A probabilistic method to detect regulatory modules - PubMed (original) (raw)
Comparative Study
A probabilistic method to detect regulatory modules
Saurabh Sinha et al. Bioinformatics. 2003.
Abstract
Motivation: The discovery of cis-regulatory modules in metazoan genomes is crucial for understanding the connection between genes and organism diversity.
Results: We develop a computational method that uses Hidden Markov Models and an Expectation Maximization algorithm to detect such modules, given the weight matrices of a set of transcription factors known to work together. Two novel features of our probabilistic model are: (i) correlations between binding sites, known to be required for module activity, are exploited, and (ii) phylogenetic comparisons among sequences from multiple species are made to highlight a regulatory module. The novel features are shown to improve detection of modules, in experiments on synthetic as well as biological data.
Similar articles
- Prediction of similarly acting cis-regulatory modules by subsequence profiling and comparative genomics in Drosophila melanogaster and D.pseudoobscura.
Grad YH, Roth FP, Halfon MS, Church GM. Grad YH, et al. Bioinformatics. 2004 Nov 1;20(16):2738-50. doi: 10.1093/bioinformatics/bth320. Epub 2004 May 14. Bioinformatics. 2004. PMID: 15145800 - MORPH: probabilistic alignment combined with hidden Markov models of cis-regulatory modules.
Sinha S, He X. Sinha S, et al. PLoS Comput Biol. 2007 Nov;3(11):e216. doi: 10.1371/journal.pcbi.0030216. Epub 2007 Sep 24. PLoS Comput Biol. 2007. PMID: 17997594 Free PMC article. - Finding cis-regulatory modules in Drosophila using phylogenetic hidden Markov models.
Wong WS, Nielsen R. Wong WS, et al. Bioinformatics. 2007 Aug 15;23(16):2031-7. doi: 10.1093/bioinformatics/btm299. Epub 2007 Jun 5. Bioinformatics. 2007. PMID: 17550911 - Computational methods for the detection of cis-regulatory modules.
Van Loo P, Marynen P. Van Loo P, et al. Brief Bioinform. 2009 Sep;10(5):509-24. doi: 10.1093/bib/bbp025. Epub 2009 Jun 4. Brief Bioinform. 2009. PMID: 19498042 Review. - Hidden Markov model and its applications in motif findings.
Wu J, Xie J. Wu J, et al. Methods Mol Biol. 2010;620:405-16. doi: 10.1007/978-1-60761-580-4_13. Methods Mol Biol. 2010. PMID: 20652513 Review.
Cited by
- Individual differences in honey bee behavior enabled by plasticity in brain gene regulatory networks.
Jones BM, Rao VD, Gernat T, Jagla T, Cash-Ahmed AC, Rubin BE, Comi TJ, Bhogale S, Husain SS, Blatti C, Middendorf M, Sinha S, Chandrasekaran S, Robinson GE. Jones BM, et al. Elife. 2020 Dec 22;9:e62850. doi: 10.7554/eLife.62850. Elife. 2020. PMID: 33350385 Free PMC article. - Functional effects of variation in transcription factor binding highlight long-range gene regulation by epromoters.
Mitchelmore J, Grinberg NF, Wallace C, Spivakov M. Mitchelmore J, et al. Nucleic Acids Res. 2020 Apr 6;48(6):2866-2879. doi: 10.1093/nar/gkaa123. Nucleic Acids Res. 2020. PMID: 32112106 Free PMC article. - TFforge utilizes large-scale binding site divergence to identify transcriptional regulators involved in phenotypic differences.
Langer BE, Hiller M. Langer BE, et al. Nucleic Acids Res. 2019 Feb 28;47(4):e19. doi: 10.1093/nar/gky1200. Nucleic Acids Res. 2019. PMID: 30496469 Free PMC article. - REforge Associates Transcription Factor Binding Site Divergence in Regulatory Elements with Phenotypic Differences between Species.
Langer BE, Roscito JG, Hiller M. Langer BE, et al. Mol Biol Evol. 2018 Dec 1;35(12):3027-3040. doi: 10.1093/molbev/msy187. Mol Biol Evol. 2018. PMID: 30256993 Free PMC article. - Computational exploration of cis-regulatory modules in rhythmic expression data using the "Exploration of Distinctive CREs and CRMs" (EDCC) and "CRM Network Generator" (CNG) programs.
Bekiaris PS, Tekath T, Staiger D, Danisman S. Bekiaris PS, et al. PLoS One. 2018 Jan 3;13(1):e0190421. doi: 10.1371/journal.pone.0190421. eCollection 2018. PLoS One. 2018. PMID: 29298348 Free PMC article.
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources