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SUMMARY:Integration of sequence and array data in a population and haploty
 pe-based model of SNPs and CNVs.
DTSTART:20101221T100000
DTSTAMP:20260407T064221Z
UID:3c88c91c9c57a838b85024d179619fd76b294791db25e761ba1a150f
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr Lachlan Coin\, Epidemiology & Biostatistics\, School of Pub
 lic Health\, Imperial College\, London\, UK\nRead depth analysis has been 
 proposed as a method for detecting copy number variants from second genera
 tion sequence data. However\, the resolution of this approach strongly dep
 ends on coverage. In particular\, the resolution of current single-sample 
 methods may be limited on low-coverage population sequencing projects such
  as the 1000 genomes project. To overcome this\, we extended our previousl
 y published algorithm\, cnvHap\, which learns the local CNV haplotype stru
 cture in the entire population\, to low-coverage sequence data. We used de
 nse array CGH data collected on Hapmap samples to demonstrate the improvem
 ent in resolution available from this approach. We have previously quantif
 ied the improvement in CNV genotyping accuracy from integrating multiple g
 enotyping and CGH platforms in a single probabilistic model (cnvHap). In o
 rder to investigate whether existing array data can improve CNV genotyping
  accuracy from low coverage sequence data\, we also integrated array and s
 equence data in our model\, and ascertained improvements in CNV genotyping
  accuracy.
LOCATION:AI 1153 https://plan.epfl.ch/?room==AI%201153
STATUS:CONFIRMED
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