An integrated model of multiple-condition ChIP-Seq data reveals predeterminants of Cdx2 binding.

ABSTRACT: Regulatory proteins can bind to different sets of genomic targets in various cell types or conditions. To reliably characterize such condition-specific regulatory binding we introduce MultiGPS, an integrated machine learning approach for the analysis of multiple related ChIP-seq experiments. MultiGPS is based on a generalized Expectation Maximization framework that shares information across multiple experiments for binding event discovery. We demonstrate that our framework enables the simultaneous modeling of sparse condition-specific binding changes, sequence dependence, and replicate-specific noise sources. MultiGPS encourages consistency in reported binding event locations across multiple-condition ChIP-seq datasets and provides accurate estimation of ChIP enrichment levels at each even

SUBMITTER: Mahony S 

PROVIDER: S-EPMC3967921 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature