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jMOSAiCS: joint analysis of multiple ChIP-seq datasets

Xin Zeng1, Rajendran Sanalkumar3, Emery H Bresnick3, Hongda Li4, Qiang Chang45 and Sündüz Keleş12*

Author affiliations

1 Department of Statistics, University of Wisconsin-Madison, 1220 Medical Sciences Center, 1300 University Avenue, Madison, WI 53706, USA

2 Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, K6/446 Clinical Sciences Center, 600 Highland Avenue, Madison, WI 53792-4675, USA

3 Wisconsin Institutes for Medical Research, University of Wisconsin-Madison Carbone Cancer Center, Department of Cell and Regenerative Biology, University of Wisconsin School of Medicine and Public Health, 325 Services Memorial Institute, 1300 University Avenue, Madison, WI 53706, USA

4 Genetics Training Program, Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Madison, WI 53705-2280, USA

5 Department of Genetics and Neurology, University of Wisconsin-Madison, 425 Henry Mall Madison, WI 53706, USA

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Citation and License

Genome Biology 2013, 14:R38  doi:10.1186/gb-2013-14-4-r38

Published: 29 April 2013


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].