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Genome Biology & Applied Bioinformatics Gene Sets Mehmet Tevfik DORAK, MD PhD

Genome Biology & Applied Bioinformatics Gene Sets Mehmet Tevfik DORAK, MD PhD. Schedule. Outline. How to Generate a Gene Set (NOT the nearest genes) Gene Set Enrichment Analysis (GSEA) (DAVID, GSEA, WebGestalt , ToppGene , HumanMine ). Gene Set Enrichment Possibilities.

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Genome Biology & Applied Bioinformatics Gene Sets Mehmet Tevfik DORAK, MD PhD

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  1. Genome Biology & Applied Bioinformatics Gene Sets Mehmet Tevfik DORAK, MD PhD

  2. Schedule

  3. Outline How to Generate a Gene Set (NOT the nearest genes) Gene Set Enrichment Analysis (GSEA) (DAVID, GSEA, WebGestalt, ToppGene, HumanMine)

  4. Gene Set Enrichment Possibilities Gene Ontology (GO Terms) Transcription Factor Binding Motifs miRNA targets Belonging to a Defined Group (Molecular Signatures)

  5. DAVID

  6. DAVID

  7. DAVID

  8. DAVID Default options are selected. Explore other options.

  9. DAVID You have done a pathway analysis.

  10. GSEA

  11. GSEA/MSigDB

  12. GSEA/MSigDB

  13. Gene Ontology

  14. CTDbase

  15. WebGestalt

  16. iGsea4Gwas

  17. HumanMine

  18. HumanMine

  19. iGsea4Gwas

  20. MAGENTA (MATLAB)

  21. GSEA-SNP (R)

  22. Key Points - A gene set can be generated from a SNP list using the targets of each SNP’s effect - A gene set can be subject to many different types of enrichment of overlap analyses - Results of an enrichment (including pathway) analysis may help to gain insight into disease biology

  23. … Looking forward …..

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