A new approach for the joint analysis of multiple ChIP-seq libraries with application to histone modification.

ABSTRACT: Most approaches for analyzing ChIP-Seq data are focused on inferring exact protein binding sites from a single library. However, frequently multiple ChIP-Seq libraries derived from differing cell lines or tissue types from the same individual may be available. In such a situation, a separate analysis for each tissue or cell line may be inefficient. Here, we describe a novel method to analyze such data that intelligently uses the joint information from multiple related ChIP-Seq libraries. We present our method as a two-stage procedure. First, separate single cell line analysis is performed for each cell line. Here, we use a novel mixture regression approach to infer the subset of genes that are most likely to be involved in protein binding in each cell line. In the second step, we combine t

SUBMITTER: Ferguson JP 

PROVIDER: S-EPMC3770480 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature