Literature DB >> 15767776

Applications of hidden Markov models for characterization of homologous DNA sequences with a common gene.

Asger Hobolth1, Jens Ledet Jensen.   

Abstract

Identifying and characterizing the structure in genome sequences is one of the principal challenges in modern molecular biology, and comparative genomics offers a powerful tool. In this paper, we introduce a hidden Markov model that allows a comparative analysis of multiple sequences related by a phylogenetic tree, and we present an efficient method for estimating the parameters of the model. The model integrates structure prediction methods for one sequence, statistical multiple alignment methods, and phylogenetic information. This unified model is particularly useful for a detailed characterization of DNA sequences with a common gene. We illustrate the model on a variety of homologous sequences.

Mesh:

Year:  2005        PMID: 15767776     DOI: 10.1089/cmb.2005.12.186

Source DB:  PubMed          Journal:  J Comput Biol        ISSN: 1066-5277            Impact factor:   1.479


  3 in total

1.  Efficient algorithms for training the parameters of hidden Markov models using stochastic expectation maximization (EM) training and Viterbi training.

Authors:  Tin Y Lam; Irmtraud M Meyer
Journal:  Algorithms Mol Biol       Date:  2010-12-09       Impact factor: 1.405

2.  Fast and robust characterization of time-heterogeneous sequence evolutionary processes using substitution mapping.

Authors:  Jonathan Romiguier; Emeric Figuet; Nicolas Galtier; Emmanuel J P Douzery; Bastien Boussau; Julien Y Dutheil; Vincent Ranwez
Journal:  PLoS One       Date:  2012-03-27       Impact factor: 3.240

3.  Vertebrate gene finding from multiple-species alignments using a two-level strategy.

Authors:  David Carter; Richard Durbin
Journal:  Genome Biol       Date:  2006-08-07       Impact factor: 13.583

  3 in total

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