A Fully Bayesian Hidden Ising Model for ChIP-Seq Data Analysis

• Activity Number: 234
• Type: Contributed
• Date/Time: Monday, August 1, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Qianxing Mo*+
• Companies: Memorial Sloan-Kettering Cancer Center
• Keywords: ChIP-seq ; Ising model ; next generation sequencing ; Massively parallel sequencing ; Bayesian Hierarchical ; Transcription factor

Abstract

Chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) is a powerful technique that is being used in a wide range of biological studies. To systematically model ChIP-seq data, we build a dynamic signal profile for each chromosome, and then model the profile using a fully Bayesian hidden Ising model. The proposed model naturally takes into account spatial dependency, global and local distributions of sequence tags. It can be used for one-sample and two-sample analyses. Through model diagnosis, the proposed method can detect falsely enriched regions caused by sequencing and/or mapping errors, which is usually not offered by the existing hypothesis-testing-based methods. The proposed method is illustrated using three transcription factor ChIP-seq data sets and four mixed ChIP-seq data sets, and compared with four popular and/or well-documented methods: MACS, CisGenome, BayesPeak and SISSRs. The results indicate that the proposed method achieves equivalent or higher sensitivity and spatial resolution in detecting transcription factor binding sites with false discovery rate at a much lower level.