Sarah Carratt: Week 12: Difference between revisions

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==Instructions==
=STEM=
=== Clustering and Gene Ontology Analysis with STEM ===


For this assignment, keep an electronic lab notebook recording all of the actions that you take following the protocol.  In addition, answer the questions embedded in the protocol.
==Preparing to Use STEM==


# '''Begin by downloading and extracting the STEM software.'''  [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#First, I downloaded the software and registered with the website. [http://www.andrew.cmu.edu/user/zivbj/stemreg.html STEM]
#* 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.
#After unziping the file (7-zip > Extract Here), I launched the program using the command window.
#* Unzip the file.  In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
##To do this, I went to the start menu and clicked ''Programs > Accessories > Command Prompt''.
#* This will create a folder called <code>stem</code>.  Inside the folder, double-click on the <code>stem.cmd</code> to launch the STEM program.
##Then I entered the following commands in the window that appeared:  
#** In Seaver 120, we encountered an issue where the program would not launch on the Windows XP machines due to a lack of memory.  To get around this problem, launch STEM from the command line.
###<code>cd Desktop\stem</code>
#*** Go to the start menu and click on ''Programs > Accessories > Command Prompt''.
###<code>java -mx512M -jar stem.jar -d defaults.txt</code>   
#*** You will need to navigate to the directory (folder) in which the STEM program resides.  If you followed the instructions above and extracted the stem folder to the Desktop, type the following: <code>cd Desktop\stem</code> and press "Enter".
#*** To launch the program then type:  <code>java -mx512M -jar stem.jar -d defaults.txt</code>  and press "Enter".  This will launch the program with less memory allocated to it.
# '''Prepare your microarray data file for loading into STEM.'''
#* Using the Excel spreadsheet that you turned in for your [[BIOL398-01/S11:Week 11 | Week 11 Assinment]], insert a new worksheet and name it "stem".
#* Select all of the data from your "final" worksheet and paste it into your "stem" worksheet.
#** Your leftmost column should have the column header "MasterIndex".  Rename this column to "SPOT".  Column B should be named "ID".  Rename this column to "Gene Symbol".
#** Delete all of the data columns '''''EXCEPT''''' for the AvgLogFC columns for each timepoint.
#** Rename the data columns with just the time and units.  Use the same timepoints for all samples.  For example, 15m, 30m, etc. for the Dahlquist lab data or 0.17h, 0.5h, etc. for the Schade data (for the Schade data, leave out the 0 timepoint).
#** 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.
# '''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 new PowerPoint presentation to save your figures.
## Click on each of the profiles 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 this file to [http://lionshare.lmu.edu LionShare] and provide a link to Dr. Dahlquist and Dr. Fitzpatrick.
##* For each of the 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" or "Schade" 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 this file to [http://lionshare.lmu.edu LionShare] and provide a link to Dr. Dahlquist and Dr. Fitzpatrick.
# '''Analyzing and Interpreting STEM Results'''
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data.  We suggest that you choose one that has a pattern of up- or down-regulated genes at the early (first three) timepoints.  Answer the following:
##* '''''Why did you select this profile?  In other words, why was it intersting to you?'''''
###9, mostly down regulated and never up-regulated
##* '''''How many genes belong to this profile?'''''
###221 genes assigned
##* '''''How many genes were expected to belong to this profile?'''''
###55.9 (56) genes expected
##* '''''What is the p value for the enrichment of genes in this profile?'''''  Bear in mind that in [[BIOL398-01/S11:Week 11 | last week's assignment]], you computed 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.
###1.5E-65 (significant)
##* 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 10 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).  '''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org].  Write a paragraph that describes 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?'''''


=== Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes ===
==Preparing the Spreadsheet==
#I opened my master spreadsheet and inserted a new worksheet and named it "stem".
#I copied over data from the "final" worksheet to the "stem" worksheet.
#Renamed the columns: "MasterIndex"→ "SPOT" and "ID"→ "Gene Symbol"
#Deleted all of the data columns except AvgLogFC columns.
#Renamed the data columns with time and units for simplicity.
#'Save as Text (Tab-delimited) (*.txt).


