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

@article{citeulike:2919177, 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.}, author = {Eddy, Sean R.}, citeulike-article-id = {2919177}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pcbi.1000069}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18516236}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18516236}, day = {30}, doi = {10.1371/journal.pcbi.1000069}, issn = {1553-7358}, journal = {PLoS computational biology}, keywords = {computational\_biology, hmms, probabilistic\_model, sequence\_alignment}, month = {May}, number = {5}, pages = {e1000069+}, posted-at = {2009-11-06 03:27:24}, priority = {2}, publisher = {Public Library of Science}, title = {A probabilistic model of local sequence alignment that simplifies statistical significance estimation.}, url = {http://dx.doi.org/10.1371/journal.pcbi.1000069}, volume = {4}, year = {2008} }

TY - JOUR ID - citeulike:2919177 L3 - citeulike-article-id:2919177 N2 - 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. IS - 5 JF - PLoS computational biology SN - 1553-7358 TI - A probabilistic model of local sequence alignment that simplifies statistical significance estimation. PB - Public Library of Science VL - 4 SP - e1000069 KW - computational_biology KW - hmms KW - probabilistic_model KW - sequence_alignment AU - Eddy, Sean PY - 2008/05/30/ UR - http://dx.doi.org/10.1371/journal.pcbi.1000069 ER -