Bioinformatics at WSU

Matt SettlesBioinformatics Core

Washington State University

Wednesday, April 23, 2008WSU Linux User Group (LUG)

What is Bioinformatics

Computational Biologist

BioinformaticianBiostatistician

Biology

Computer Science

Statistics

The analysis of biological information using computers and statistical techniques

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

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

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

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

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.

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?

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

Results

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

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

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!

The END

QUESTIONS??