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SUMMARY:Sequence-based discovery of transcriptional targets. - Dr Stein Ae
 rts\, Department of Human Genetics\, KU Leuven University
DTSTART:20130424T133000Z
DTEND:20130424T143000Z
UID:TALK44084@talks.cam.ac.uk
CONTACT:Caroline Newnham
DESCRIPTION:The identification and characterization of functional cis-regu
 latory elements in eukaryotic genomes remains a key challenge in genome bi
 ology. We present a computational framework to analyse a human\, mouse\, o
 r fly gene network or gene signature and to confidently identify cis-regul
 atory modules and transcription factor (TF) binding sites. The framework u
 ses Hidden Markov Models to identify motif clusters\, combined with compar
 ative genomics cues\, rank statistics to identify enriched motifs\, and a 
 “motif2TF” step to prioritize candidate transcription factors (TF) for
  each enriched motif. The Drosophila version of our method (called i-cisTa
 rget) utilizes large collections of motifs (>6000 position weight matrices
 ) and of 'regulatory tracks' (> 500 data sets) as cues\, including the ent
 ire modEncode and BDTNP data sets. The human version of our method (called
  iRegulon) works as a Cytoscape plugin and thereby integrates cis-regulato
 ry sequence analysis with network biology.\n\nTo illustrate our methods\, 
 we show two case studies\, one in Drosophila retinal determination and the
  second in human cancer. For the Drosophila case study we have performed c
 ross-species transcriptomics by next-generation sequencing across three Dr
 osophila species and obtained a highly conserved core set of eye-specific 
 genes. Motif and CRM discovery unveiled a regulatory network downstream of
  the transcription factor Glass\, which we validated by RNA-Seq in glass m
 utant eyes and by in vivo enhancer-reporter assays. For the human case stu
 dy we have performed RNA-Seq and ChIP-Seq analysis for p53 in a breast can
 cer cell line and show how iRegulon successfully identifies known and nove
 l p53 binding sites and target genes. Encouraged by these results\, we app
 lied iRegulon to more than twenty thousand cancer gene signatures obtained
  both from signature databases and from bi-clustering 91 large cancer gene
  expression datasets\, and defined for each TF a context-free “meta-targ
 etome”. In conclusion\, i-cisTarget and iRegulon are next-generation mot
 if discovery methods that confidently identify master regulators and bona 
 fide direct targets from sets of co-regulated genes.\n
LOCATION:Part II Room\, Department of Genetics
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