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MM-ChIP enables integrative analysis of cross-platform and between-laboratory ChIP-chip or ChIP-seq data

Yiwen Chen1, Clifford A Meyer1, Tao Liu1, Wei Li23, Jun S Liu4 and Xiaole Shirley Liu1*

Author Affiliations

1 Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, 44 Binney Street, Boston, MA 02115, USA

2 Division of Biostatistics, Dan L Duncan Cancer Center, Department of Molecular and Cellular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA

3 Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, 200092, PR China

4 Department of Statistics, Harvard University, 1 Oxford Street, Cambridge, MA 02138, USA

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Genome Biology 2011, 12:R11  doi:10.1186/gb-2011-12-2-r11

Published: 1 February 2011

Abstract

The ChIP-chip and ChIP-seq techniques enable genome-wide mapping of in vivo protein-DNA interactions and chromatin states. The cross-platform and between-laboratory variation poses a challenge to the comparison and integration of results from different ChIP experiments. We describe a novel method, MM-ChIP, which integrates information from cross-platform and between-laboratory ChIP-chip or ChIP-seq datasets. It improves both the sensitivity and the specificity of detecting ChIP-enriched regions, and is a useful meta-analysis tool for driving discoveries from multiple data sources.