TY - JOUR
AU - Haussler, David
AB - 1 2 Adam Siepel and David Haussler Center for Biomolecular Science and Engineering, University of California, Santa Cruz, CA 95064, USA, acs@soe.ucsc.edu Center for Biomolecular Science and Engineering, University of California, Santa Cruz, CA 95064, USA, haussler@soe.ucsc.edu Phylogenetic hidden Markov models, or phylo-HMMs, are probabilistic mod- els that consider not only the way substitutions occur through evolutionary history at each site of a genome but also the way this process changes from one site to the next. By treating molecular evolution as a combination of two Markov processes—one that operates in the dimension of space (along a genome) and one that operates in the dimension of time (along the branches of a phylogenetic tree)—these models allow aspects of both sequence structure and sequence evolution to be captured. Moreover, as we will discuss, they per- mit key computations to be performed exactly and efficiently. Phylo-HMMs allow evolutionary information to be brought to bear on a wide variety of problems of sequence “segmentation,” such as gene prediction and the iden- tification of conserved elements. Phylo-HMMs were first proposed as a way of improving phylogenetic mod- els that allow for variation among sites in the rate of substitution [9, 52]. Soon afterward, 
TI - Statistical Methods in Molecular Evolution: Phylogenetic Hidden Markov Models
DA - 2005-01-01
UR - https://www.deepdyve.com/lp/springer-journals/statistical-methods-in-molecular-evolution-phylogenetic-hidden-markov-196xjXvzmw
DP - DeepDyve
ER -