Markov models of genome segmentation.
We introduce Markov models for segmentation of symbolic sequences, extending a segmentation procedure based on the Jensen-Shannon divergence that has been introduced earlier. Higher-order Markov models are more sensitive to the details of local patterns and in application to genome analysis, this makes it possible to segment a sequence at positions that are biologically meaningful. We show the advantage of higher-order Markov-model-based segmentation procedures in detecting compositional inhomogeneity in chimeric DNA sequences constructed from genomes of diverse species, and in application to the E. coli K12 genome, boundaries of genomic islands, cryptic prophages, and horizontally acquired regions are accurately identified.
Thakur
Vivek
V
Center for Computational Biology and Bioinformatics, School of Information Technology, Jawaharlal Nehru University, New Delhi 110 067, India.
Azad
Rajeev K
RK
Ramaswamy
Ram
R
D000465:Algorithms; D001499:Bayes Theorem; D001703:Biophysics / Q000379:methods*; D003198:Computer Simulation; D004926:Escherichia coli / Q000235:genetics*; D016680:Genome, Bacterial; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D008962:Models, Theoretical; D012680:Sensitivity and Specificity