Msaeedi23 Week 8

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10/15/15

  • Went toOpen Wet Ware and created an account. I then copied and pasted the text for the part 1 page onto my own and then edited it.

Before we begin...

  • The data from the Merrell et al. (2002) paper was accessed from this page at the Stanford Microarray Database.
  • The Log2 of R/G Normalized Ratio (Median) has been copied from the raw data files downloaded from the Stanford Microarray Database.
    • Patient A
      • Sample 1: 24047.xls (A1)
      • Sample 2: 24048.xls (A2)
      • Sample 3: 24213.xls (A3)
      • Sample 4: 24202.xls (A4)
    • Patient B
      • Sample 5: 24049.xls (B1)
      • Sample 6: 24050.xls (B2)
      • Sample 7: 24203.xls (B3)
      • Sample 8: 24204.xls (B4)
    • Patient C
      • Sample 9: 24053.xls (C1)
      • Sample 10: 24054.xls (C2)
      • Sample 11: 24205.xls (C3)
      • Sample 12: 24206.xls (C4)
    • Stationary Samples (We will not be using these, they are listed here for completeness, but do not appear in your compiled raw data file.)
      • Sample 13: 24059.xls (Stationary-1)
      • Sample 14: 24060.xls (Stationary-2)
      • Sample 15: 24211.xls (Stationary-3)
      • Sample 16: 24212.xls (Stationary-4)
  • I downloaded the Merrell_Compiled_Raw_Data_Vibrio.xls file to my Desktop.

Normalize the log ratios for the set of slides in the experiment

I scaled and centered the data (between chip normalization) by performing the following operations:

  • Inserted a new Worksheet into my Excel file, and named it "scaled_centered".
  • Went back to the "compiled_raw_data" worksheet, Selected All and Copy. Went to my new "scaled_centered" worksheet, clicked on the upper, left-hand cell (cell A1) and Pasted.
  • Inserted two rows in between the top row of headers and the first data row.
  • In cell A2, I typed "Average" and in cell A3, typed "StdDev".
  • I went to compute the Average log ratio for each chip (each column of data). In cell B2, I typed the following equation:
=AVERAGE(B4:B5224)
and pressed "Enter". Excel computed the average value of the cells specified in the range given inside the parentheses. Instead of typing the cell designations, I clicked on the beginning cell, scrolled down to the bottom of the worksheet, and shift-clicked on the ending cell.
  • Then I computed the Standard Deviation of the log ratios on each chip (each column of data). In cell B3, I typed the following equation:
=STDEV(B4:B5224)
and pressed "Enter".
  • Excel did some work for me. I copied these two equations (cells B2 and B3) and pasted them into the empty cells in the rest of the columns. Excel automatically changed the equation to match the cell designations for those columns.
  • I had computed the average and standard deviation of the log ratios for each chip. Then I did the scaling and centering based on these values.
  • I copied the column headings for all of my data columns and then pasted them to the right of the last data column so that I had a second set of headers above blank colums of cells. I edited the names of the columns so that they read: A1_scaled_centered, A2_scaled_centered, etc.
  • In cell N4, I typed the following equation:
=(B4-B$2)/B$3
In this case, I wanted the data in cell B4 to have the average subtracted from it (cell B2) and be divided by the standard deviation (cell B3). I used the dollar sign symbols in front of the "2" and "3" to tell Excel to always reference that row in the equation, even though I pasted it for the entire column of 5221 genes. Why is this important? Because it will keep the data consistent.
  • I copied and pasted this equation into the entire column. One easy way to do this was to click on the original cell with my equation and positioned my cursor at the bottom right corner. I saw my cursor change to a thin black plus sign (not a chubby white one). When it did, I double clicked, and the formula magically was copied to the entire column of genes.
  • I copied and pasted the scaling and centering equation for each of the columns of data with the "_scaled_centered" column header. I made sure that my equation was correct for the column I was calculating.

Perform statistical analysis on the ratios

I performed this step on the scaled and centered data I produced in the previous step.

