Lenaolufson Week 15

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12/8/15

  • It was now time for me to prepare my file for GenMAPP, and I did so by the Vibrio cholerae instructions found here.]
  • I inserted a new worksheet and named it "forGenMAPP".
  • I went back to the "statistics" worksheet and Selected All and Copied.
  • I went to my new sheet and clicked on cell A1 and selected Paste Special, clicked on the Values radio button, and clicked OK.
    • I then deleted the ID columns besides the far left one in column A, and I deleted the second MasterIndex column because it was unnecessary.
    • I added a "1" before all of the titles of columns D through I so that none of the columns would have the same names due to the replicates.
  • I selected Columns V through Y (all the fold changes). I selected the menu item Format > Cells. Under the number tab, I selected 2 decimal places. I clicked OK.
  • I selected all the columns containing p values. I selected the menu item Format > Cells. Under the number tab, I selected 4 decimal places. I clicked OK.
  • I deleted the left-most Bonferroni p value column, preserving the one that showed the result of my "if" statement.
  • I inserted a column to the right of the "ID" column. I typed the header "SystemCode" into the top cell of this column. I filled the entire column (each cell) with the letter "N".
  • I selected the menu item File > Save As, and chose "Text (Tab-delimited) (*.txt)" from the file type drop-down menu.
  • After preparing it for GenMAPP, here are the .xls and .txt files:
  • Then it was time to perform a sanity check, which was done using the Vibrio cholerae instructions found here.]
  • I opened my spreadsheet and went to the "forGenMAPP" tab.
  • I clicked on cell A1 and selected the menu item Data > Filter > Autofilter. Little drop-down arrows appeared at the top of each column. This enabled me to filter the data according to criteria I set.
  • I clicked on the drop-down arrow on my "Pvalue" column. I selected "Custom". In the window that appeared, I set a criterion that filtered my data so that the Pvalue was less than 0.05.
    • p-value less than 0.05: 1923/3552, 54%
    • p-value less than 0.01: 1028/3552, 29%
    • p-value less than 0.001: 242/3552, 7%
    • p-value less than 0.0001: 40/3552, 1%
    • p < 0.05 for the Bonferroni-corrected p value: 9/3552, 0.2%
    • p < 0.05 for the Benjamini and Hochberg-corrected p value: 1365/3552, 38%
  • Keeping the (unadjusted) "Pvalue" filter at p < 0.05, I filtered the "Avg_ABC_Samples" column to show all genes with an average log fold change greater than zero.
    • 964/3552, 27%
  • Keeping the (unadjusted) "Pvalue" filter at p < 0.05, I filtered the "Avg_ABC_Samples" column to show all genes with an average log fold change less than zero.
    • 959/3552, 27%
  • With an average log fold change of > 0.25 and p < 0.05
    • 874/3552, 25%
  • With an average log fold change of < -0.25 and p < 0.05
    • 848/3552, 24%
  • the fold change cut-off of greater than 0.25 or less than -0.25 and the unadjusted p value cut off of p < 0.05
    • 1722/3552, 48%
  • I then was ready to run my .txt file in GenMAPP.
  • I downloaded the .gdb file from my team page [[1]] so that I would have it to run GenMAPP with.
  • I opened the Expression Dataset Manger from the Data drop-down list in GenMAPP.
  • I selected New Dataset from the Expression Datasets menu and choose the tab-delimited text file formatted for GenMAPP (.txt).
  • Upon specifying that all data was numerical, the Expression Dataset Manager converted my data to .gex file. This process took approximately one minute to complete. In addition to converting the data to a .gex file, an exceptions file (.EX.txt) was also produced, as 342 errors were reportedly detected in the raw data.
    • However, there was a problem at this point because the data set had a few mistakes in it.
  • I went back to my data sheet and with the help of Dr. Dahlquist, we discovered that some of the values were incorrect as they displayed: #DIV/0!
    • We then replaced all of the #DIV/0! cells with blank cells.
      • 23 replacements for the #DIV/0!
  • I then saved and exported this new .txt file and ran it through GenMAPP again.
  • This resulted in fewer errors and everything was smooth.
  • I customized the new Expression Dataset by creating a Color Sets= with instructions to GenMAPP for displaying data on MAPPs. The new Color Set was entitled "LogFoldChange".
    • First, I created a criterion for this color set to label genes that demonstrated a significant increase in their expression.
      • I specified the Gene value as "Avg_ABC_Samples" for the Vibrio dataset.
      • I activated the Criteria Builder by clicking the New button and named the criterion "Increased".
      • I selected the color for this criterion using the color box.
      • I stated the criterion as follows and added it to the Criteria List: [Avg_ABC_Samples] > 0.25 AND [Pvalue] < 0.05
    • Second, I created a criterion for this color set to label genes that demonstrated a significant decrease in their expression.
      • I specified the Gene value as "Avg_ABC_Samplesl" for the Vibrio dataset.
      • I activated the Criteria Builder by clicking the New button and named the criterion "Decreased".
      • I selected the color for this criterion using the color box.
      • I stated the criterion as follows and added it to the Criteria List: [Avg_ABC_Samples] < -0.25 AND [Pvalue] < 0.05
  • Upon entering these color sets, I savedthe entire Expression Dataset by selecting Save from the Expression Dataset menu.
  • links to files created:

12/13/15

  • The above steps were repeated due to the creation of a new .gdb file by the Coder. Once downloading the bpertussis-std_cw20151210.gdb file, I obtained the new .txt file and then prepared to import into GenMAPP.

bpertussis-std_cw20151210.gdb Use in GenMAPP

The following analysis was conducted in GenMAPP Version 2.1. Within GenMAPP, the Bordetella pertussis gene database was loaded by selecting Data > Choose Gene Database and then selecting the file bpertussis-std_cw20151210.gdb.

Putting a Gene on the MAPP Using the GeneFinder Window

I made a sample MAPP in which gene IDs conforming to the naming conventions of the 5 major gene databases containing Bordetella pertussis genome data were added. A screenshot of the resulting MAPP is provided below: Samplegenemapp.png

  • Gene IDs:
    • bp1123 refers to the OrderedLocusNames gene ID system.
    • CAE43716 refers to the EmsemblBacteria gene ID system.
    • Q7VWE5 refers to the UniProt gene ID system.
    • 2665491 refers to the GeneID system.
    • NP_881255 refers to the RefSeq gene ID system.

Note: Gene IDs tested from the above gene ID systems all had complete Backpages and were successfully placed on the MAPP.

Creating an Expression Dataset in the Expression Dataset Manager

The file [[File:Bpertussis compiledrawdata cw20151208.txt]] was used to create an expression dataset in GenMAPP.

  • Total Number of Gene IDs Imported
    • 3211 of the 3552 gene IDs from the microarray dataset were imported into the expression dataset.
    • There were 341 exceptions during the creation of the expression dataset. A screenshot of the error message is shown here:
      • Errors in genmapp.png

Coloring a MAPP with Expression Data

Creating a New Color Set

  1. I customized the new Expression Dataset by creating a new color set entitled "LogFoldChange".
  2. I created a criterion for this color set to label genes that demonstrated a significant increase in their expression.
    • I specified the gene value as "Avg_ABC_Samples" for the Bordetella pertussis microarray dataset.
    • I activated the Criteria Builder by clicking the New button and named the criterion "Increased".
    • I selected the color for this criterion as red using the color box.
    • I stated the criterion as follows and added it to the Criteria List: [Avg_ABC_Samples] > 0.25 AND [Pvalue] < 0.05.
  3. Second, I created a criterion for this color set to label genes that demonstrated a significant decrease in their expression.
    • I specified the gene value as "Avg_ABC_Samples" for the Bordetella pertussis microarray dataset.
    • I activated the Criteria Builder by clicking the New button and named the criterion "Decreased".
    • I selected the color for this criterion as green using the color box.
    • I stated the criterion as follows and added it to the Criteria List: [Avg_ABC_Samples] < -0.25 AND [Pvalue] < 0.05
  4. Upon entering these color sets, I saved the entire Expression Dataset by selecting Save from the Expression Dataset menu. This effectively updated my .gex file with the new Color Set.

Screenshot of Color Set criteria:

  • Expressioncolorset.png

Note: No errors were encountered in the creation of the Color Set.