In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time.  The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors.  We will explore this using the YEASTRACT database.
==Running STEM==
#Expression Data Info: selected my file (no normalization/add 0 and Spot IDs included in the data file)
#Gene Info: 'Saccharomyces cerevisiae (SGD) with no cross references'and no gene locations
#Options: STEM Clustering Method was selected, no changes
#Execute: Run the program


# Open the gene list in Excel for the profile/cluster that you analyzed above.
==Viewing and Saving STEM Results==
#* Copy the list of gene IDs onto your clipboard.
#Changed to "Based on real time" from Interface Options and took a screenshot of this window (saved to powerpoint)
# Launch a web browser and go to the [http://www.yeastract.com/ YEASTRACT database].
#Opened detailed plots of each profile and took individual screenshots (saved to powerpoint)
#* On the left panel of the window, click on the link to [http://www.yeastract.com/formgroupbytf.php ''group by TF''].
#"Profile Gene Table" and "Profile GO Table":  saved tables and uploaded to lionshare with correct names
#* Paste your list of genes from your cluster into the box labeled ''ORFs/Genes''.
#* Check the box for ''Check for all TFs''.
#* Uncheck the box for ''Indirect Evidence''.
#* Click the ''Search'' button.
# Answer the following questions:
#* '''''What are the top 10 transcription factors in your results? List them on your wiki page with the percent of the genes in your cluster that they each regulate.'''''
**Ste12p 31.7, Rap1p 21.6, Fhl1p 12.8, Sok2p 12.8, Yap6p 9.7, Ino4p 9.4, Sko1p 9.0, Cin5p 8.9, Skn7p 8.8, Phd1p 8.2
#* '''''Is Gln3 on the list?  What percentage of the genes in the cluster does it regulate?  How many genes does it regulate?  What are the names of the genes?'''''
**Gln3p 2.5, 143
**ADH7 ASN1 CAR1 CPS1 DAL1 DAL2 DAL3 DAL4 DAL5 DAL7 DAL80 DCG1 DUR3 ENA1 GAP1 GAT1 GCN4 GDH2 GLN1 GLT1 LAP4 MEP2 PET130 PMA1 PUT1 RAD28 RPA34 UGA1 UGA3 UGA4 VAB2 VPS27 MIC14 YEA4 YEL008w YHI9 YOR102w CLN3 EXO84 SIF2 HIS4 VAC17 CHA1 MAK32 YCR064c HCM1 IDP1 COX9 GGC1 RSM10 SED1 YDR090c YDR210w YDR278c GNP1 SMT3 SPS2 YEL007w NOP16 TMT1 ECM32 YER189w YRF1-2 FRS2 ERG26 YGL007w FZF1 ZRT1 QCR9 YHB1 RPS20 WSC4 OCA5 ERG11 ARG4 SLT2 COX6 EGD2 MDM31 RPI1 IST3 MGA2 CTK2 ARG3 RFA3 CPA2 YMR1 BAT2 YKR040c UTH1 SRP40 PTR2 SDC25 PAU17 IRC19 RIX7 IZH3 FRE8 RPS0b ZRT2 ACS2 YLR257w MRPL4 SPO20 VBA1 MRPL24 AAH1 YNL143c AIM38 YNR068c ARG1 ZEO1 MCH4 YOL159c WTM1 SFG1 YPL056c DIP5 YPR038w YPR039w TIP41 OPT2 GLN3 ARP7 YAR053w YJL022w PAU3 PLP1 RAS1 CTK1 SWM2 UBX3 VPS74 YOL160w GEF1 FEN2 DPL1 SPS1 AIM25 LHS1 ROG3 RRG1 COY1 TMA20 ATC1 POL31 NMD3 AIM6 NFU1 FLO10 TRS33 RBG2 YGR026w
# For the mathematical model that we will build in class, 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.  The model that we will start with has the following transcription factors in it:
CIN5
CUP9
FHL1
GTS1
HSF1
MSN1
MSN4
NRG1
RAP1
RCS1
REB1
ROX1
RPH1
YAP1
YAP6


