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Activity Number:
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463
- SPEED: Statistics in Epidemiology and Genomics and Genetics
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Type:
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Contributed
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Date/Time:
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Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #323977
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View Presentation
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Title:
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A Statistical Method for the Analysis of Multiple ChIP-Seq Data Sets
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Author(s):
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Pedro Luiz Baldoni* and Naim Rashid and Joseph G Ibrahim
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Companies:
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Univ of North Carolina At Chapel Hill and University of North Carolina at Chapel Hill and UNC
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Keywords:
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Mixed model ;
Hidden Markov model ;
High-throughput sequencing ;
Mixture regression
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Abstract:
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Chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) is an experimental technique to detect regions of protein-DNA interaction in the genome, such a transcription factor binding sites or regions containing histone modifications ("enrichment regions"). A common goal of the analysis of ChIP-seq data is to identify genomic loci enriched for sequencing reads pertaining to DNA bound to the factor of interest. Given the reduction of massive parallel sequencing costs over time, novel methods to detect consensus regions of enrichment across multiple samples or differential enrichment between groups of samples are of interest. We develop a statistical method and software to simultaneously analyze multiple histone modification ChIP-seq datasets through a class of efficient Mixed Hidden Markov Models (MHMM) to call consensus broad regions region of enrichment found across samples. This novel methodology will provide a tool able to compare data from different experiments in a single framework by considering subject and population-specific variation in a single model. Simulation studies and real data results will be presented.
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Authors who are presenting talks have a * after their name.