  • I inserted a new worksheet and named it "statistics".
  • I went back to the "scaling_centering" worksheet and copied the first column ("ID").
  • I pasted the data into the first column of my new "statistics" worksheet.
  • I went back to the "scaling_centering" worksheet and copied the columns that were designated "_scaled_centered".
  • I went to my new worksheet and clicked on the B1 cell. I selected "Paste Special" from the Edit menu. A window opened: I clicked on the radio button for "Values" and clicked OK. This pasted the numerical result into my new worksheet instead of the equation which must have made calculations on the fly.
  • I deleted Rows 2 and 3 where it said "Average" and "StDev" so that my data rows with gene IDs were immediately below the header row 1.
  • I went to a new column on the right of my worksheet. I typed the header "Avg_LogFC_A", "Avg_LogFC_B", and "Avg_LogFC_C" into the top cell of the next three columns.
  • I computed the average log fold change for the replicates for each patient by typing the equation:
=AVERAGE(B2:E2)
into cell N2. I copied this equation and pasted it into the rest of the column.
  • I created the equation for patients B and C and pasted them into their respective columns.
  • I then computed the average of the averages. I typed the header "Avg_LogFC_all" into the first cell in the next empty column. I created the equation that computed the average of the three previous averages I calculated and pasted it into this entire column.
  • I inserted a new column next to the "Avg_LogFC_all" column that I computed in the previous step. I labeled the column "Tstat". This computed a T statistic that told me whether the scaled and centered average log ratio was significantly different than 0 (no change). I entered the equation: 
=AVERAGE(N2:P2)/(STDEV(N2:P2)/SQRT(number of replicates))
(NOTE: in this case the number of replicates was 3. I was careful that I was using the correct number of parentheses.) I copied the equation and pasted it into all rows in that column.
  • I labeled the top cell in the next column "Pvalue". In the cell below the label, I entered the equation: 
=TDIST(ABS(R2),degrees of freedom,2)

The number of degrees of freedom was the number of replicates minus one, so in my case there were 2 degrees of freedom. I copied the equation and pasted it into all rows in that column.

Calculate the Bonferroni p value Correction

  • I performed adjustments to the p value to correct for the multiple testing problem. I labeled the next two columns to the right with the same label, Bonferroni_Pvalue.
  • I typed the equation =S2*5221, Upon completion of this single computation, I used the trick to copy the formula throughout the column.
  • I replaced any corrected p value that was greater than 1 by the number 1 by typing the following formula into the first cell below the second Bonferroni_Pvalue header: =IF(T2>1,1,T2). I used the trick to copy the formula throughout the column.

10/20/15 Protocol

  • I continued with the BIOL398-01/S10:Sample Microarray Analysis Vibrio cholerae page to finish the list of actions to perform.

Calculate the Benjamini & Hochberg p value Correction

  • I inserted a new worksheet named "B-H_Pvalue".
  • I copied and pasted the "ID" column from my previous worksheet into the first column of the new worksheet.
  • I inserted a new column on the very left and named it "MasterIndex". I created a numerical index of genes so that I could always sort them back into the same order.
    • I typed a "1" in cell A2 and a "2" in cell A3.
    • I selected both cells. I hovered my mouse over the bottom-right corner of the selection until it made a thin black + sign. I double-clicked on the + sign to fill the entire column with a series of numbers from 1 to 5221 (the number of genes on the microarray).
  • For the following, I used Paste special > Paste values. I copied my unadjusted p values from my previous worksheet and pasted them into Column C.
  • I selected all of columns A, B, and C. I sorted by ascending values on Column C. I clicked the sort button from A to Z on the toolbar, in the window that appeared, sorted by column C, smallest to largest.
  • I typed the header "Rank" in cell D1. I created a series of numbers in ascending order from 1 to 5221 in this column. This was the p value rank, smallest to largest. I typed "1" into cell D2 and "2" into cell D3. I selected both cells D2 and D3. I double-clicked on the plus sign on the lower right-hand corner of my selection to fill the column with a series of numbers from 1 to 5221.
  • Then I calculated the Benjamini and Hochberg p value correction. I typed B-H_Pvalue in cell E1. I typed the following formula in cell E2: =(C2*5221)/D2 and pressed enter. I copied that equation to the entire column.
  • I typed "B-H_Pvalue" into cell F1.
  • I typed the following formula into cell F2: =IF(E2>1,1,E2) and pressed enter. I copied that equation to the entire column.
  • I selected columns A through F. I then sorted them by your MasterIndex in Column A in ascending order.
  • I copied column F and used Paste special > Paste values to paste it into the next column on the right of my "statistics" sheet.

