analysis in subsequent weeks.

==References==
==Acknowledgements==
Hayden and I worked on this assignment together, we compare results together in class and texted each other if we had any questions.
While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.

ArashLari Week 10

2017-11-06T03:01:56Z

ArashLari: setting up page

==File==

==Electronic Notebook==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* Insert a new worksheet into your Excel workbook, and name it "(STRAIN)_stem".
#* Select all of the data from your "(STRAIN)_ANOVA" worksheet and Paste special > paste values into your "(STRAIN)_stem" worksheet.
#** Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. '''''Record the number of genes left in your electronic notebook.'''''
#** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
#** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
#** Save your work. Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Now download and extract the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* Click on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
##* Click on the radio button ''No normalization/add 0''.
##* Check the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
##*Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. '''''Each member of your group should choose a different profile.''''' Answer the following:
##* '''''Why did you select this profile? In other words, why was it interesting to you?'''''
##* '''''How many genes belong to this profile?'''''
##* '''''How many genes were expected to belong to this profile?'''''
##* '''''What is the p value for the enrichment of genes in this profile?''''' Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
##* Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''How many GO terms are associated with this profile at p < 0.05?''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''
##* Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
##** Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
##**'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org]. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor that was deleted from your strain?'''''
##** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
##** Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
##** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].

'''''This is the stopping point for the Week 10 Assignment.''''' We will pick up the next steps in the analysis in subsequent weeks.

==References==
==Acknowledgements==

File:Work.zip

2017-11-02T23:17:19Z

ArashLari: All the work files for the Week 10 assignment

All the work files for the Week 10 assignment

ArashLari Week 9

2017-10-31T06:12:31Z

ArashLari: /* Electronic Workbook */ clarifying what passed means

= Electronic Workbook =
For the purposes of testing I used the following files:
*21-genes_31-edges_Schade-data_estimation_output.xlsx
*21-genes_31-edges_Schade-data_estimation_output.sif
*21-genes_31-edges_Schade-data_estimation_output.graphml
Then proceed to the GRNSight-beta from the link provided in the instructions for this assignment
*Note: A test passing means that it satisfied all the criteria detailed in the instructions
== Test 1 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 2 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 3 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 4 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 5 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 6 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 7 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 8 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 9 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 10 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 11 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 12 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 13 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 14 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 15 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 16 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 17 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 18 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Observations ==
While they all passed, it should be noted that for tests 7 through 12, the user can technically see all the numbers, but sometimes they might overlap and become hard to read.
This can be seen in the following image:

[[File:ArashObservation.png]]

= Web Service API Exploration =
While I was originally confused as to what the assignment was asking for, once we figured it out it became much clearer. I simply went to the ensemble api page, and found the command that returned the json file using the gene name. The example given on the website had the species as "homo_sapien", so i changed that to "saccharomyces_cerevisiae" in the url and put the name of a random yeast file to test it. This process worked, but because the instructions pointed to [http://rest.ensembl.org/documentation/info/lookup] I used the ID retrieved from the first command and placed it in the command given from this page to do it the way the instructions alluded to.
==Deliverable==
*Note: to find the json file for any other gene, simply replace the text that says "GENE" in the url with your desired gene name. (this code won't work as it's generic)
curl 'http://rest.ensembl.org/lookup/symbol/saccharomyces_cerevisiae/GENE?' -H 'Content-type:application/json'
From there you could put the id retrieved from the json file into the url where it says "ID" in uppercase, but the json file above also returns all the relevant information already.
curl 'http://rest.ensembl.org/lookup/id/ID?expand=1' -H 'Content-type:application/json'

= Acknowledgements =
Nicole Kalcic was my partner and we met up once and worked over text message. I also worked with Zach Van Ysseldyk and Antoni Porras on the Web API section and we figured out how to return the desired deliverable together.
While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 15:36, 30 October 2017 (PDT)
{{Template:ArashLari}}

ArashLari Week 9

2017-10-31T06:11:28Z

ArashLari: /* Deliverable */ formatting

= Electronic Workbook =
For the purposes of testing I used the following files:
*21-genes_31-edges_Schade-data_estimation_output.xlsx
*21-genes_31-edges_Schade-data_estimation_output.sif
*21-genes_31-edges_Schade-data_estimation_output.graphml
Then proceed to the GRNSight-beta from the link provided in the instructions for this assignment
== Test 1 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 2 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 3 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 4 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 5 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 6 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 7 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 8 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 9 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 10 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 11 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 12 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 13 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 14 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 15 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 16 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 17 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 18 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Observations ==
While they all passed, it should be noted that for tests 7 through 12, the user can technically see all the numbers, but sometimes they might overlap and become hard to read.
This can be seen in the following image:

