A Probabilistic Model of Local Sequence Alignment That Simplifies Statistical Significance Estimation

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, optim...

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Veröffentlicht in:PLoS computational biology Jg. 4; H. 5; S. e1000069
1. Verfasser: Eddy, Sean R.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 01.05.2008
Public Library of Science (PLoS)
Schlagworte:
Algorithms
Base Sequence
Chromosome Mapping - methods
Computational Biology/Protein Homology Detection
Computer Simulation
Data Interpretation, Statistical
Dynamic programming
Mathematics/Statistics
Models, Genetic
Models, Statistical
Molecular Sequence Data
Sequence Alignment - methods
Sequence Analysis, DNA - methods
Simulation
Studies
Data Interpretation, Statistical
Sequence Alignment
Algorithms
Base Sequence
Computer Simulation
Molecular Sequence Data
Chromosome Mapping
Models, Genetic
Models, Statistical
Sequence Analysis, DNA
ISSN:1553-7358, 1553-734X, 1553-7358
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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Conceived and designed the experiments: SE. Performed the experiments: SE. Analyzed the data: SE. Contributed reagents/materials/analysis tools: SE. Wrote the paper: SE.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1000069