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Functional Genomics with R

Functional Genomics with R. Alena van Bömmel Max Planck Institute for Molecular Genetics. Transcriptional Regulation. Target Genes of a TF. Experiments. Target Genes of a TF. Experiments. ChIP -chip, ChIP-seq. Target Genes of a TF. Experiments. Microarray. ChIP -chip, ChIP-seq.

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Functional Genomics with R

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  1. Functional Genomics with R • Alena van Bömmel • Max Planck Institute for Molecular Genetics

  2. Transcriptional Regulation

  3. Target Genes of a TF Experiments

  4. Target Genes of a TF Experiments ChIP-chip, ChIP-seq

  5. Target Genes of a TF Experiments Microarray ChIP-chip, ChIP-seq

  6. Target Genes of a TF Experiments Microarray PWM Genome Scan ChIP-chip, ChIP-seq

  7. Target Genes of a TF Experiments Microarray PWM Genome Scan ChIP-chip, ChIP-seq Data analysis

  8. Target Genes of a TF Experiments Microarray PWM Genome Scan ChIP-chip, ChIP-seq Data analysis

  9. Target Genes of a TF Experiments Microarray PWM Genome Scan ChIP-chip, ChIP-seq Data analysis

  10. Target Genes of a TF Experiments Microarray PWM Genome Scan ChIP-chip, ChIP-seq Data must be from the same cell line/tissue! Data analysis

  11. Target Genes of a TF Data analysis

  12. Target Genes of a TF Data analysis Comparison

  13. Some important questions • How do we find the target genes of ChIP-seq peaks in enhancers? • distant location of peaks to genes (>5kb) • how do we find the target genes? • additional tools for enhancer-target prediction • In which genomic locations do we predict the TF-binding? • promoter region-which exact coordinates? • do we have an information about open chromatin? • which additional data can we use?

  14. Additional analysis • Binding motifs • are the overrepresented motifs in the ChIP-peak regions different? • do we find any co-factors? • Recommended tool: • RSAT rsat.ulb.ac.be binding motifs binding motifs binding motifs

  15. Bioinformatics • Read mapping (Bowtie/bwa) • Peak Calling (MACS/Bioconductor) • Peak-Target Analysis (Bioconductor) • Microarray data analysis (Bioconductor) • Differential Genes (R) • GSEA • PWM Genome Scan (TRAP/MatScan) • Statistics (R) • Data Integration (R/Python/Perl) • Statistical Analysis (R)

  16. Bioinformatics tools READ THE MANUALS! • Bowtiebowtie-bio.sourceforge.net/manual.shtml • bwabio-bwa.sourceforge.net/bwa.shtml • MACS github.com/taoliu/MACS/blob/macs_v1/README.rst • TRAPtrap.molgen.mpg.de/cgi-bin/home.cgi • matrix-scan http://rsat.ulb.ac.be/ • Bioconductorwww.bioconductor.org/(more info in R course) Databases • GEOwww.ncbi.nlm.nih.gov/geo/ • ENCODE genome.ucsc.edu/ENCODE/ • SRAwww.ncbi.nlm.nih.gov/sra

  17. Schedule • 11.03. Introduction lecture, R course • 19.03. Presentation of the detailed plan of each group (which TF, cell line, tools, data, data integration, team work ) • 26.03., 02.04., 09.04., 16.04. progress meetings • 23.04. Final report deadline, Presentations • 25.04. Final meeting, discussion of final reports

  18. Group 1 • Expression data: Lin, Vega et al., PLoS Genet., 2007 • ChIP-seq data: Joseph, Orlov, Huss et al., MSB, 2010 • PWM database: jaspar.genereg.net

  19. Group 2 • Expression data: Cappellen, Schlange, Bauer et al., EMBO reports, 2007 • Musgrove et al., PLoS One, 2008 • ChIP-seq data: ENCODE Project • PWM database: jaspar.genereg.net

  20. Group 3 • Expression data: Lin, Vega et al., PLoS Genet., 2007 • ChIP-seq data: Joseph, Orlov, Huss et al., MSB, 2010 • PWM database: jaspar.genereg.net

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