[[File:ArashObservation.png]]

= Web Service API Exploration =
While I was originally confused as to what the assignment was asking for, once we figured it out it became much clearer. I simply went to the ensemble api page, and found the command that returned the json file using the gene name. The example given on the website had the species as "homo_sapien", so i changed that to "saccharomyces_cerevisiae" in the url and put the name of a random yeast file to test it. This process worked, but because the instructions pointed to [http://rest.ensembl.org/documentation/info/lookup] I used the ID retrieved from the first command and placed it in the command given from this page to do it the way the instructions alluded to.
==Deliverable==
*Note: to find the json file for any other gene, simply replace the text that says "GENE" in the url with your desired gene name. (this code won't work as it's generic)
curl 'http://rest.ensembl.org/lookup/symbol/saccharomyces_cerevisiae/GENE?' -H 'Content-type:application/json'
From there you could put the id retrieved from the json file into the url where it says "ID" in uppercase, but the json file above also returns all the relevant information already.
curl 'http://rest.ensembl.org/lookup/id/ID?expand=1' -H 'Content-type:application/json'

= Acknowledgements =
Nicole Kalcic was my partner and we met up once and worked over text message. I also worked with Zach Van Ysseldyk and Antoni Porras on the Web API section and we figured out how to return the desired deliverable together.
While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 15:36, 30 October 2017 (PDT)
{{Template:ArashLari}}

ArashLari Week 9

2017-10-31T06:10:52Z

ArashLari: /* Web Service API Exploration */ Made it generic and added a "how i did it"

= Electronic Workbook =
For the purposes of testing I used the following files:
*21-genes_31-edges_Schade-data_estimation_output.xlsx
*21-genes_31-edges_Schade-data_estimation_output.sif
*21-genes_31-edges_Schade-data_estimation_output.graphml
Then proceed to the GRNSight-beta from the link provided in the instructions for this assignment
== Test 1 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 2 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 3 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 4 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 5 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 6 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 7 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 8 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 9 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 10 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 11 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 12 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 13 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 14 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 15 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 16 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 17 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 18 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Observations ==
While they all passed, it should be noted that for tests 7 through 12, the user can technically see all the numbers, but sometimes they might overlap and become hard to read.
This can be seen in the following image:

[[File:ArashObservation.png]]

= Web Service API Exploration =
While I was originally confused as to what the assignment was asking for, once we figured it out it became much clearer. I simply went to the ensemble api page, and found the command that returned the json file using the gene name. The example given on the website had the species as "homo_sapien", so i changed that to "saccharomyces_cerevisiae" in the url and put the name of a random yeast file to test it. This process worked, but because the instructions pointed to [http://rest.ensembl.org/documentation/info/lookup] I used the ID retrieved from the first command and placed it in the command given from this page to do it the way the instructions alluded to.
==Deliverable==
*Note: to find the json file for any other gene, simply replace the text that says "GENE" in the url with your desired gene name. (this code won't work as it's generic)
curl 'http://rest.ensembl.org/lookup/symbol/saccharomyces_cerevisiae/GENE?' -H 'Content-type:application/json'
From there you could put the id retrieved from the json file into the url where it says "ID" in uppercase, but the json file above also returns all the relevant information already.
curl 'http://rest.ensembl.org/lookup/id/ID?expand=1' -H 'Content-type:application/json'

= Acknowledgements =
Nicole Kalcic was my partner and we met up once and worked over text message. I also worked with Zach Van Ysseldyk and Antoni Porras on the Web API section and we figured out how to return the desired deliverable together.
While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 15:36, 30 October 2017 (PDT)
{{Template:ArashLari}}

