1 / 49

Making Sense Out of Transcriptome Integrative Bioinformatic Approaches

Making Sense Out of Transcriptome Integrative Bioinformatic Approaches. Anil Jegga , D.V.M., M.S. Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center Department of Pediatrics University of Cincinnati College of Medicine Email: anil.jegga@cchmc.org

Download Presentation

Making Sense Out of Transcriptome Integrative Bioinformatic Approaches

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Making Sense Out of TranscriptomeIntegrative Bioinformatic Approaches Anil Jegga, D.V.M., M.S. Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center Department of Pediatrics University of Cincinnati College of Medicine Email: anil.jegga@cchmc.org Homepage: http://anil.cchmc.org

  2. Acknowledgements • Scott Tabar • Eric Bardes • Bruce Aronow • Jing Chen* • Siva Gowrisankar • Vivek Kaimal • Amit Sinha* • Mrunal Deshmukh • Nishanth Vepachedu • Divya Sardana

  3. Expression Profile - Gene Lists Annotation Databases Gene Ontology, Pathways Genome-wide Promoters Putative Regulatory Signatures E2F RB1 MCM4 FOS SIVA….. PDX1 GLUT2 PAX4 PDX1 IAPP…. p53 CDKN1A CTSD CASP DDB2…. DNA Repair XRCC1 OGG1 ERCC1 MPG….. Angiogenesis HIF1A ANGPT1 VEGF KLF5…. Gene lists associated with similar function/process/pathway Enrichment Analysis P53 CTSD CASP DDB2…. E2F RB1 MCM4 FOS… Observed Expected DNA Repair XRCC1 OGG1 ERCC1 MPG…. Angiogenesis HIF1A ANGPT1 VEGF….. Significant Enrichment Random Distribution

  4. I have a list of co-expressed mRNAs (Transcriptome)…. Now what? • Identify putative shared regulatory elements Identify the underlying biological theme • Known transcription factor binding sites (TFBS) • Conserved • Non-conserved • Unknown TFBS or Novel motifs • Conserved • Non-conserved • MicroRNAs • Gene Ontology • Pathways • Phenotype/Disease Association • Protein Domains • Protein Interactions • Expression in other tissues/experiments Give a man a fish and you feed him for a day. Teach a man to fish and you feed him for a lifetime

  5. Gene Regulatory Networks (GRNs) Biological question: Are co-expressed genes co-regulated? Do they share cis-elements or TFBSs? Are there any significantcommon motifs within the promoter regions?

  6. http://genometrafac.cchmc.org

  7. You need to have a login account; contact anil.jegga@cchmc.org 1 2 3

  8. 4 5 6

  9. 7 8 9

  10. 10

  11. 11 12

  12. 13 14

  13. 4 5 6

  14. 7 8 9

  15. 13 14

  16. DiRE: http://dire.dcode.org

  17. http://www.cisreg.ca/oPOSSUM/

  18. 3 4 5 2 1 Genome Browser Gateway choices: • Select Clade • Select genome/species: You can search only one species at a time • Assembly: the official backbone DNA sequence • Position: location in the genome to examine or search term (gene symbol, accession number, etc.) • Image width: how many pixels in display window; 5000 max • Configure: make fonts bigger + other options 6 Golden Path: http://genome.ucsc.edu

  19. 1 2 • Paste the gene symbols • Remember it is case-sensitive: • Human: all upper case (e.g. XRCC1) • Mouse: lower case (first letter upper case. E.g. Xrcc1) Select “refFlat” under “table” Ensure that “region” is “genome” Click on “paste list” Describe your track 3 4 Enter number of bp you want to analyze/download Select the output format as “custom track”

  20. 6 5 Select “Variation and Repeats” under “Group” Click on “create” under “intersection” Change the “group” to “Custom Tracks” and select the appropriate “track” and “table” 7 8 Try GTF output too

  21. 9 10 Genome Browser view that lists all the SNPs lying within the upstream 1 kb (the region we queried) region of one of the genes analyzed. One drawback with this output is it doesn’t tell you which SNPs are in the upstream region of which gene. However, since the positions of SNPs are included, you can compare them with the gene coordinates and figure it out .

  22. Functional Networks Biological question: Are co-expressed genes functionally similar? Do they share same GO terms or pathways? Are there any significantenriched terms within a group of gene list?

  23. http://david.abcc.ncifcrf.gov/

  24. http://www.pantherdb.org/ You can compare multiple lists!

  25. ToppGene – General Schema http://toppgene.cchmc.org

  26. TOPPGene - Data Sources • Gene Ontology: GO and NCBI Entrez Gene • Mouse Phenotype: MGI (used for the first time for human disease gene prioritization) • Pathways: KEGG, BioCarta, BioCyc, Reactome, GenMAPP, MSigDB • Domains: UniProt (Pfam, Interpro,etc.) • Interactions: NCBI Entrez Gene (Biogrid, Reactome, BIND, HPRD, etc.) • Pubmed IDs: NCBI Entrez Gene • Expression: GEO • Cytoband: MSigDB • Cis-Elements: MSigDB • miRNA Targets: MSigDB

  27. http://www.fatigo.org

  28. http://vortex.cs.wayne.edu/projects.htm

  29. My NCBI: Can create/store queries; Set mail options to receive new articles on your chosen queries/subjects • PubMed Limits: Useful to refine your queries and get more appropriate results • Preview/Index: You can customize (intersect, etc.) your previous queries

More Related