Title: Learning HMMs for nucleotide sequences from amino acid alignments
Authors: Fischer, Carlos ×
Carareto, Claudia
dos Santos, Renato
Cerri, Ricardo
Costa, Eduardo
Schietgat, Leander
Vens, Celine #
Issue Date: 1-Jun-2015
Publisher: Oxford University Press
Series Title: Bioinformatics vol:31 issue:11 pages:1836-1838
Abstract: 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 alignment, after which an NT-based HMM is trained to be applied directly on a genome.
ISSN: 1367-4803
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
Public Health and Primary Care, Campus Kulak Kortrijk
Department of Public Health miscellaneous
× corresponding author
# (joint) last author

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