ArashLari Week 9

2017-10-31T06:03:54Z

ArashLari: /* Ensemble URL */ updated deliverable

= Electronic Workbook =
For the purposes of testing I used the following files:
*21-genes_31-edges_Schade-data_estimation_output.xlsx
*21-genes_31-edges_Schade-data_estimation_output.sif
*21-genes_31-edges_Schade-data_estimation_output.graphml
Then proceed to the GRNSight-beta from the link provided in the instructions for this assignment
== Test 1 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 2 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 3 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 4 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 5 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 6 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 7 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 8 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 9 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 10 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 11 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 12 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 13 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 14 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 15 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 16 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 17 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 18 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Observations ==
While they all passed, it should be noted that for tests 7 through 12, the user can technically see all the numbers, but sometimes they might overlap and become hard to read.
This can be seen in the following image:

[[File:ArashObservation.png]]

= Web Service API Exploration =

==Deliverable==
*Note: to find the json file for any other gene, simply replace the text that says "ACT1" in the url with your desired gene name.
curl 'http://rest.ensembl.org/lookup/symbol/saccharomyces_cerevisiae/ACT1?' -H 'Content-type:application/json'

= Acknowledgements =
Nicole Kalcic was my partner and we met up once and worked over text message. I also worked with Zach Van Ysseldyk and Antoni Porras on the Web API section and we figured out how to return the desired deliverable together.
While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 15:36, 30 October 2017 (PDT)
{{Template:ArashLari}}

ArashLari Week 9

2017-10-31T06:01:35Z

ArashLari: /* Acknowledgements */ added them

= Electronic Workbook =
For the purposes of testing I used the following files:
*21-genes_31-edges_Schade-data_estimation_output.xlsx
*21-genes_31-edges_Schade-data_estimation_output.sif
*21-genes_31-edges_Schade-data_estimation_output.graphml
Then proceed to the GRNSight-beta from the link provided in the instructions for this assignment
== Test 1 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 2 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 3 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 4 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 5 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 6 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 7 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 8 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 9 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 10 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 11 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 12 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 13 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 14 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 15 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 16 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 17 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 18 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Observations ==
While they all passed, it should be noted that for tests 7 through 12, the user can technically see all the numbers, but sometimes they might overlap and become hard to read.
This can be seen in the following image:

[[File:ArashObservation.png]]

= Ensemble URL =
curl 'http://rest.ensembl.org/lookup/symbol/saccharomyces_cerevisiae/ACT1?' -H 'Content-type:application/json'

= Acknowledgements =
Nicole Kalcic was my partner and we met up once and worked over text message. I also worked with Zach Van Ysseldyk and Antoni Porras on the Web API section and we figured out how to return the desired deliverable together.
While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 15:36, 30 October 2017 (PDT)
{{Template:ArashLari}}

ArashLari Week 9

2017-10-31T05:59:51Z

ArashLari: /* Observations */ uploaded image

= Electronic Workbook =
For the purposes of testing I used the following files:
*21-genes_31-edges_Schade-data_estimation_output.xlsx
*21-genes_31-edges_Schade-data_estimation_output.sif
*21-genes_31-edges_Schade-data_estimation_output.graphml
Then proceed to the GRNSight-beta from the link provided in the instructions for this assignment
== Test 1 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 2 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 3 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 4 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 5 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 6 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Show With Mouse Over"
Result: '''PASSED'''
== Test 7 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 8 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 9 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 10 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 11 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 12 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Always Show Edge Weights"
Result: '''PASSED'''
== Test 13 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 14 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 15 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Check
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 16 ==
Instructions:
*Load Graph - File Menu -> Open
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 17 ==
Instructions:
*Load Graph - File Menu -> Import SIF
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Test 18 ==
Instructions:
*Load Graph - File Menu -> Import GraphML
*Restrict Graph to Viewport - Uncheck
*Hide/Show Edge Weights - Select "Never Show Edge Weights"
Result: '''PASSED'''
== Observations ==
While they all passed, it should be noted that for tests 7 through 12, the user can technically see all the numbers, but sometimes they might overlap and become hard to read.
This can be seen in the following image:

[[File:ArashObservation.png]]

= Ensemble URL =
curl 'http://rest.ensembl.org/lookup/symbol/saccharomyces_cerevisiae/ACT1?' -H 'Content-type:application/json'

= Acknowledgements =

[[User:ArashLari|ArashLari]] ([[User talk:ArashLari|talk]]) 15:36, 30 October 2017 (PDT)
{{Template:ArashLari}}

File:ArashObservation.png

2017-10-31T05:58:51Z

ArashLari: An observation i made during testing

An observation i made during testing