Format

Send to

Choose Destination
See comment in PubMed Commons below
PLoS Comput Biol. 2008 May 30;4(5):e1000069. doi: 10.1371/journal.pcbi.1000069.

A probabilistic model of local sequence alignment that simplifies statistical significance estimation.

Author information

1
Howard Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, Virginia, United States of America. eddys@janelia.hhmi.org

Abstract

Sequence database searches require accurate estimation of the statistical significance of scores. Optimal local sequence alignment scores follow Gumbel distributions, but determining an important parameter of the distribution (lambda) requires time-consuming computational simulation. Moreover, optimal alignment scores are less powerful than probabilistic scores that integrate over alignment uncertainty ("Forward" scores), but the expected distribution of Forward scores remains unknown. Here, I conjecture that both expected score distributions have simple, predictable forms when full probabilistic modeling methods are used. For a probabilistic model of local sequence alignment, optimal alignment bit scores ("Viterbi" scores) are Gumbel-distributed with constant lambda = log 2, and the high scoring tail of Forward scores is exponential with the same constant lambda. Simulation studies support these conjectures over a wide range of profile/sequence comparisons, using 9,318 profile-hidden Markov models from the Pfam database. This enables efficient and accurate determination of expectation values (E-values) for both Viterbi and Forward scores for probabilistic local alignments.

PMID:
18516236
PMCID:
PMC2396288
DOI:
10.1371/journal.pcbi.1000069
[Indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Public Library of Science Icon for PubMed Central
    Loading ...
    Support Center