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Heterogeneity in DNA Multiple Alignments: Modeling, Inference, and Applications in Motif Finding
Author(s) -
Chen Gong,
Zhou Qing
Publication year - 2010
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2009.01362.x
Subject(s) - hidden markov model , inference , dna binding site , computer science , computational biology , markov chain , bayes' theorem , gibbs sampling , motif (music) , bayesian inference , bayesian probability , sequence motif , multiple sequence alignment , sequence alignment , biology , data mining , genetics , artificial intelligence , gene , machine learning , gene expression , promoter , physics , acoustics , peptide sequence
Summary Transcription factors bind sequence‐specific sites in DNA to regulate gene transcription. Identifying transcription factor binding sites (TFBSs) is an important step for understanding gene regulation. Although sophisticated in modeling TFBSs and their combinatorial patterns, computational methods for TFBS detection and motif finding often make oversimplified homogeneous model assumptions for background sequences. Since nucleotide base composition varies across genomic regions, it is expected to be helpful for motif finding to incorporate the heterogeneity into background modeling. When sequences from multiple species are utilized, variation in evolutionary conservation violates the common assumption of an identical conservation level in multiple alignments. To handle both types of heterogeneity, we propose a generative model in which a segmented Markov chain is used to partition a multiple alignment into regions of homogeneous nucleotide base composition and a hidden Markov model (HMM) is employed to account for different conservation levels. Bayesian inference on the model is developed via Gibbs sampling with dynamic programming recursions. Simulation studies and empirical evidence from biological data sets reveal the dramatic effect of background modeling on motif finding, and demonstrate that the proposed approach is able to achieve substantial improvements over commonly used background models.