a probabilistic model of local sequence alignment that simplifies statistical significance estimation当地的序列比对的概率模型,简化了统计显著性估计.pdfVIP

A Probabilistic Model of Local Sequence Alignment That Simplifies Statistical Significance Estimation Sean R. Eddy* Howard Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, Virginia, United States of America 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 (l) 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 l = log 2, and the high scoring tail of Forward scores is exponential with the same constant l. 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. Citation: Eddy SR (2008) A Probabilistic Model of Local Sequence Alignment That Simplifies Statistical Significance Estimation. PLoS Comput Biol 4(5): e1000069. doi:10.1371/journal.pcbi.1000069 Editor: Burkhard Rost, Columbia University, United States of America Received December 5, 2007; Accepted March 26, 2008; Published May 30, 2008 Copyright: 2008 Sean Eddy. This is an open-access article distributed under the terms of the Creative Commo