A Bayesian Hidden Markov Model for Motif Discovery Through Joint Modeling of Genomic Sequence and ChIP-Chip Data Export

@article{citeulike:4057705, abstract = {We propose a unified framework for the analysis of chromatin (Ch) immunoprecipitation (IP) microarray (ChIP-chip) data for detecting transcription factor binding sites (TFBSs) or motifs. ChIP-chip assays are used to focus the genome-wide search for TFBSs by isolating a sample of DNA fragments with TFBSs and applying this sample to a microarray with probes corresponding to tiled segments across the genome. Present analytical methods use a two-step approach: (i) analyze array data to estimate IP-enrichment peaks then (ii) analyze the corresponding sequences independently of intensity information. The proposed model integrates peak finding and motif discovery through a unified Bayesian hidden Markov model (HMM) framework that accommodates the inherent uncertainty in both measurements. A Markov chain Monte Carlo algorithm is formulated for parameter estimation, adapting recursive techniques used for HMMs. In simulations and applications to a yeast RAP1 dataset, the proposed method has favorable TFBS discovery performance compared to currently available two-stage procedures in terms of both sensitivity and specificity.}, address = {Department of Epidemiology and Biostatistics, Mail Code 7933, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, Texas 78229-3900, U.S.A.; Department of Biostatistics, Boston University, 801 Massachusetts Avenue, Boston, Massachusetts 02118, U.S.A.; Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina 27599, U.S.A.}, author = {Gelfond, Jonathan A. L. and Gupta, Mayetri and Ibrahim, Joseph G.}, citeulike-article-id = {4057705}, citeulike-linkout-0 = {http://dx.doi.org/10.1111/j.1541-0420.2008.01180.x}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19210737}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19210737}, citeulike-linkout-3 = {http://www3.interscience.wiley.com/cgi-bin/abstract/121683236/ABSTRACT}, doi = {10.1111/j.1541-0420.2008.01180.x}, journal = {Biometrics}, keywords = {chipchip, discovery, motif}, number = {9999}, posted-at = {2009-02-16 11:11:16}, priority = {2}, title = {A Bayesian Hidden Markov Model for Motif Discovery Through Joint Modeling of Genomic Sequence and ChIP-Chip Data}, url = {http://dx.doi.org/10.1111/j.1541-0420.2008.01180.x}, volume = {9999}, year = {2009} }

TY - JOUR ID - citeulike:4057705 L3 - citeulike-article-id:4057705 N2 - We propose a unified framework for the analysis of chromatin (Ch) immunoprecipitation (IP) microarray (ChIP-chip) data for detecting transcription factor binding sites (TFBSs) or motifs. ChIP-chip assays are used to focus the genome-wide search for TFBSs by isolating a sample of DNA fragments with TFBSs and applying this sample to a microarray with probes corresponding to tiled segments across the genome. Present analytical methods use a two-step approach: (i) analyze array data to estimate IP-enrichment peaks then (ii) analyze the corresponding sequences independently of intensity information. The proposed model integrates peak finding and motif discovery through a unified Bayesian hidden Markov model (HMM) framework that accommodates the inherent uncertainty in both measurements. A Markov chain Monte Carlo algorithm is formulated for parameter estimation, adapting recursive techniques used for HMMs. In simulations and applications to a yeast RAP1 dataset, the proposed method has favorable TFBS discovery performance compared to currently available two-stage procedures in terms of both sensitivity and specificity. IS - 9999 JF - Biometrics AD - Department of Epidemiology and Biostatistics, Mail Code 7933, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, Texas 78229-3900, U.S.A.; Department of Biostatistics, Boston University, 801 Massachusetts Avenue, Boston, Massachusetts 02118, U.S.A.; Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina 27599, U.S.A. TI - A Bayesian Hidden Markov Model for Motif Discovery Through Joint Modeling of Genomic Sequence and ChIP-Chip Data VL - 9999 KW - chipchip KW - discovery KW - motif AU - Gelfond, Jonathan AU - Gupta, Mayetri AU - Ibrahim, Joseph PY - 2009/// UR - http://dx.doi.org/10.1111/j.1541-0420.2008.01180.x ER -