Prepare file for GenMAPP

  • I inserted a new worksheet and named it "forGenMAPP".
  • I went back to the "statistics" worksheet and Selected All and Copy.
  • 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 formatted this worksheet for import into GenMAPP.
  • I selected Columns B through Q (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. Excel made me click through a couple of warnings because it didn't like me going all independent and choosing a different file type than the native .xls. That was OK. My new *.txt file was now ready for import into GenMAPP. But before I did that, I wanted to know a few things about my data as shown in the next section.

Sanity Check: Number of genes significantly changed

Before I moved on to the GenMAPP/MAPPFinder analysis, I wanted to perform a sanity check to make sure that I performed our data analysis correctly. I found out the number of genes that are significantly changed at various p value cut-offs and also compared my data analysis with the published results of Merrell et al. (2002).

  • 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, set a criterion that will filter my data so that the Pvalue has to be less than 0.05.
    • How many genes have p value < 0.05? and what is the percentage (out of 5221)?
    • 948/5221, about 18.2 % of genes have p value < 0.05.
    • What about p < 0.01? and what is the percentage (out of 5221)?
    • 235/5221, about 4.5 % of genes have p value < 0.01
    • What about p < 0.001? and what is the percentage (out of 5221)?
    • 24/5221, about 0.46% of genes have p value < 0.0001
    • What about p < 0.0001? and what is the percentage (out of 5221)?
    • 2/5221, about 0.04 % of genes have p value less than 0.0001
  • When I used a p value cut-off of p < 0.05, what is assumed is that there would have been a gene expression change that deviated this far from zero less than 5% of the time.
  • I had just performed 5221 T tests for significance. Another way to state what I was seeing with p < 0.05 is that I expected to see this magnitude of a gene expression change in about 5% of my T tests, or 261 times. (Tested my understanding: http://xkcd.com/882/.) Since I had more than 261 genes that pass this cut off, I knew that some genes were significantly changed. However, I didn't know which ones. To apply a more stringent criterion to my p values, I performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values. The Bonferroni correction was very stringent. The Benjamini-Hochberg correction was less stringent. To see this relationship, I filtered my data to determine the following:
    • How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 5221)?
    • 6/5221, about 0.12% of genes
    • How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 5221)?
    • 0/5221, 0% of genes
  • In summary, the p value cut-off was not thought of as some magical number at which data became "significant". Instead, it was a moveable confidence level. If I wanted to be very confident of my data, I used a small p value cut-off. If I was OK with being less confident about a gene expression change and wanted to include more genes in my analysis, I used a larger p value cut-off.
  • The "Avg_LogFC_all" told me the size of the gene expression change and in which direction. Positive values were increases relative to the control; negative values were decreases relative to the control.
    • I kept the (unadjusted) "Pvalue" filter at p < 0.05, filtered the "Avg_LogFC_all" column to show all genes with an average log fold change greater than zero. How many are there? (and %)
    • 325 genes, about 6.74% of genes.
    • I kept the (unadjusted) "Pvalue" filter at p < 0.05, filtered the "Avg_LogFC_all" column to show all genes with an average log fold change less than zero. How many are there? (and %)
    • 596 genes, about 11.42% of genes.
    • What about an average log fold change of > 0.25 and p < 0.05? (and %)
    • 339 genes, about 6.5 % of genes.
    • Or an average log fold change of < -0.25 and p < 0.05? (and %) (These are more realistic values for the fold change cut-offs because it represents about a 20% fold change which is about the level of detection of this technology.)
    • 579 genes, 11.09% of genes.
  • In summary, the p value cut-off was not thought of as some magical number at which data became "significant". Instead, it was a moveable confidence level. If I wanted to be very confident of my data, I used a small p value cut-off. If I was OK with being less confident about a gene expression change and wanted to include more genes in my analysis, I used a larger p value cut-off. For the GenMAPP analysis below, I used 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 for my analysis because I wanted to include several hundred genes in my analysis.
  • What criteria did Merrell et al. (2002) use to determine a significant gene expression change? How does it compare to our method?
    • Merrell et al. conducted a two-class SAM analysis, with the in vitro strain as class I and the each of the patients' samples being class II. The study selected genes with statistically significant changes, at least 2 fold, in each patient sample and these sample data were used to identify genes that significantly changed in expression for all three samples. Our method was similar in that we utilized the data from all three samples, however we used p values (less than 0.05) to identify significant changes in gene expression.

