< Terug naar vorige pagina
Publicatie
Learning HMMs for nucleotide sequences from amino acid alignments
Tijdschriftbijdrage - Tijdschriftartikel
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.
Tijdschrift: Bioinformatics
ISSN: 1367-4803
Issue: 11
Volume: 31
Pagina's: 1836 - 1838
Jaar van publicatie:2015
BOF-keylabel:ja
IOF-keylabel:ja
BOF-publication weight:10
CSS-citation score:1
Auteurs:International
Authors from:Higher Education
Toegankelijkheid:Open