* We will also include GLN3 because it is known to regulate the genes that code for enzymes in nitrogen metabolism. Based on your previous analysis of the transcription factors that regulate your chosen cluster above, select up to five additional transcription factors to add to the network. '''''Which transcription factors do you want to add to the model and why?'''''
==Analyzing and Interpreting STEM Results==
* Go back to the YEASTRACT database and follow the link to ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]''.
#Selected profile 9 for further interpretation, which was mostly down regulated and never up-regulated. I chose this gene because I find down-regulated genes easier to understand and explain. It also followed a simple pattern that I knew I would be able to put into context.
** Copy and paste the list above, plus GLN3, plus the additional transcription factors you identified into both the "Transcription Factor" field and the "ORF/Genes" field.
#221 genes were assigned to this profile.
** Uncheck the box for "Indirect Evidence" and select "JPEG" from the drop-down menu for the "Output Image".
#55.9 (56) genes were expected to be assigned to this profile.
** Click the "Generate" button.
#The p value is 1.5E-65 (significant).
* In the results window that appears, click on the link to the "RegulationMatrix" file that appears and save it to your Desktop.
#Opened the GO list and selected the third row. From the menu, I clicked Data > Filter > Autofilter.
* Click on the "Image" link to see the diagram of the network.  Save the image file (you can copy and paste it to your PowerPoint file or upload it to the wiki).
#Looked at terms with p value of < 0.05: 38 terms
* We will use this matrix file as an input to your model next week.
#Looked at corrected values with p value of < 0.05: 2 terms
* Make sure that your wiki assignment page includes your PowerPoint file to which you saved your screenshots and your "RegulationMatrix" file.


== Online Sources ==
==Definitions==


* [http://www.biology-online.org/dictionary/Main_Page/ Biology Dictionary]
#'''GO:0005737 cytoplasm''': All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures.
#'''GO:0044445 cytosolic part''': Any constituent part of cytosol, that part of the cytoplasm that does not contain membranous or particulate subcellular components.
#'''GO:0005829 cytosol''': The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes.  
#'''GO:0005622 intracellular''': The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm.
#'''GO:0044424 intracellular part''': The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm.
#'''GO:0008483 transaminase activity''': Catalysis of the transfer of an amino group to an acceptor, usually a 2-oxo acid.
#'''GO:0016769 transferase activity, transferring nitrogenous groups''': Catalysis of the transfer of a nitrogenous group from one compound (donor) to another (acceptor).
#'''GO:0034637 cellular carbohydrate biosynthetic process''': The chemical reactions and pathways resulting in the formation of carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y, carried out by individual cells.
#'''GO:0009063 cellular amino acid catabolic process''': The chemical reactions and pathways resulting in the breakdown of amino acids, organic acids containing one or more amino substituents.
#'''GO:0044444 cytoplasmic part''': Any constituent part of the cytoplasm, all of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures.  


==Student Response==
*SOURCE: [http://geneontology.org http://geneontology.org]
*The pattern for my profile was 0.0, -1.0, -2.0, -2.0, -1.0, 0.0. Of these most significant GO terms, most have to do with cytoplasm/cytosol. There are a couple for forming carbohydrates and breaking down the amino acids, but I was truly surprised to see that the cytoplasm/cytosol/intracellular parts would matter so much in this profile. To be honest, I still don't understand how these elements would be regulated, or for what purpose/advantage. I can only imagine that changing the interior makeup of the cell might affect the surface area to volume ratio and help a down regulated cell conserve heat and energy.


