Learning HMMs for nucleotide sequences from amino acid alignments
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Data
2015-06-01
Autores
Fischer, Carlos Norberto [UNESP]
Carareto, Claudia Marcia [UNESP]
Santos, Renato Augusto Corrêa dos [UNESP]
Cerri, Ricardo
Costa, Eduardo
Schietgat, Leander
Vens, Celine
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Oxford Univ Press
Resumo
Profile hidden Markov models (profile HMMs) are known to efficiently predict whether an amino acid (AA) sequence belongs to a specific protein family. Profile HMMs can also be used to search for protein domains in genome sequences. In this case, HMMs are typically learned from AA sequences and then used to search on the six-frame translation of nucleotide (NT) sequences. However, this approach demands additional processing of the original data and search results. Here, we propose an alternative and more direct method which converts an AA alignment into an NT one, after which an NT-based HMM is trained to be applied directly on a genome.
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Bioinformatics. Oxford: Oxford Univ Press, v. 31, n. 11, p. 1836-1838, 2015.