Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido

IMPACT FACTOR 2017: 0.812
5-year IMPACT FACTOR: 1.104

CiteScore 2017: 0.86

SCImago Journal Rank (SJR) 2017: 0.456
Source Normalized Impact per Paper (SNIP) 2017: 0.527

Mathematical Citation Quotient (MCQ) 2017: 0.04

See all formats and pricing
More options …
Volume 11, Issue 1


Volume 10 (2011)

Volume 9 (2010)

Volume 6 (2007)

Volume 5 (2006)

Volume 4 (2005)

Volume 2 (2003)

Volume 1 (2002)

A Context Dependent Pair Hidden Markov Model for Statistical Alignment

Ana Arribas-Gil / Catherine Matias
  • Laboratoire Statistique et Génome, Université d'Évry Val d'Essonne, UMR CNRS 8071, USC INRA
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2012-01-06 | DOI: https://doi.org/10.2202/1544-6115.1733

This article proposes a novel approach to statistical alignment of nucleotide sequences by introducing a context dependent structure on the substitution process in the underlying evolutionary model. We propose to estimate alignments and context dependent mutation rates relying on the observation of two homologous sequences. The procedure is based on a generalized pair-hidden Markov structure, where conditional on the alignment path, the nucleotide sequences follow a Markov distribution. We use a stochastic approximation expectation maximization (saem) algorithm to give accurate estimators of parameters and alignments. We provide results both on simulated data and vertebrate genomes, which are known to have a high mutation rate from CG dinucleotide. In particular, we establish that the method improves the accuracy of the alignment of a human pseudogene and its functional gene.

Keywords: comparative genomics; contextual alignment; DNA sequence alignment; em algorithm; insertion deletion model; pair hidden Markov model; probabilistic alignment; sequence evolution; statistical alignment; stochastic expectation maximization algorithm

About the article

Published Online: 2012-01-06

Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 11, Issue 1, Pages 1–29, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1733.

Export Citation

©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston.Get Permission

Comments (0)

Please log in or register to comment.
Log in