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• W2020150461 abstract "Progressive algorithms are widely used heuristics for the production of alignments among multiple nucleic-acid or protein sequences. Probabilistic approaches providing measures of global and/or local reliability of individual solutions would constitute valuable developments.We present here a new method for multiple sequence alignment that combines an HMM approach, a progressive alignment algorithm, and a probabilistic evolution model describing the character substitution process. Our method works by iterating pairwise alignments according to a guide tree and defining each ancestral sequence from the pairwise alignment of its child nodes, thus, progressively constructing a multiple alignment. Our method allows for the computation of each column minimum posterior probability and we show that this value correlates with the correctness of the result, hence, providing an efficient mean by which unreliably aligned columns can be filtered out from a multiple alignment." @default.
• W2020150461 created "2016-06-24" @default.
• W2020150461 creator A5000878403 @default.
• W2020150461 creator A5053385433 @default.
• W2020150461 date "2003-08-12" @default.
• W2020150461 modified "2023-09-26" @default.
• W2020150461 title "A hidden Markov model for progressive multiple alignment" @default.
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• W2020150461 doi "https://doi.org/10.1093/bioinformatics/btg193" @default.
• W2020150461 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/12912831" @default.
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