title: A fully Bayesian hidden Ising model for ChIP-seq data analysis.
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 including genome-wide measurements of protein-DNA interactions, DNA methylation, and histone modifications. The vast amount of data and biases introduced by sequencing and/or genome mapping pose new challenges and call for effective methods and fast computer programs for statistical analysis. 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 and 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 3 transcription factor (TF) ChIP-seq data sets and 2 mixed ChIP-seq data sets and compared with 4 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 TF binding sites with false discovery rate at a much lower level.
authors.affiliation: Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA. moq@mskcc.org
authors.forename: Qianxing
authors.initials: Q
authors.lastname: Mo
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001665:Binding Sites / Q000235:genetics; D001709:Biotechnology; D047369:Chromatin Immunoprecipitation / Q000706:statistics & numerical data*; D004247:DNA / Q000235:genetics / Q000378:metabolism; D003627:Data Interpretation, Statistical; D030561:Databases, Nucleic Acid; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D017422:Sequence Analysis, DNA / Q000706:statistics & numerical data*; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D014157:Transcription Factors; D004247:DNA