=YEASTRACT=
#Opened web window [http://www.yeastract.com/formgroupbytf.php].
#Opened gene list in Excel for profile 9.
#Copied the list of gene IDs into web box for ORFs/Genes.
#Checked the box for ''Check for all TFs''.
#Unchecked the box for ''Indirect Evidence''.
#Clicked the ''Search'' button.
#Top 10 transcription factors: Ste12p (36.2%), Rap1p (29.9%), Fhl1p (18.6%), Cin5p (14.0%), Phd1p (13.6%), Sok2p (13.1%), Yap6p (12.7%), Yap5p (11.3%), Skn7p (10.4%), Yap1p (9.5%).
#GLN3 is on the list, representing 3.2% and 7 genes: YDR210w, YEL007w, UGA1, CPS1, PUT1, ZEO1, WTM1.
#Transcription factors I used to general the matrix and diagram: CIN5, CUP9, FHL1, GTS1, HSF1, MSN1, MSN4, NRG1, RAP1, RCS1, REB1, ROX1, RPH1, YAP1, YAP6, GLN3, STE12, PHD1, SOK2, YAP5, SKN7
#I added the top five (non-overlapping) transcription factors to the list because I figured that if they represent such a large portion of regulated genes that they should be included in the map.
#Before I generated the figures, I unchecked the box for "Indirect Evidence" and selected "JPEG" from the drop-down menu for the "Output Image".
#Clicked "Generate"
#Saved RegulationMatrix to Lionshare.
#Clicked on the "Image" link to see the diagram of the network.
#Pasted image into my PowerPoint file and uploaded to Lionshare.
=Uploaded Files=
[[Media:screenshots.ppt|Screenshots for Week 12]]
[[Media:RegulationMatrix_Sarah.ppt|Regulation Matrix for Dahlquist_wt Profile 9]]
=Navigation Guide=
==Individual Assignments==
{| style="width: 50em"
| [[Sarah Carratt: Week 2]]
| [[Sarah Carratt: Week 6]]
| [[Sarah Carratt: Week 11]]
|-
| [[Sarah Carratt: Week 3]]
| [[Sarah Carratt: Week 7]]
| [[Sarah Carratt: Week 12]]
|-
| [[Sarah Carratt: Week 4]]
| [[Sarah Carratt: Week 8]]
| [[Sarah Carratt: Week 13]]
|-
| [[Sarah Carratt: Week 5]]
| [[Sarah Carratt: Week 9]]
| [[Sarah Carratt: Week 14]]
|}
==Class Assignments==
{| style="width: 50em"
| [[BIOL398-01/S11:Class Journal Week 1|Shared Journal: Week 1]]
| [[BIOL398-01/S11:Class Journal Week 6|Shared Journal: Week 6]]
| [[BIOL398-01/S11:Class Journal Week 11|Shared Journal: Week 11]]
|-
| [[BIOL398-01/S11:Class Journal Week 2|Shared Journal: Week 2]]
| [[BIOL398-01/S11:Class Journal Week 7|Shared Journal: Week 7]]
| [[BIOL398-01/S11:Class Journal Week 12|Shared Journal: Week 12]]
|-
| [[BIOL398-01/S11:Class Journal Week 3|Shared Journal: Week 3]]
| [[BIOL398-01/S11:Class Journal Week 8|Shared Journal: Week 8]]
| [[BIOL398-01/S11:Class Journal Week 13|Shared Journal: Week 13]]
|-
| [[BIOL398-01/S11:Class Journal Week 4|Shared Journal: Week 4]]
| [[BIOL398-01/S11:Class Journal Week 9|Shared Journal: Week 9]]
| [[BIOL398-01/S11:Class Journal Week 14|Shared Journal: Week 14]]
|-
| [[BIOL398-01/S11:Class Journal Week 5|Shared Journal: Week 5]]
| [[BIOL398-01/S11:Class Journal Week 10|Shared Journal: Week 10]]
|}
==Class Notes==
{| style="width: 50em"
| [[Media:Biomath Notes 1.1.doc|Sarah Carratt_1.18.11]]
| [[Media:Biomath Notes 6.doc|Sarah Carratt_2.3.11]]
| [[Media:Biomath Notes 11.doc|Sarah Carratt_2.22.11]]
|-
| [[Media:Biomath Notes 2.doc|Sarah Carratt_1.20.11]]
| [[Media:Biomath Notes 7.doc|Sarah Carratt_2.8.11]]
| [[Media:Biomath Notes 12.doc|Sarah Carratt_2.24.11]]
|-
| [[Media:Biomath Notes 3.doc|Sarah Carratt_1.25.11]]
| [[Media:Biomath Notes 8.doc|Sarah Carratt_2.10.11]]
| [[Media:Biomath Notes 13.doc|Sarah Carratt_3.1.11]]
|-
| [[Media:Biomath Notes 4.doc|Sarah Carratt_1.27.11]]
| [[Media:Biomath Notes 9.doc|Sarah Carratt_2.15.11]]
| [[Media:Biomath Notes 14.doc|Sarah Carratt_3.3.11]]
|-
| [[Media:Biomath Notes 5.doc|Sarah Carratt_2.1.11]]
| [[Media:Biomath Notes 10.doc|Sarah Carratt_2.17.11]]
| [[Media:Biomath Notes 15.doc|Sarah Carratt_3.8.11]]
|}
==Internal Links==
{| style="width: 50em"
| [[BIOL398-01/S11|BIOL398-01/S11:Assignments]]
| [[BIOL398-01/S11:People]]
| [[User:Sarah Carratt|BIOL398-01/S11:Sarah Carratt]]
|}


