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SUMMARY:New tools for new technologies: Differential analysis of genome-sc
 ale sequencing data
DTSTART:20110113T100000
DTSTAMP:20260407T095107Z
UID:37d74bf2d13bf23d056337dfbdd3d732b3a669e07b33463312a25442
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr Mark ROBINSON\, Epigenetics Laboratory\, Garvan – Bioinfo
 rmatics Division WEHI\, Australia\nNew technologies\, and specifically hig
 h-throughput sequencing experiments\, are driving biological enquiry.  Lar
 ge-scale sequencing projects extract information in two distinct ways: fro
 m the sequence itself\, or from the density of reads mapping to a particul
 ar location.  For the latter\, the data can often be aggregated into count
 s at some level of interest\, such as transcripts\, promoters\, or genomic
  regions\, depending on the type of experiment.  We have developed statist
 ical methods for discovering changes in count data\, analogous to the succ
 essful procedures developed for microarray data.  In the first part of the
  talk\, I will give an overview of the procedures and highlight applicatio
 ns of our methods to transcriptome and epigenome data.  In particular\, sp
 ecial considerations need to be made for biases such as RNA composition or
  copy number state.  In the second part of the talk\, I will discuss our c
 omparisons of high-throughput data from the myriad of techniques that inte
 rrogate DNA methylation.
LOCATION:AI 1153 https://plan.epfl.ch/?room==AI%201153
STATUS:CONFIRMED
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