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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Main Author:	Sean R Eddy
Format:	article
Language:	EN
Published:	Public Library of Science (PLoS) 2008
Subjects:	Biology (General) QH301-705.5
Online Access:	https://doaj.org/article/5dfd88e407dc49c6b637624e120208de
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id	oai:doaj.org-article:5dfd88e407dc49c6b637624e120208de
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spelling	oai:doaj.org-article:5dfd88e407dc49c6b637624e120208de2021-11-25T05:41:16ZA probabilistic model of local sequence alignment that simplifies statistical significance estimation.1553-734X1553-735810.1371/journal.pcbi.1000069https://doaj.org/article/5dfd88e407dc49c6b637624e120208de2008-05-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18516236/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Sequence 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.Sean R EddyPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 4, Iss 5, p e1000069 (2008)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Sean R Eddy A probabilistic model of local sequence alignment that simplifies statistical significance estimation.
description	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.
format	article
author	Sean R Eddy
author_facet	Sean R Eddy
author_sort	Sean R Eddy
title	A probabilistic model of local sequence alignment that simplifies statistical significance estimation.
title_short	A probabilistic model of local sequence alignment that simplifies statistical significance estimation.
title_full	A probabilistic model of local sequence alignment that simplifies statistical significance estimation.
title_fullStr	A probabilistic model of local sequence alignment that simplifies statistical significance estimation.
title_full_unstemmed	A probabilistic model of local sequence alignment that simplifies statistical significance estimation.
title_sort	probabilistic model of local sequence alignment that simplifies statistical significance estimation.
publisher	Public Library of Science (PLoS)
publishDate	2008
url	https://doaj.org/article/5dfd88e407dc49c6b637624e120208de
work_keys_str_mv	AT seanreddy aprobabilisticmodeloflocalsequencealignmentthatsimplifiesstatisticalsignificanceestimation AT seanreddy probabilisticmodeloflocalsequencealignmentthatsimplifiesstatisticalsignificanceestimation
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A probabilistic model of local sequence alignment that simplifies statistical significance estimation.

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