1. Bioinformatics at WSU Matt Settles Bioinformatics Core Washington State University Wednesday, April 23, 2008 WSU Linux User Group (LUG)‏

2. What is Bioinformatics Computational Biologist Bioinformatician Biostatistician Biology Computer Science Statistics The analysis of biological information using computers and statistical techniques

3. Subfields of Bioinformatics Sequence analysis  Main areas: Sequence alignment and Sequence databases Genome annotation  Main areas: Gene finding, Gene predicting Computational evolutionary biology  Main areas: Systematics, Phylogenetics Analysis of High-throughput data  Main areas: RNA microarrays, aCGH, Whole genome genotyping arrays. Analysis of Whole Genome Sequencing Data  Emerging Field

4. Subfields of Bioinformatics Comparative genomics  How are species different and how are they the same? Systems Biology  Networks of Networks (the golden goose!!)‏ Quantitative Genetics Measuring Biodiversity Modeling biological systems High-throughput image analysis Analysis of protein expression Prediction of protein structure Protein-protein docking

5. What does a Bioinformatician Do? Works in an interdisciplinary team  Design of experiments  Data management, databases  Analysis from start to finish  Data integration, annotation, visualization Software Development  Visual tools  Databases Research  New techniques for the storage and analysis of biological data, both statistical and compuational

6. What tools do we use Software programs developed by others,  GUI and command line  Open Source preferably Statistical Programming Languages/Environments  R – programming environment www.r-project.org www.bioconductor.org C like Interpreted language that acts similar to scheme, Full graphics capabilities C/python/perl interfaces Software programs we ourselves develop

7. Central dogma of molecular biology Each gene is transcribed (at the appropriate time) from DNA into mRNA, which then leaves the nucleus and is translated into the required protein.

8. Whole Genome Association Analysis Whole Genome Genotyping Array  Bovine (COW) 58,000 SNPs Illumina Beadarray  Represents all 29 chromosomes, X chromosome and the Unknown chromosome Samples  255 dairy cattle from 4 different heards  130 Control cattle (healthy)‏  125 Johne's positive cattle (sick)‏ 14.8 MILLION DATA POINTS !!! Biological Question of interest  Is there a collection of SNPs that are associated with the disease Johne's?

9. Analysis Outline Read in and format data into something we can work with in R and plink. Quality Assurance  Toss samples that do not meet QA (7 samples)‏  Toss SNPs that do not meet QA (8,935 SNPs)‏ Treat SNPs as independent and analyze each with a statistical model. Correct for multiple testing Visualize results

10. Results 10 regions were identified as being potentially interesting with a p < 0.001 multiple testing correction (permutation based)‏

11. Next Step Validate in the lab, the regions of interest. Perform multi-locus analysis, computer cluster will be necessary here. Mine the data for additional information

12. Job Position Position with the Bioinformatics Core ~ 20 hours per week ~ $12-$15/hour Potential internship credit Description: Aid in the analysis of microarray data, create analysis pipeline to be used by WSU researchers. Required Skills: know how to code Bonuses: Possibility of publications!

13. The END QUESTIONS??