title: jMOSAiCS: joint analysis of multiple ChIP-seq datasets.
abstract: The ChIP-seq technique enables genome-wide mapping of in vivo protein-DNA interactions and chromatin states. Current analytical approaches for ChIP-seq analysis are largely geared towards single-sample investigations, and have limited applicability in comparative settings that aim to identify combinatorial patterns of enrichment across multiple datasets. We describe a novel probabilistic method, jMOSAiCS, for jointly analyzing multiple ChIP-seq datasets. We demonstrate its usefulness with a wide range of data-driven computational experiments and with a case study of histone modifications on GATA1-occupied segments during erythroid differentiation. jMOSAiCS is open source software and can be downloaded from Bioconductor 1.
authors.forename: Xin
authors.initials: X
authors.lastname: Zeng
authors.forename: Rajendran
authors.initials: R
authors.lastname: Sanalkumar
authors.forename: Emery H
authors.initials: EH
authors.lastname: Bresnick
authors.forename: Hongda
authors.initials: H
authors.lastname: Li
authors.forename: Qiang
authors.initials: Q
authors.lastname: Chang
authors.forename: Sündüz
authors.initials: S
authors.lastname: Keleş
mesh_terms: D000818:Animals; D042002:Chromatin Assembly and Disassembly; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D066264:Datasets as Topic; D050982:GATA1 Transcription Factor / Q000378:metabolism; D006657:Histones / Q000378:metabolism; D006801:Humans; D012984:Software
chemical_list: D050982:GATA1 Transcription Factor; D006657:Histones