Creating a Pathway-Based MAPP Using Colored Genes

Ribosome Kegg Pathway

  • I was able to create a mapp of the ribosome pathway by using the genes provided from the http://www.genome.jp/kegg/ website.
    • Once accessing the website, I selected KEGG PATHWAY from the main page.
    • Next, I scrolled down to "Ribosome" that was under section 2.2 Translation and selected it.
    • Then, I searched my organism in the drop down menu at the top of the page, and I selected the Bordetella pertussis Tomaha I organism, and clicked "Go".
    • This lead me to a page of the ribosome pathway with the gene IDs that pertained to my specific organism. I then was able to create a mapp using these genes in GenMAPP.
    • Each of the green highlighted genes on the ribosome pathway were entered into the GenMAPP mapp by entering each gene ID and the name given from the Kegg pathway, and then the expression dataset "bpertussis_expressiondataset_cw20151213" was applied to the genes to color code them.
    • Here is the screenshot of the final mapp for the ribosome pathway created:
  • RibosomeGenMAPP.png
    • Most of the ribosome genes that were generated on this mapp appeared to be the color green, symbolizing a decrease, except for the grey colored genes that were not significantly changed in this experiment. Since the genes mapped for the ribosome pathway all appeared to be green, this means that the expression levels of the genes pertaining to the ribosome category all decreased during the microarray experiment. Ribosomes play a key role in the translation process in cells and without them genes are often repressed and unable to perform their proper functions as they are unable to complete the replication processes. The microarray experiment analysis revealed that the absence of a membrane-associated protein named KpsT in B. pertussis, resulted in global down-regulation of gene expression including key virulence genes. The ribosome pathway depicted genes that were decreasing in gene expression, thus linking the translation process to the down-regulated key genes from the experiment because since these genes were lacking a necessary protein to help them perform the proper replication processes, translation did not occur in these genes and thus the ribosomes were not involved, ultimately leading to the decrease in expression of the genes mapped in the ribosome pathway.

Nitrogen Cycle Kegg Pathway

  • I was also able to create another mapp using the nitrogen cycle pathway genes provided from the http://www.genome.jp/kegg/ website.
    • Once accessing the website, I selected KEGG PATHWAY from the main page.
    • Next, I scrolled down to "Nitrogen Metabolism" that was under section 1.2 Energy Metabolism and selected it.
    • Then, I searched my organism in the drop down menu at the top of the page, and I selected the Bordetella pertussis Tomaha I organism, and clicked "Go".
    • This lead me to a page of the nitrogen metabolism pathway with the gene IDs that pertained to my specific organism. I was then able to create a mapp using these genes in GenMAPP.
    • Each of the green highlighted genes on the nitrogen metabolism pathway were entered into the GenMAPP mapp by entering each gene ID and the name given from the Kegg pathway, and then the expression dataset "bpertussis_expressiondataset_cw20151213" was applied to the genes to color code them.
    • Here is the screenshot of the final mapp for the nitrogen cycle pathway created:
  • NitrogencycleGenMAPP.png
    • This mapp displayed both red and green colored genes; the green highlighted genes symbolizing a decrease and the red highlighted genes symbolizing an increase, as well a couple of gray genes that were not significant to the criterion. This nitrogen cycle mapp was created due to the important metabolic processes that occur in order to keep cells alive and reproducing, and specifically the nitrogen metabolism cycle. The genes that displayed red in this mapp had increased expression during the microarray experiment, and from the kegg pathway given for nitrogen metabolism, these genes can be seen to specifically aid in the metabolism of glutamate. Glutamate is important to cells as it plays a role in providing energy to allow the cells to operate correctly, and since the glutamate-related genes that we mapped were increased, it can be determined that glutamate plays a role in supplying the underlying energy to allow for the Bordetella pertussis strains to produce the polysaccharide capsule transport proteins, as studied in the microarray experiment.

Running MAPPFinder

  • MAPPFinder Procedure
    • I launched the MAPPFinder program from within GenMAPP and ensured that the bpertussis-std_cw20151210.gdb gene database was still loaded into GenMAPP.
    • I clicked on the button "Calculate New Results" followed by "Find File", at which point I specified the .gex file updated during the creation of the "LogFoldChange" color set.
    • I chose to apply both the "Increased" and "Decreased" criteria present within the LogFoldChange color set to the data.
    • I checked the boxes next to "Gene Ontology" and "p value", specified the results file, and then clicked "Run MAPPFinder".
      • This analysis took several minutes to complete.
  • MAPPFinder Analysis Results
    • I selected "Show Ranked List" to see a list of the most significant Gene Ontology terms. A screenshot of this output is shown below:
    • Mappfinderrankedlist.png
      • The majority of the most significant gene ontology terms pertained to ribosome biosynthesis and translation.

Note: The MAPPFinder analysis took approximately 8 minutes to complete. No errors were encountered in the process. MAPPFinder thus was confirmed to work with the Bordetella pertussis gene database.


new jpeg of ribosome mapp: Bpertussis ribosomepathway cw20151215.jpg

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