Dramir36 Week 9 - Revision history

Dramir36: /* Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes (Tuesday, October 29) */

2019-10-29T22:54:04Z

‎Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes (Tuesday, October 29)

Dramir36: /* Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes (Tuesday, October 29) */answered question

2019-10-29T22:53:39Z

‎Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes (Tuesday, October 29): answered question

Dramir36: /* Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes (Tuesday, October 29) */removed ZAP1

2019-10-29T22:47:15Z

‎Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes (Tuesday, October 29): removed ZAP1

Dramir36: /* Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes (Tuesday, October 29) */ answered a question

2019-10-29T22:45:25Z

‎Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes (Tuesday, October 29): answered a question

Dramir36: added more Week 9 methods

2019-10-29T22:31:44Z

added more Week 9 methods

Dramir36: /* Data/Files */ updated master file

2019-10-29T22:19:37Z

‎Data/Files: updated master file

Dramir36: /* Data/Files */ updated PowerPoint document

2019-10-29T02:54:32Z

‎Data/Files: updated PowerPoint document

Dramir36: /* References */ fixed link

2019-10-29T02:39:17Z

‎References: fixed link

Dramir36: /* References */ added references

2019-10-29T02:38:58Z

‎References: added references

Dramir36: /* Clustering and GO Term Enrichment with stem (part 2) */ answered questions

2019-10-29T02:34:01Z

‎Clustering and GO Term Enrichment with stem (part 2): answered questions

@@ Line 245: / Line 245: @@
 #** '''''Are CIN5, GLN3, and/or HAP4 on the list?  If so, what is their "% in user set", "% in YEASTRACT", and "p value".'''''
 #**:CIN5 has a 0.2125% in user set, 0.028% in YEASTRACT, and a 0.999790859 p value. GLN3 has a 0.3624%in user set, 0.0432% in YEASTRACT, and a 0.167633128 p value. HAP4 has a 0.1986% in user set, 0.0522% in YEASTRACT, and a 0.013392287 p value.
 # For the mathematical model that we will build, we need to define a ''gene regulatory network'' of transcription factors that regulate other transcription factors.  We can use YEASTRACT to assist us with creating the network.  We want to generate a network with approximately 15-20 transcription factors in it.
 #* You need to select from this list of "significant" transcription factors, which ones you will use to run the model.  You will use these transcription factors and add GLN3, HAP4, and CIN5 if they are not in your list.  Explain in your electronic notebook how you decided on which transcription factors to include.  Record the list and your justification in your electronic lab notebook.  Each group member will select a different network (they can have some overlapping transcription factors, but some should also be different).

@@ Line 244: / Line 244: @@
 #** '''''Upload the Excel file to the wiki and link to it in your electronic lab notebook.'''''
 #** '''''Are CIN5, GLN3, and/or HAP4 on the list?  If so, what is their "% in user set", "% in YEASTRACT", and "p value".'''''
 # For the mathematical model that we will build, we need to define a ''gene regulatory network'' of transcription factors that regulate other transcription factors.  We can use YEASTRACT to assist us with creating the network.  We want to generate a network with approximately 15-20 transcription factors in it.
 #* You need to select from this list of "significant" transcription factors, which ones you will use to run the model.  You will use these transcription factors and add GLN3, HAP4, and CIN5 if they are not in your list.  Explain in your electronic notebook how you decided on which transcription factors to include.  Record the list and your justification in your electronic lab notebook.  Each group member will select a different network (they can have some overlapping transcription factors, but some should also be different).

@@ Line 245: / Line 245: @@
 #** '''''Are CIN5, GLN3, and/or HAP4 on the list?  If so, what is their "% in user set", "% in YEASTRACT", and "p value".'''''
 # For the mathematical model that we will build, we need to define a ''gene regulatory network'' of transcription factors that regulate other transcription factors.  We can use YEASTRACT to assist us with creating the network.  We want to generate a network with approximately 15-20 transcription factors in it.
-#* You need to select from this list of "significant" transcription factors, which ones you will use to run the model.  You will use these transcription factors and add GLN3, HAP4, and ZAP1 if they are not in your list.  Explain in your electronic notebook how you decided on which transcription factors to include.  Record the list and your justification in your electronic lab notebook.  Each group member will select a different network (they can have some overlapping transcription factors, but some should also be different).
+#* You need to select from this list of "significant" transcription factors, which ones you will use to run the model.  You will use these transcription factors and add GLN3, HAP4, and CIN5 if they are not in your list.  Explain in your electronic notebook how you decided on which transcription factors to include.  Record the list and your justification in your electronic lab notebook.  Each group member will select a different network (they can have some overlapping transcription factors, but some should also be different).
 #* Go back to the YEASTRACT database and follow the link to ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]''.
-#* Copy and paste the list of transcription factors you identified (plus HAP4, GLN3, and ZAP1) into both the "Transcription factors" field and the "Target ORF/Genes" field.
+#* Copy and paste the list of transcription factors you identified (plus HAP4, GLN3, and CIN5) into both the "Transcription factors" field and the "Target ORF/Genes" field.
 #* We are going to use the "Regulations Filter" options of "Documented", "'''Only''' DNA binding evidence"
 #** Click the "Generate" button.

@@ Line 238: / Line 238: @@
 #* Click the ''Search'' button.
 # Answer the following questions:
-#* In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant".  '''''How many transcription factors are green or "significant"?'''''
+#* In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant".
 #* '''''Copy the table of results from the web page and paste it into a new Excel workbook to preserve the results.'''''
 #** '''''Upload the Excel file to the wiki and link to it in your electronic lab notebook.'''''

@@ Line 223: / Line 223: @@
 ##** 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].
 ==Data/Files==

@@ Line 226: / Line 226: @@
 ==Data/Files==
-*[[Media:BIOL367 F19 microarray-data dHAP4 DR.xlsx|Dramir36 dHAP4 Microarray data]]
+*[[Media:BIOL367 F19 microarray-data dHAP4 DR most updated.xlsx|Dramir36 dHAP4 Master Excel Data File]]
 *[[Media:BIOL367 F19 dHAP4 DR data powerpoint.pptx|Dramir36 dHAP4 data slides]]
 *[[Media:DR dHAP4 profile genelist.zip|Profile dHAP4 Gene Tables Dramir36]]

← Older revision		Revision as of 02:39, 29 October 2019
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	*LMU BioDB 2019. (2019). Week 9. Retrieved October 28, 2019, from https://xmlpipedb.cs.lmu.edu/biodb/fall2019/index.php/Week_9		*LMU BioDB 2019. (2019). Week 9. Retrieved October 28, 2019, from https://xmlpipedb.cs.lmu.edu/biodb/fall2019/index.php/Week_9

−	*Gene Ontology Consortium. (2019) Gene Ontology. Retrieved October 28, 2019, from	+	*Gene Ontology Consortium. (2019) Gene Ontology. Retrieved October 28, 2019, from http://geneontology.org/
−	http://geneontology.org/

← Older revision		Revision as of 02:38, 29 October 2019
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	==References==		==References==
		+	*LMU BioDB 2019. (2019). Week 9. Retrieved October 28, 2019, from https://xmlpipedb.cs.lmu.edu/biodb/fall2019/index.php/Week_9
		+
		+	*Gene Ontology Consortium. (2019) Gene Ontology. Retrieved October 28, 2019, from
		+	http://geneontology.org/

@@ Line 209: / Line 209: @@
 ##*:The p value is equal to 1.3E-114, which is significant.
 ##*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?'''''
+##* 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.
 ##* 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 research presentation.  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.