Time: Friday, July 8, 9:00 am - noon
Place: Bioinformatics Computer Core Lab (Rm 104), Life Sciences Bldg
Organizers: Michael Miles
Audience: Primarily 1st year students

Topics of session

• continued analysis of expression data from Wookies
• Discussion of study questions from Problem Set 2
• Combining bioinformatic expression data with data from other sources

Resources of session

Required Reading: (from previous simulation)
• Daniel H. Geschwind, MD, PhD; Joseph D. Dougherty; Lili C. Kudo; Stanislav Karsten. Beyond The Gene List: Using Bioinformatics To Make Sense Out Of Array Data.
• Michael F. Miles, M.D., Ph.D. and Robnet Kerns, Ph.D. Analysis and Interpretation of Microarray Data. Department of Pharmacology and Toxicology. Virginia Commonwealth University Richmond, VA

Optional Reading:
• Article: Quackenbush (2000): Computational Analysis of Microarray Data. Nature Reviews Genetics 2:418-427 [HTML]
  Excellent in terms of explaining analysis issues/approaches
• Article: Teferri A, et al (2002): Primer on Medical Genomics, Part III: Microarray Experiments and Data Analysis. Mayo Clin Proc. 77:927-940. [PDF]
  Good general review of microarrays and data analysis
• Article: NCBI (2003): A Science Primer: Microarrays -- Chipping Away at the Mysteries of Science and Medicine. [HTML]
  Short and basic


Additional Resources
• Presentation: BBSIlect2_7_05.ppt - PowerPoint slides we will use in today's class.
• Link: TIGR Microarray Resources
  
• Link: Fatigo: Resources for identifying over-representation of certain functional groups (e.g. Biological pathways) in a set of microarray data
• Link: PubGene: An algorithm for identifying associations between genes based upon a keyword search of the biomedical literature
• Link: Stanford SOURCE: A very convenient starting place for annotating genes derived from lists/clusters of array data. Compiles info from multiple databases.
• Link: TIGR Resourcer: One of many tools available at TIGR. This allows matching of results across different chip types.
• Link: UCSC genome browser: Site for drilling down to genomic data for any sequence.
• Link: UniGene: NCBI based database compiling and expressed transcripts into clusters giving best representation of expressed genes
• Link: WebQTL: a new web-based tool for identifying genetic correlations amongst expression patterns. Also superimposes genetics of expression upon a database of genetic data for various complex traits

Before coming to the session

• Look over Problem Set 2.
• Read the required articles listed above if you haven't already.
• Look over the additional resource links and become familiar with them. You will use these in the second problem set.

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