Use of runs statistics for pattern recognition in genomic DNA sequences.
In this article, the use of the finite Markov chain imbedding (FMCI) technique to study patterns in DNA under a hidden Markov model (HMM) is introduced. With a vision of studying multiple runs-related statistics simultaneously under an HMM through the FMCI technique, this work establishes an investigation of a bivariate runs statistic under a binary HMM for DNA pattern recognition. An FMCI-based recursive algorithm is derived and implemented for the determination of the exact distribution of this bivariate runs statistic under an independent identically distributed (IID) framework, a Markov chain (MC) framework, and a binary HMM framework. With this algorithm, we have studied the distributions of the bivariate runs statistic under different binary HMM parameter sets; probabilistic profiles of runs are created and shown to be useful for trapping HMM maximum likelihood estimates (MLEs). This MLE-trapping scheme offers good initial estimates to jump-start the expectation-maximization (EM) algorithm in HMM parameter estimation and helps prevent the EM estimates from landing on a local maximum or a saddle point. Applications of the bivariate runs statistic and the probabilistic profiles in conjunction with binary HMMs for pattern recognition in genomic DNA sequences are illustrated via case studies on DNA bendability signals using human DNA data.
Cheung
Leo Wang-Kit
LW
Epidemiology Section, Cancer Etiology Program, Cancer Research Center of Hawaii, University of Hawaii, Honolulu, HI 96813-2479, USA. lcheung@crch.hawaii.edu
D000465:Algorithms; D003196:Computer Graphics; D003198:Computer Simulation; D016678:Genome; D010363:Pattern Recognition, Automated; D011487:Protein Conformation; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA; D020539:Sequence Analysis, Protein; D012984:Software