title: A probabilistic model for sequence alignment with context-sensitive indels.
abstract: Probabilistic approaches for sequence alignment are usually based on pair Hidden Markov Models (HMMs) or Stochastic Context Free Grammars (SCFGs). Recent studies have shown a significant correlation between the content of short indels and their flanking regions, which by definition cannot be modelled by the above two approaches. In this work, we present a context-sensitive indel model based on a pair Tree-Adjoining Grammar (TAG), along with accompanying algorithms for efficient alignment and parameter estimation. The increased precision and statistical power of this model is shown on simulated and real genomic data. As the cost of sequencing plummets, the usefulness of comparative analysis is becoming limited by alignment accuracy rather than data availability. Our results will therefore have an impact on any type of downstream comparative genomics analyses that rely on alignments. Fine-grained studies of small functional regions or disease markers, for example, could be significantly improved by our method. The implementation is available at www.mcb.mcgill.ca/~blanchem/software.html.
authors.affiliation: Center for Biomolecular Science and Engineering, University of California, Santa Cruz, California 95064, USA. hickey@soe.ucsc.edu
authors.forename: Glenn
authors.initials: G
authors.lastname: Hickey
authors.forename: Mathieu
authors.initials: M
authors.lastname: Blanchette
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D015894:Genome, Human; D055106:Genome-Wide Association Study; D006801:Humans; D054643:INDEL Mutation; D016013:Likelihood Functions; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D012015:Reference Standards; D016415:Sequence Alignment / Q000379:methods* / Q000592:standards; D017422:Sequence Analysis, DNA / Q000379:methods