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

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.

[1]  T. Mikkelsen,et al.  Genome-wide maps of chromatin state in pluripotent and lineage-committed cells , 2007, Nature.

[2]  Wei Li,et al.  Model-based analysis of two-color arrays (MA2C) , 2007, Genome Biology.

[3]  S. Batzoglou,et al.  Genome-Wide Analysis of Transcription Factor Binding Sites Based on ChIP-Seq Data , 2008, Nature Methods.

[4]  Clifford A. Meyer,et al.  Model-based Analysis of ChIP-Seq (MACS) , 2008, Genome Biology.

[5]  Wing Hung Wong,et al.  TileMap: create chromosomal map of tiling array hybridizations , 2005, Bioinform..

[6]  William Stafford Noble,et al.  Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project , 2007, Nature.

[7]  John D. Storey The positive false discovery rate: a Bayesian interpretation and the q-value , 2003 .

[8]  A. Mortazavi,et al.  Genome-Wide Mapping of in Vivo Protein-DNA Interactions , 2007, Science.

[9]  Allen D. Delaney,et al.  Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing , 2007, Nature Methods.

[10]  P. Park,et al.  Design and analysis of ChIP-seq experiments for DNA-binding proteins , 2008, Nature Biotechnology.

[11]  Hongkai Ji,et al.  TileProbe: modeling tiling array probe effects using publicly available data , 2009, Bioinform..

[12]  M. Gerstein,et al.  Unlocking the secrets of the genome , 2009, Nature.

[13]  Raymond K. Auerbach,et al.  PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls , 2009, Nature Biotechnology.

[14]  Mark Gerstein,et al.  Systematic evaluation of variability in ChIP-chip experiments using predefined DNA targets. , 2008, Genome research.

[15]  Simak Ali,et al.  Regulation of ERBB2 by oestrogen receptor–PAX2 determines response to tamoxifen , 2008, Nature.

[16]  Mark Gerstein,et al.  Tilescope: online analysis pipeline for high-density tiling microarray data , 2007, Genome Biology.

[17]  P. Farnham Insights from genomic profiling of transcription factors , 2009, Nature Reviews Genetics.

[18]  Mark Bieda,et al.  Unbiased location analysis of E2F1-binding sites suggests a widespread role for E2F1 in the human genome. , 2006, Genome research.

[19]  B. Bernstein,et al.  Chromatin state maps: new technologies, new insights. , 2008, Current opinion in genetics & development.

[20]  Hao Wu,et al.  JAMIE: joint analysis of multiple ChIP-chip experiments , 2010, Bioinform..

[21]  Tae Hoon Kim,et al.  Genome-wide analysis of protein-DNA interactions. , 2006, Annual review of genomics and human genetics.

[22]  Clifford A. Meyer,et al.  Model-based analysis of tiling-arrays for ChIP-chip , 2006, Proceedings of the National Academy of Sciences.

[23]  Hyungwon Choi,et al.  Hierarchical hidden Markov model with application to joint analysis of ChIP-chip and ChIP-seq data , 2009, Bioinform..

[24]  Dustin E. Schones,et al.  High-Resolution Profiling of Histone Methylations in the Human Genome , 2007, Cell.

[25]  Dustin E. Schones,et al.  Genome-wide approaches to studying chromatin modifications , 2008, Nature Reviews Genetics.

[26]  Clifford A. Meyer,et al.  Genome-wide analysis of estrogen receptor binding sites , 2006, Nature Genetics.