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SUMMARY:Exploiting spatial features in the analysis of ChIP- and BS-Seq da
 ta - 	Guido Sanguinetti - School of Informatics &amp\; SynthSys\, Edinburg
 h
DTSTART:20161019T121500Z
DTEND:20161019T131500Z
UID:TALK68457@talks.cam.ac.uk
CONTACT:Pietro Lio
DESCRIPTION:Epigenetic modifications such as histone modifications and DNA
  methylation play a central role in the regulation of gene expression. Nex
 t generation sequencing technologies are now enabling genome-wide measurem
 ents of epigenetic marks\, yet the data returned by such technologies is d
 ifficult to interpret. Here\, we start from the observation that such meas
 urements often return broad\, spatially correlated patterns of modificatio
 n which are highly reproducible between replicate experiments. We then use
  machine  learning methodologies to exploit such spatial correlations to d
 efine\n stronger prediction methods. In particular\, I will illustrate nov
 el\n statistical hypothesis testing methodologies for ChIP-Seq (MMDiff) an
 d BS-Seq (M3D) data\, which exploit spatial features to yield more powerfu
 l tests. I will also elaborate on what may be the mechanisms underpinning 
 the presence of such spatial correlations\, and illustrate how higher-orde
 r spatial features may be used to predict gene expression from DNA methyla
 tion alone.
LOCATION:Lecture Theatre 2\, Computer Laboratory
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