SMCis: An Effective Algorithm for Discovery of Cis-Regulatory Modules

• Published in: PloS one Vol. 11; no. 9; p. e0162968
• Main Authors: Guo, Haitao, Huo, Hongwei, Yu, Qiang
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.09.2016; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Animals; Binding sites; Bioinformatics; Biology and Life Sciences; Computational Biology; Computer applications; Computer science; Customer relationship management software; Deoxyribonucleic acid; DNA; Drosophila; Gene expression; Genomes; Insects; Markov chains; Markov processes; Modules; Nucleotides; Physical sciences; Probabilistic methods; Probability; Regulatory sequences; Research and Analysis Methods; Transcription; Transcription factors; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0162968

The discovery of cis-regulatory modules (CRMs) is a challenging problem in computational biology. Limited by the difficulty of using an HMM to model dependent features in transcriptional regulatory sequences (TRSs), the probabilistic modeling methods based on HMMs cannot accurately represent the distance between regulatory elements in TRSs and are cumbersome to model the prevailing dependencies between motifs within CRMs. We propose a probabilistic modeling algorithm called SMCis, which builds a more powerful CRM discovery model based on a hidden semi-Markov model. Our model characterizes the regulatory structure of CRMs and effectively models dependencies between motifs at a higher level of abstraction based on segments rather than nucleotides. Experimental results on three benchmark datasets indicate that our method performs better than the compared algorithms.