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• W1996112765 endingPage "e1000069" @default.
• W1996112765 startingPage "e1000069" @default.
• W1996112765 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." @default.
• W1996112765 created "2016-06-24" @default.
• W1996112765 creator A5020118397 @default.
• W1996112765 date "2008-05-30" @default.
• W1996112765 modified "2023-10-11" @default.
• W1996112765 title "A Probabilistic Model of Local Sequence Alignment That Simplifies Statistical Significance Estimation" @default.
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• W1996112765 doi "https://doi.org/10.1371/journal.pcbi.1000069" @default.
• W1996112765 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/2396288" @default.
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• W1996112765 hasPublicationYear "2008" @default.
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