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Making Sense Out of Transcriptome Integrative Bioinformatic Approaches

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Title: Making Sense Out of Transcriptome Integrative Bioinformatic Approaches


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Making Sense Out of TranscriptomeIntegrative
Bioinformatic Approaches
Anil Jegga, D.V.M., M.S. Division of Biomedical
Informatics Cincinnati Childrens Hospital
Medical Center Department of Pediatrics University
of Cincinnati College of Medicine Email
anil.jegga_at_cchmc.org Homepage http//anil.cchmc.o
rg
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Acknowledgements
  • Jing Chen
  • Siva Gowrisankar
  • Vivek Kaimal
  • Amit Sinha
  • Mrunal Deshmukh
  • Nishanth Vepachedu
  • Divya Sardana
  • Scott Tabar
  • Eric Bardes
  • Bruce Aronow

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Expression Profile - Gene Lists
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I have a list of co-expressed mRNAs
(Transcriptome). Now what?
  • Identify the underlying biological theme
  • Identify putative shared regulatory elements
  • 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
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Gene Regulatory Networks (GRNs)
Biological question Are co-expressed genes
co-regulated?
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http//genometrafac.cchmc.org
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DiRE http//dire.dcode.org
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http//www.cisreg.ca/oPOSSUM/
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Golden Path http//genome.ucsc.edu
  • 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

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Functional Networks
Biological question Are co-expressed genes
functionally similar?
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http//david.abcc.ncifcrf.gov/
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http//www.pantherdb.org/
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ToppGene General Schema
http//toppgene.cchmc.org
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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

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http//www.fatigo.org
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http//vortex.cs.wayne.edu/projects.htm
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