Sanity Check: Compare individual genes with known data

  • Merrell et al. (2002) report that genes with IDs: VC0028, VC0941, VC0869, VC0051, VC0647, VC0468, VC2350, and VCA0583 were all significantly changed in their data. Look these genes up in your spreadsheet. What are their fold changes and p values? Are they significantly changed in our analysis?
  • VC0028
    • Fold Change: 1st entry = 1.65, 2nd entry = 1.27
    • P-Value: 1st entry = 0.0474, 2nd entry = 0.0692
    • Significance: 1st and 2nd entry = statistically significant
  • VC0941
    • Fold Change: 1st entry = 0.09, 2nd entry = -0.28
    • P-Value: 1st entry = 0.6759, 2nd entry = 0.1636
    • Significance: 1st and 2nd entry = not statistically significant
  • VC0869
    • Fold Change (nth entry): 1 = 1.59, 2 = 1.95, 3 = 2.20, 4 = 1.50, 5 = 2.12
    • P-Value (nth entry): 1 = 0.0463, 2 = 0.0227, 3 = 0.0020, 4 = 0.0174, 5 = 0.0200
    • Significance (nth entry): 1 = significant, 2 = significant, 3 = significant, 4 = significant, 5 = significant
  • VC0051
    • Fold Change: 1st entry = 1.92, 2nd entry = 1.89
    • P-Value: 1st entry = 0.0139, 2nd entry = 0.0160
    • Significance: 1st and 2nd entry = statistically significant
  • VC0468
    • Fold Change: -0.17
    • P-Value: 0.3350
    • Significance: not statistically significant
  • VC2350
    • Fold Change: -2.40
    • P-Value: 0.0130
    • Significance: statistically significant
  • VCA0583
    • Fold Change: 1.06
    • P-Value: 0.1011
    • Significance: not statistically significant
  • I then moved onto the and part 2 page to continue the assignment now using the programs genMAPP and MAPPFinder. I used the text as a template.

10/22/15 GenMAPP Expression Dataset Manager Procedure

  • I launched the GenMAPP Program. I checked to make sure the correct Gene Database was loaded.
    • I looked in the lower, left-hand corner of the main GenMAPP Drafting Board window to see the name of the Gene Database that was loaded. If it was not the correct Gene Database or it said "No Gene Database", then went to the Data > Choose Gene Database menu item and selected the Gene Database I needed to perform the analysis.
    • Remember, you and your partner are going to use different versions of the Vibrio cholerae Gene Database for this exercise.
  • I selected the Data menu from the main Drafting Board window and chose Expression Dataset Manager from the drop-down list. The Expression Dataset Manager window opened.
  • I selected New Dataset from the Expression Datasets menu. I selected the tab-delimited text file that I formatted for GenMAPP (.txt) in the procedure above from the file dialog box that appeared.
    • I needed to download my .txt file from the wiki onto my Desktop.
  • The Data Type Specification window appeared. GenMAPP was expecting that I was providing numerical data. If any of my columns had text (character) data, I checked the box next to the field (column) name.
    • The Vibrio data I had been working with did not have any text (character) data in it.
  • I allowed the Expression Dataset Manager to convert my data.
    • This took a few minutes depending on the size of the dataset and the computer’s memory and processor speed. When the process was complete, the converted dataset was active in the Expression Dataset Manager window and the file was saved in the same folder the raw data file was in, named the same except with a .gex extension; for example, MyExperiment.gex.
    • A message appeared saying that the Expression Dataset Manager could not convert one or more lines of data. Lines that generated an error during the conversion of a raw data file were not added to the Expression Dataset. Instead, an exception file was created. The exception file was given the same name as my raw data file with .EX before the extension (e.g., MyExperiment.EX.txt). The exception file contained all of my raw data, with the addition of a column named ~Error~. This column contained either error messages or, if the program found no errors, a single space character.
      • Record the number of errors. For your journal assignment, open the .EX.txt file and use the Data > Filter > Autofilter function to determine what the errors were for the rows that were not converted. Record this information in your individual journal page.
      • After the conversion, there were 772 errors detected in the raw data by genMAPP using the 2009 databse.
      • It is likely that you will have a different number of errors than your partner who is using a different version of the Vibrio cholerae Gene Database. Which of you has more errors? Why do you think that is? Record your answers in your journal page.
      • I had many more errors in the 2009 database and this makes sense because it is an older database and with the updates there would be expected to be less errors, as my partner saw.

