A transdimensional Bayesian model for pattern recognition in DNA sequences.
Identification of transcription factor binding sites (TFBSs) is essential to elucidate gene regulatory networks. This article is focused on the recognition of overpresented short patterns, called "motifs", that may correspond to regulatory binding sites in the DNA sequences upstream of genes. An integrated Bayesian model is proposed to incorporate all unknown characteristics in motif discovery, including the number of motifs, motif widths, motif compositions, the number of motif sites, and locations of motif sites. Reversible jump Markov chain Monte Carlo is used to obtain posterior inference in the transdimensional parameter space. We present a number of suggestions for graphical summarization of the posterior distribution over the complex parameter space. The basic model is extended using a third-order Markov structure for nonmotif bases and allowing positions within a motif to be switched between 2 types: "conserved" and "degenerate." We evaluate the prediction accuracy for the simulated data with 3 motifs and apply the model to upstream sequences in high signal-to-noise regions in a human ChIP-chip study. The performance of the Bayesian model is assessed using yeast data sets of various numbers of sequences and background structures, with and without true TFBSs. The performance is also compared to other computational methods, including 2 statistical approaches, AlignACE and multiple expectation maximization for motif elicitation, and 1 word numeration-based approach, yeast motif finder (YMF).
Li
Sierra M
SM
Division of Oncology Biostatistics, Sidney Kimmel Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD 21205-2013, USA.
Wakefield
Jon
J
Self
Steve
S
D000465:Algorithms; D001499:Bayes Theorem; D001665:Binding Sites / Q000235:genetics; D047369:Chromatin Immunoprecipitation; D003198:Computer Simulation; D004268:DNA-Binding Proteins / Q000378:metabolism; D006360:Heat-Shock Proteins / Q000378:metabolism; D018398:Homeodomain Proteins / Q000378:metabolism; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D026341:Minichromosome Maintenance 1 Protein; D015233:Models, Statistical; D009010:Monte Carlo Method; D000071317:Nanog Homeobox Protein; D050814:Octamer Transcription Factor-3 / Q000378:metabolism; D010363:Pattern Recognition, Automated; D011485:Protein Binding; D020218:Response Elements / Q000235:genetics*; D055748:SOXB1 Transcription Factors / Q000378:metabolism; D012441:Saccharomyces cerevisiae / Q000235:genetics; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism; D017422:Sequence Analysis, DNA / Q000379:methods*; D035181:TATA-Box Binding Protein / Q000235:genetics; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
D004268:DNA-Binding Proteins; C067168:HSF1 protein, S cerevisiae; D006360:Heat-Shock Proteins; D018398:Homeodomain Proteins; C498030:MCM1 protein, S cerevisiae; D026341:Minichromosome Maintenance 1 Protein; C486780:NANOG protein, human; D000071317:Nanog Homeobox Protein; D050814:Octamer Transcription Factor-3; C494173:POU5F1 protein, human; C527782:SOX2 protein, human; D055748:SOXB1 Transcription Factors; D029701:Saccharomyces cerevisiae Proteins; D035181:TATA-Box Binding Protein; D014157:Transcription Factors