{{Template:SarahCarratt}}


[[Category:BIOL398-01/S11]]
[[Category:BIOL398-01/S11]]
[[Category: Biomathematical Modeling]]
[[Category: Biomathematical Modeling]]

Latest revision as of 23:35, 11 April 2011

STEM

Preparing to Use STEM

  1. First, I downloaded the software and registered with the website. STEM
  2. After unziping the file (7-zip > Extract Here), I launched the program using the command window.
    1. To do this, I went to the start menu and clicked Programs > Accessories > Command Prompt.
    2. Then I entered the following commands in the window that appeared:
      1. cd Desktop\stem
      2. java -mx512M -jar stem.jar -d defaults.txt

Preparing the Spreadsheet

  1. I opened my master spreadsheet and inserted a new worksheet and named it "stem".
  2. I copied over data from the "final" worksheet to the "stem" worksheet.
  3. Renamed the columns: "MasterIndex"→ "SPOT" and "ID"→ "Gene Symbol"
  4. Deleted all of the data columns except AvgLogFC columns.
  5. Renamed the data columns with time and units for simplicity.
  6. 'Save as Text (Tab-delimited) (*.txt).

Running STEM

  1. Expression Data Info: selected my file (no normalization/add 0 and Spot IDs included in the data file)
  2. Gene Info: 'Saccharomyces cerevisiae (SGD) with no cross references'and no gene locations
  3. Options: STEM Clustering Method was selected, no changes
  4. Execute: Run the program

Viewing and Saving STEM Results

  1. Changed to "Based on real time" from Interface Options and took a screenshot of this window (saved to powerpoint)
  2. Opened detailed plots of each profile and took individual screenshots (saved to powerpoint)
  3. "Profile Gene Table" and "Profile GO Table": saved tables and uploaded to lionshare with correct names

Analyzing and Interpreting STEM Results

  1. Selected profile 9 for further interpretation, which was mostly down regulated and never up-regulated. I chose this gene because I find down-regulated genes easier to understand and explain. It also followed a simple pattern that I knew I would be able to put into context.
  2. 221 genes were assigned to this profile.
  3. 55.9 (56) genes were expected to be assigned to this profile.
  4. The p value is 1.5E-65 (significant).
  5. Opened the GO list and selected the third row. From the menu, I clicked Data > Filter > Autofilter.
  6. Looked at terms with p value of < 0.05: 38 terms
  7. Looked at corrected values with p value of < 0.05: 2 terms

Definitions

  1. GO:0005737 cytoplasm: All of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures.
  2. GO:0044445 cytosolic part: Any constituent part of cytosol, that part of the cytoplasm that does not contain membranous or particulate subcellular components.
  3. GO:0005829 cytosol: The part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes.
  4. GO:0005622 intracellular: The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm.
  5. GO:0044424 intracellular part: The living contents of a cell; the matter contained within (but not including) the plasma membrane, usually taken to exclude large vacuoles and masses of secretory or ingested material. In eukaryotes it includes the nucleus and cytoplasm.
  6. GO:0008483 transaminase activity: Catalysis of the transfer of an amino group to an acceptor, usually a 2-oxo acid.
  7. GO:0016769 transferase activity, transferring nitrogenous groups: Catalysis of the transfer of a nitrogenous group from one compound (donor) to another (acceptor).
  8. GO:0034637 cellular carbohydrate biosynthetic process: The chemical reactions and pathways resulting in the formation of carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y, carried out by individual cells.
  9. GO:0009063 cellular amino acid catabolic process: The chemical reactions and pathways resulting in the breakdown of amino acids, organic acids containing one or more amino substituents.
  10. GO:0044444 cytoplasmic part: Any constituent part of the cytoplasm, all of the contents of a cell excluding the plasma membrane and nucleus, but including other subcellular structures.
  • SOURCE: http://geneontology.org
  • The pattern for my profile was 0.0, -1.0, -2.0, -2.0, -1.0, 0.0. Of these most significant GO terms, most have to do with cytoplasm/cytosol. There are a couple for forming carbohydrates and breaking down the amino acids, but I was truly surprised to see that the cytoplasm/cytosol/intracellular parts would matter so much in this profile. To be honest, I still don't understand how these elements would be regulated, or for what purpose/advantage. I can only imagine that changing the interior makeup of the cell might affect the surface area to volume ratio and help a down regulated cell conserve heat and energy.

