BIOL368/F14:Week 13

This journal entry is due on Wednesday, November 26 at midnight PST (Tuesday night/Wednesday morning). NOTE that the server records the time as Eastern Standard Time (EST). Therefore, midnight will register as 03:00.

NOTE that this assignment is UNDER CONSTRUCTION.

Background

References

• Brown, P.O. & Botstein, D. (1999) Exploring the new world of the genome with DNA microarrays Nature Genetics 21: 33-37.
• Campbell, A.M. and Heyer, L.J. (2003), “Chapter 4: Basic Research with DNA Microarrays”, in Discovering Genomics, Proteomics, and Bioinformatics, Cold Spring Harbor Laboratory Press, pp. 107-124. (Available on MyLMUConnect)
• Dahlquist, K.D., Salomonis, N., Vranizan, K., Lawlor, S.C., & Conklin, B.R. (2002) GenMAPP, A New Tool for Viewing and Analyzing Microarray Data on Biological Pathways. Nature Genetics 31: 19-20.
• DeRisi, J.L., Iyer, V.R., and Brown, P.O. (1997) Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale. Science 278: 680-686.
• Doniger et al. (2003)
• Salomonis et al. (2007)

Overview of DNA Microarray Analysis

This is a list of steps required to analyze DNA microarray data.

1. Quantitate the fluorescence signal in each spot in the microarray image.
   • Typically performed by the scanner software, although third party software packages do exist.
   • The image of the microarray slide and this quantitation are considered the "raw-est" form of the data.
   • Ideally, this type of raw data would be made publicly available upon publication.
   • In practice, the image data is usually not made available because the raw image file of one slide could be up to 100 MB in size.
   • Also, some journals do not require data deposition as a requirement for publication, so often published data are not actually available anywhere for download.
   • Microarray data is not centrally located on the web. Some major sources are:
     • NCBI GEO
     • EBI ArrayExpress
     • Stanford Microarray Database
     • PUMAdb (Princeton Microarray Database)
     • In addition, microarray data can sometimes be found as supplementary information with a journal article or on an investigator's own web site.
2. Calculate the ratio of red/green fluorescence
3. Log(base 2) transform the ratios
4. Normalize the log ratios on each microarray slide
5. Normalize the log ratios for a set of slides in an experiment
6. Perform statistical analysis on the log ratios
7. Compare individual genes with known data
8. Look for patterns (expression profiles; clusters) in the data (many programs are available to do this)
9. Perform Gene Ontology term enrichment analysis (we will use MAPPFinder for this)
10. Map onto biological pathways (we will use GenMAPP for this)

Individual Journal Assignment

• Store this journal entry as "username Week 13" (i.e., this is the text to place between the square brackets when you link to this page).
• Create the following set of links. These links should all be in your personal template; then use the template on your journal entry.
  • Link to your journal entry from your user page.
  • Link back from your journal entry to your user page.
  • Link to this assignment from your journal entry.
  • Don't forget to add the "BIOL368/F14" category to the end of your wiki page.

Begin Microarray Data Analysis

Getting to know your microarray data

The task for this week is to download and organize the microarray data corresponding to your paper to get it ready for analysis next week.

1. Go to the ArrayExpress site for your data and download the following files:
   • "Investigation description" .idf.txt
   • "Sample and data relationship" .sdrf.txt
   • "Raw data" .raw.zip or .raw.gz
   • "Processed data" .processed.zip or .processed.gz
   • "Array design" .adf.txt
2. Then upload these files to the OpenWetWare wiki (if possible) or to Lionshare and then link to them on your individual journal pages.
3. From the methods section of your microarray paper, you need to figure out the following:
   • What samples did they collect and use for the microarray experiment?
   • How many microarray chips did they hybridize in the experiment?
   • Which samples were paired to hybridize on the chip?
   • Which was labeled red (Cy5)? Which was labeled green (Cy3)?
   • How many replicates did they perform of each type?
     • Biological replicates are made from entirely different biological samples.
     • Technical replicates are made when one biological sample is split at a particular stage in the procedure and then carried through to the end of the procedure.
4. Record this information on your individual journal pages. If you have this from your journal outline, you can copy and paste it into your new journal page.
5. Using the .sdrf.txt file, you need to then find the names of the files that correspond to the names of your samples from the paper. Make a list that says which file corresponds to which sample.
6. The instructor will then show you which columns of data to copy into a new Master spreadsheet. You will upload this spreadsheet to the wiki and then link to it from your journal page.
7. If you finish this assignment early, let the instructor know. I will then guide you in the next steps of the data analysis.

Shared Journal Assignment

• Store your journal entry in the shared BIOL368/F14:Class Journal Week 13 page. If this page does not exist yet, go ahead and create it.
• Link to the shared journal entry from your user page; this should be part of your template.
• Link the shared journal page to this assignment page.
• Sign your portion of the journal with the standard wiki signature shortcut (~~~~).
• Add the "BIOL368/F14" category to the end of the wiki page (if someo

View

Now that you've done your own microarray analysis, we will revisit the case "Deception at Duke".

• View the video: The Importance of Reproducible Research in High-Throughput Biology: Case Studies in Forensic Bioinformatics.
• View the slides from DataONE on data entry and manipulation.
• Optional: for more information on the Duke saga, see the web site put together by Baggerly and Coombes here.

Reflection

• What were the main issues with the data and analysis identified by Baggerly and Coombs? What best practices enumerated by DataONE were violated? Which of these did Dr. Baggerly claim were common issues?
• What recommendations does Dr. Baggerly recommend for reproducible research? How do these correspond to what DataONE recommends?
• Do you have any further reaction to this case after viewing Dr. Baggerly's talk?
• Look at the methods and results described in the Merrell et al. (2002) paper. Do you think there is sufficient information there to reproduce their data analysis? Why or why not?