Matt Settles
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Matt Settles Bioinformatics Core Washington State University Wednesday, April 23, 2008 WSU Linux User Group (LUG) ‏. Bioinformatics at WSU. Biology. Computer Science. Statistics. What is Bioinformatics. The analysis of biological information using computers and statistical techniques.

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Bioinformatics at WSU

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Bioinformatics at wsu

Matt Settles

Bioinformatics Core

Washington State University

Wednesday, April 23, 2008

WSU Linux User Group (LUG)‏

Bioinformatics at WSU


What is bioinformatics

Biology

Computer Science

Statistics

What is Bioinformatics

The analysis of biological information using computers and statistical techniques

Computational Biologist

Biostatistician

Bioinformatician


Subfields of bioinformatics

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 bioinformatics1

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

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

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

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 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

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

Results

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


Next step

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

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

The END

QUESTIONS??


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