Map Onto Biological Pathways

Each time I launched GenMAPP, I needed to make sure that the correct Gene Database (.gdb) was loaded.

  • I checked in the lower left-hand corner of the window to see which Gene Database had been selected.
  • If I needed to change the Gene Database, I selected Data > Choose Gene Database. I navigated to the directory C:\GenMAPP 2 Data\Gene Databases and chose the correct one for my species.
  • For the exercise, I needed to download the appropriate Vibrio cholerae Gene Database.
    • Half of the class will use the Vc-Std_External_20090622.gdb Gene Database that was initially created by the Fall 2008 Biological Databases class.
    • Half of the class will use a new Vc-Std_External_20101022.gdb Gene Database that was created by Drs. Dahlquist and Dionisio a year later.
    • The members of a pair should each choose a different gene database.
      • I downloaded the Vc-Std_External_20090622.gdb Gene Database for this exercise.
  • I clicked on the link for the 2009 gene database, downloaded the file, and saved it into the folder C:\GenMAPP 2 Data\Gene Databases, and extracted it.

Color Sets

  • I customized the new Expression Dataset by creating new Color Sets which contained the instructions to GenMAPP for displaying data on MAPPs.
    • Color Sets contain the instructions to GenMAPP for displaying data from an Expression Dataset on MAPPs. I created a Color Set by filling in the following different fields in the Color Set area of the Expression Dataset Manager: a name for the Color Set, the gene value, and the criteria that determined how a gene object is colored on the MAPP. I entered a name in the Color Set Name field that is 20 characters or fewer.
    • The Gene Value was the data displayed next to the gene box on a MAPP. I selected the column of data to be used as the Gene Value from the drop down list or select [none]. I used "Avg_LogFC_all" for the Vibrio dataset I just created.
    • I activated the Criteria Builder by clicking the New button.
    • I entered a name for the criterion in the Label in Legend field.
    • I chose a color for the criterion by left-clicking on the Color box. I chose a color from the Color window that appeared and clicked OK.
    • I stated the criterion for color-coding a gene in the Criterion field.
      • A criterion was stated with relationships such as "this column greater than this value" or "that column less than or equal to that value". Individual relationships could be combined using as many ANDs and ORs as needed. A typical relationship is
[ColumnName] RelationalOperator Value
with the column name always enclosed in brackets and character values enclosed in single quotes. For example:
[Fold Change] >= 2
[p value] < 0.05
[Quality] = 'high'
To create criteria I chose items from the Columns and Ops (operators) lists shown in the Criteria Builder. The Columns list contained all of the column headings from my Expression Dataset. I chose a column from the list, I clicked on the column heading. It appeared at the location of the cursor in the Criterion box. The Criteria Builder surrounded the column names with brackets.
The Ops (operators) list contained the relational operators that were used in the criteria: equals ( = ) greater than ( > ), less than ( < ), greater than or equal to ( >= ), less than or equal to ( <= ), is not equal to ( <> ). I chose an operator from the list, by clicking on the symbol. It appeared at the location of the insertion bar (cursor) in the Criterion box. The Criteria Builder automatically surrounded the operators with spaces.
The Ops list also contained the conjunctions AND and OR, which were used to make compound criteria. For example:
[Fold Change] > 1.2 AND [p value] <= 0.05
Parentheses controlled the order of evaluation. Anything in parentheses was evaluated first. Parentheses were nested. For example:
[Control Average] = 100 AND ([Exp1 Average] > 100 OR [Exp2 Average] > 100)
Column names were used anywhere a value could be, for example: 
[Control Average] < [Experiment Average]
  • After completing a new criterion, add the criterion entry (label, criterion, and color) to the Criteria List by clicking the Add button.
    • For the Vibrio dataset, you will create two criterion. "Increased" will be [Avg_LogFC_all] > 0.25 AND [Pvalue] < 0.05 and "Decreased will be [Avg_LogFC_all] < -0.25 AND [Pvalue] < 0.05.
    • You may continue to add criteria to the Color Set by using the previous steps.
      • The buttons to the right of the list represent actions that can be performed on individual criteria. To modify a criterion label, color, or the criterion itself, first select the criterion in the list by left-clicking on it, and then click the Edit button. This puts the selected criterion into the Criteria Builder to be modified. Click the Save button to save changes to the modified criterion; click the Add button to add it to the list as a separate criterion. To remove a criterion from the list, left-click on the criterion to select it, and then click on the Delete button. The order of Criteria in the list has significance to GenMAPP. When applying an Expression Dataset and Color Set to a MAPP, GenMAPP examines the expression data for a particular gene object and applies the color for the first criterion in the list that is true. Therefore, it is imperative that when criteria overlap the user put the most important or least inclusive criteria in the list first. To change the order of the criteria in the list, left-click on the criterion to select it and then click the Move Up or Move Down buttons. No criteria met and Not found are always the last two positions in the list.
  • Save the entire Expression Dataset by selecting Save from the Expression Dataset menu. Changes made to a Color Set are not saved until you do this.
  • Exit the Expression Dataset Manager to view the Color Sets on a MAPP. Choose Exit from the Expression Dataset menu or click the close box in the upper right hand corner of the window.
  • Upload your .gex file to your journal entry page for later retrieval.