YEASTRACT

  1. Opened web window [1].
  2. Opened gene list in Excel for profile 9.
  3. Copied the list of gene IDs into web box for ORFs/Genes.
  4. Checked the box for Check for all TFs.
  5. Unchecked the box for Indirect Evidence.
  6. Clicked the Search button.
  7. Top 10 transcription factors: Ste12p (36.2%), Rap1p (29.9%), Fhl1p (18.6%), Cin5p (14.0%), Phd1p (13.6%), Sok2p (13.1%), Yap6p (12.7%), Yap5p (11.3%), Skn7p (10.4%), Yap1p (9.5%).
  8. GLN3 is on the list, representing 3.2% and 7 genes: YDR210w, YEL007w, UGA1, CPS1, PUT1, ZEO1, WTM1.
  9. Transcription factors I used to general the matrix and diagram: CIN5, CUP9, FHL1, GTS1, HSF1, MSN1, MSN4, NRG1, RAP1, RCS1, REB1, ROX1, RPH1, YAP1, YAP6, GLN3, STE12, PHD1, SOK2, YAP5, SKN7
  10. I added the top five (non-overlapping) transcription factors to the list because I figured that if they represent such a large portion of regulated genes that they should be included in the map.
  11. Before I generated the figures, I unchecked the box for "Indirect Evidence" and selected "JPEG" from the drop-down menu for the "Output Image".
  12. Clicked "Generate"
  13. Saved RegulationMatrix to Lionshare.
  14. Clicked on the "Image" link to see the diagram of the network.
  15. Pasted image into my PowerPoint file and uploaded to Lionshare.

Uploaded Files

Screenshots for Week 12

Regulation Matrix for Dahlquist_wt Profile 9

Navigation Guide

Individual Assignments

Sarah Carratt: Week 2 Sarah Carratt: Week 6 Sarah Carratt: Week 11
Sarah Carratt: Week 3 Sarah Carratt: Week 7 Sarah Carratt: Week 12
Sarah Carratt: Week 4 Sarah Carratt: Week 8 Sarah Carratt: Week 13
Sarah Carratt: Week 5 Sarah Carratt: Week 9 Sarah Carratt: Week 14

Class Assignments

Shared Journal: Week 1 Shared Journal: Week 6 Shared Journal: Week 11
Shared Journal: Week 2 Shared Journal: Week 7 Shared Journal: Week 12
Shared Journal: Week 3 Shared Journal: Week 8 Shared Journal: Week 13
Shared Journal: Week 4 Shared Journal: Week 9 Shared Journal: Week 14
Shared Journal: Week 5 Shared Journal: Week 10

Class Notes

Sarah Carratt_1.18.11 Sarah Carratt_2.3.11 Sarah Carratt_2.22.11
Sarah Carratt_1.20.11 Sarah Carratt_2.8.11 Sarah Carratt_2.24.11
Sarah Carratt_1.25.11 Sarah Carratt_2.10.11 Sarah Carratt_3.1.11
Sarah Carratt_1.27.11 Sarah Carratt_2.15.11 Sarah Carratt_3.3.11
Sarah Carratt_2.1.11 Sarah Carratt_2.17.11 Sarah Carratt_3.8.11

Internal Links

BIOL398-01/S11:Assignments BIOL398-01/S11:People BIOL398-01/S11:Sarah Carratt
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