Applications of generalized pair hidden Markov models to alignment and gene finding problems.
Hidden Markov models (HMMs) have been successfully applied to a variety of problems in molecular biology, ranging from alignment problems to gene finding and annotation. Alignment problems can be solved with pair HMMs, while gene finding programs rely on generalized HMMs in order to model exon lengths. In this paper, we introduce the generalized pair HMM (GPHMM), which is an extension of both pair and generalized HMMs. We show how GPHMMs, in conjunction with approximate alignments, can be used for cross-species gene finding and describe applications to DNA-cDNA and DNA-protein alignment. GPHMMs provide a unifying and probabilistically sound theory for modeling these problems.
Pachter
Lior
L
Department of Mathematics, University of California Berkeley, Berkeley, CA 94720, USA. lpachter@math.berkeley.edu
Alexandersson
Marina
M
Cawley
Simon
S
D000465:Algorithms; D019295:Computational Biology; D004247:DNA / Q000235:genetics; D008390:Markov Chains; D015233:Models, Statistical; D011506:Proteins / Q000235:genetics; D016415:Sequence Alignment / Q000706:statistics & numerical data*
D011506:Proteins; D004247:DNA