MAPPFinder Procedure

Note: My partner and I used the same criterion: "increased."

  • Launch the MAPPFinder program (or from within GenMAPP, select Tools > MAPPFinder).
  • Make sure that the Gene Database for the correct species is loaded. The name of the Gene Database appears at the bottom of the window. If this is not the right one, go to File > Choose Gene Database and choose the correct one. (The Gene Databases are stored in the folder C:\GenMAPP 2 Data\Gene Databases\.)
  • Click on the button "Calculate New Results".
  • Click on "Find File" and choose the your Expression Dataset file, for example, "MyDataset.gex", and click OK.
    • MAPPFinder may have found it for you already if you already had it open in GenMAPP, in which case, you just need to click OK.
  • Choose the Color Set and Criteria with which to filter the data. Click on either the "Increased" and "Decreased" criteria in the right-hand box, depending on which one your group is doing. (You could select both by holding down the Control key while clicking).
  • Check the boxes next to "Gene Ontology" and "p value".
  • Click the "Browse" button and create a meaningful filename for your results.
  • Click "Run MAPPFinder". The analysis will take several minutes. It may look like the computer is stalled; be patient, it will eventually start running.
  • When the results have been calculated, a Gene Ontology browser will open showing your results. All of the Gene Ontology terms that have at least 3 genes measured and a p value of less than 0.05 will be highlighted yellow. A term with a p value less than 0.05 is considered a "significant" result. Browse through the tree to see your results.
  • To see a list of the most significant Gene Ontology terms, click on the menu item "Show Ranked List".
    • List the top 10 Gene Ontology terms in your individual journal entry.
  1. localization
  2. aromatic amino acid family biosynthetic process
  3. cellular bipolymer biosynthetic process
  4. biopolymer metabolic process
  5. protein folding
  6. transport
  7. unfolded protein binding
  8. transporter activity
  9. molecular transducer activity
  10. macromolecule metabolic process
    • Compare your list with your partner who used a different version of the Gene Database. Are your terms the same or different? Why do you think that is? Record your answer in your individual journal entry.
      • Our GO terms are different from each other because we used a database that had been updated from one year to the next. The older database, 2009 that I examined had fewer gene entries and less gene IDs than my partner's database. This would affect the outcome of the top 10 gene ontology terms for each database, accounting for these differences we examined. A common gene between the two gene databases was protein folding.
  • One of the things you can do in MAPPFinder is to find the Gene Ontology term(s) with which a particular gene is associated. First, in the main MAPPFinder Browser window, click on the button "Collapse the Tree". Then, you can search for the genes that were mentioned by Merrell et al. (2002), VC0028, VC0941, VC0869, VC0051, VC0647, VC0468, VC2350, and VCA0583. Type the identifier for one of these genes into the MAPPFinder browser gene ID search field. Choose "OrderedLocusNames" from the drop-down menu to the right of the search field. Click on the GeneID Search button. The GO term(s) that are associated with that gene will be highlighted in blue. List the GO terms associated with each of those genes in your individual journal. (Note: they might not all be found.) Are they the same as your partner who is using a different Gene Database? Why or why not?
    • GO terms with VC0028:
      • No terms found.
    • GO terms with VC0941:
      • No terms found.
    • GO terms with VC0869:
      • No terms found.
    • GO terms with VC0051:
      • No terms found.
    • GO terms with VC0647:
      • RNA binding, 3'-5'-exoribonuclease activity, transferase activity, nucleotidyltransferase activity, polyribonucleotide nucleotidyltransferase activity
    • GO terms with VC0468
      • No terms found.
    • GO terms with VC2350:
      • No terms found.
    • GO terms with VCA0583:
      • outer membrane-bound periplasmic space
  • Click on one of the GO terms that are associated with one of the genes you looked up in the previous step. A MAPP will open listing all of the genes (as boxes) associated with that GO term. The genes named within the map are based on the UniProt identification system. To match the gene of interest to its identification go to the UniProt site and type in your gene ID into the search bar. Moreover, the genes on the MAPP will be color-coded with the gene expression data from the microarray experiment. List in your journal entry the name of the GO term you clicked on and whether the expression of the gene you were looking for changed significantly in the experiment.
    • I clicked on the transferase activity term and the expression of the gene I was looking for did decrease significantly as it was highlighted green on the map.
    • Double-click on the gene box. This will open a Internet Explorer window called the "Backpage" for this gene. This page has links to pages for this gene in the public databases. Click on the links to find out the function of this gene and record your answer in your individual journal page.
    • The MAPP that has just been created is stored in the directory, C:\GenMAPP 2 Data\MAPPs\VC GO. Upload this file and link to it in your journal.
  • In Windows, make a copy of your results (XXX-CriterionX-GO.txt) file.
    • "XXX" refers to the name you gave to your results file.
    • "CriterionX" refers to either "Criterion0" or "Criterion1". Since computers start counting at zero, "Criterion0" is the first criterion in the list you clicked on ("Increased" if you followed the directions) and "Criterion1" is the second criterion in the list you clicked on ("Decreased" if you followed the directions).
    • Upload your results file to your journal page.
  • Launch Microsoft Excel. Open the copies of the .txt files in Excel (you will need to "Show all files" and click "Finish" to the wizard that will open your file). This will show you the same data that you saw in the MAPPFinder Browser, but in tabular form.
  • Look at the top of the spreadsheet. There are rows of information that give you the background information on how MAPPFinder made the calculations. Compare this information with your partner who used a different version of the Vibrio Gene Database. Which numbers are different? Why are they different? Record this information in your individual journal entry.
    • The numbers that are different are: the genes meeting the criterion linked to a GO term, probes linked to a UniPort ID, genes linked to a GO term, and the genes meeting the criterion linked to a GO term. These numbers are clearly different because the databases have varying number of genes in them due to the fact that one of them is a year newer and would include other genes that were discovered withing the past year. This accounts for the observed difference in the calculated numbers.
  • You will filter this list to show the top GO terms represented in your data for both the "Increased" and "Decreased" criteria. You will need to filter your list down to about 20 terms.
You will use these two filters depending on the number of terms you have:
Number Changed (in column I) greater than or equal to 4 or 5 AND less than 100
Percent Changed (in column L) greater than or equal to 25-50%

