2006
DOI: 10.1093/bioinformatics/btl472
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Probalign: multiple sequence alignment using partition function posterior probabilities

Abstract: Open source code implementing Probalign as well as for producing the simulated data, and all real and simulated data are freely available from http://www.cs.njit.edu/usman/probalign

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Cited by 207 publications
(175 citation statements)
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“…Alignments for each of the one-to-one rooted core genes (593 orthologs) were generated with one outgroup sequence in the alignment, which, whenever possible, was Arcrobacter. The sequences were first aligned at the amino acid level using Probalign (v1.1) (Roshan and Livesay 2006), then backtranslated to DNA, and alignment columns with a posterior probability <0.6 were removed. Alignments with >50% of the sites removed were discarded from the analysis, resulting in 571 alignments.…”
Section: Methodsmentioning
confidence: 99%
“…Alignments for each of the one-to-one rooted core genes (593 orthologs) were generated with one outgroup sequence in the alignment, which, whenever possible, was Arcrobacter. The sequences were first aligned at the amino acid level using Probalign (v1.1) (Roshan and Livesay 2006), then backtranslated to DNA, and alignment columns with a posterior probability <0.6 were removed. Alignments with >50% of the sites removed were discarded from the analysis, resulting in 571 alignments.…”
Section: Methodsmentioning
confidence: 99%
“…Computing match probabilities via a statistical mechanics model has been introduced for sequence-based pairwise alignment by Probalign (Roshan and Livesay 2006). However, the analogous approach has not been considered for structure-based multiple alignment.…”
Section: Match Probabilitiesmentioning
confidence: 99%
“…This suggests that, rather than using a standard maximum-likelihood approach such as the Viterbi algorithm, posteriors could be profitably used to identify good alignments. Posterior decoding-alignment algorithms were proposed some time ago (Krogh 1997;Durbin et al 1998;Holmes and Durbin 1998), and recently there has been a renewed interest in probabilistic alignment algorithms, mostly focusing on proteins (Do et al 2005;Kall et al 2005;Roshan and Livesay 2006;Paten and Birney 2007), and similar approaches were found to improve RNA folding (Ding et al 2005). In contrast, the performance of posterior decoding algorithms on genomic DNA sequences has, to our knowledge, not been investigated in detail before.…”
mentioning
confidence: 99%