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Related Articles Hierarchical Hidden Markov Model with Application to Joint Analysis of ChIP-chip and ChIP-seq data.
Bioinformatics. 2009 May 14;
Authors: Choi H, Nesvizhskii AI, Ghosh D, Qin Z
MOTIVATION: Chromatin immunoprecipitation (ChIP) experiments followed by array hybridization, or ChIP-chip, is a powerful approach for identifying transcription factor binding sites (TFBS) and has been widely used. Recently, massively parallel sequencing coupled with ChIP experiments (ChIP-seq) has been increasingly used as an alternative to ChIP-chip, offering cost-effective genomewide coverage and resolution up to a single basepair. For many well-studied transcription factors, both ChIP-seq and ChIP-chip experiments have been applied and their data are publicly available.Previous analyses have revealed substantial technology-specific binding signals despite strong correlation between the two sets of results. Therefore, it is of interest to see whether the two data sources can be combined to enhance the detection of TFBS. RESULTS: In this work, hierarchical hidden Markov model (HHMM) is proposed for combining data from ChIP-seq and ChIP-chip. In HHMM, inference results from individual HMMs in ChIP-seq and ChIP-chip experiments are summarized by a higher level HMM. Simulation studies show the advantage of HHMM when data from both technologies co-exist. Analysis of two well-studied transcription factors, NRSF and CTCF, also suggests that HHMM yields improved TFBS identification in comparison to analyses using individual data sources or a simple merger of the two. AVAILABILITY: Source code for the software ChIPmeta freely available for download at URL http://www.umich.edu/~hwchoi/HHMMsoftware.zip, implemented in C and supported on linux. CONTACT: [email protected], [email protected].
PMID: 19447789 [PubMed - as supplied by publisher]
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Related Articles Hierarchical Hidden Markov Model with Application to Joint Analysis of ChIP-chip and ChIP-seq data.
Bioinformatics. 2009 May 14;
Authors: Choi H, Nesvizhskii AI, Ghosh D, Qin Z
MOTIVATION: Chromatin immunoprecipitation (ChIP) experiments followed by array hybridization, or ChIP-chip, is a powerful approach for identifying transcription factor binding sites (TFBS) and has been widely used. Recently, massively parallel sequencing coupled with ChIP experiments (ChIP-seq) has been increasingly used as an alternative to ChIP-chip, offering cost-effective genomewide coverage and resolution up to a single basepair. For many well-studied transcription factors, both ChIP-seq and ChIP-chip experiments have been applied and their data are publicly available.Previous analyses have revealed substantial technology-specific binding signals despite strong correlation between the two sets of results. Therefore, it is of interest to see whether the two data sources can be combined to enhance the detection of TFBS. RESULTS: In this work, hierarchical hidden Markov model (HHMM) is proposed for combining data from ChIP-seq and ChIP-chip. In HHMM, inference results from individual HMMs in ChIP-seq and ChIP-chip experiments are summarized by a higher level HMM. Simulation studies show the advantage of HHMM when data from both technologies co-exist. Analysis of two well-studied transcription factors, NRSF and CTCF, also suggests that HHMM yields improved TFBS identification in comparison to analyses using individual data sources or a simple merger of the two. AVAILABILITY: Source code for the software ChIPmeta freely available for download at URL http://www.umich.edu/~hwchoi/HHMMsoftware.zip, implemented in C and supported on linux. CONTACT: [email protected], [email protected].
PMID: 19447789 [PubMed - as supplied by publisher]
More...