University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Identification of enriched regions in ChIP-seq and whole-genome sequencing data

Identification of enriched regions in ChIP-seq and whole-genome sequencing data

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If you have a question about this talk, please contact Mustapha Amrani.

Statistical Challenges Arising from Genome Resequencing

An important statistical problem in analysis of ChIP-seq data is robust identification of both sharp peaks (e.g., for transcription factors) and broad regions (e.g., for many histone modifications) in the enrichment profile. I will describe a method based on model selection by maximum likelihood. This method is intuitive, fast, and can be extended easily to multi-sample cases. This method is also applicable to detection of copy number variations from whole-genome sequencing data. I will illustrate the applications of this method with examples from the Encyclopedia of DNA Elements (ENCODE) and the Cancer Genome Atlas (TCGA) data.

This talk is part of the Isaac Newton Institute Seminar Series series.

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