Conclusion

  • In this assignment, with the research of Merrell et al. we were able to review and analyze the microarray data that was used. Reformatting and statistical analysis was done using the data from the research. In the original experiment 3 samples of 'V. Cholerae' were taken from each of three separate patients. The research conducted analysis of its data using a software called SAM. In our analysis, we reformatted the data and did our calculations using microsoft excel. This data was then inputted into GenMAPP in order to visualize the relationships between specific genes. Analysis of the data confirmed the studies results that, "The characterization of 'V. cholerae' from human stools results in high expression levels of genes required for nutrient acquisition and motility, and low expression levels of genes required for bacterial chemotaxis" (Merret et al.) In having a chance to really see a vast data set and having the chance to manipulate, format, and statistically analyze the data in a way that demonstrates which genes are involved with what functions, I was able to experience the utilization of a certain database.

Here we report that characterization of Vibrio cholerae from human stools supports a model whereby human colonization creates a hyperinfectious bacterial state that is maintained after dissemination and that may contribute to epidemic spread of cholera. Transcriptional profiling of V. cholerae from stool samples revealed a unique physiological and behavioural state characterized by high expression levels of genes required for nutrient acquisition and motility, and low expression levels of genes required for bacterial chemotaxis.

Uploaded Files

Msaeedi23 (talk) 22:37, 26 October 2015 (PDT) Mahrad Saeedi

Class Whoopers Team Page
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Individual Journals
Shared Journals
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