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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Learning HMMs for nucleotide sequences from amino acid alignments

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Author(s):
Fischer, Carlos N. [1] ; Carareto, Claudia M. A. [2] ; dos Santos, Renato A. C. [3] ; Cerri, Ricardo [4] ; Costa, Eduardo [5] ; Schietgat, Leander [6] ; Vens, Celine [7]
Total Authors: 7
Affiliation:
[1] UNESP Sao Paulo State Univ, Dept Stat Appl Maths & Comp Sci, Rio Claro, SP - Brazil
[2] UNESP Sao Paulo State Univ, Dept Biol, Sao Jose Do Rio Preto, SP - Brazil
[3] UNESP Sao Paulo State Univ, Inst Biosci, Rio Claro, SP - Brazil
[4] UFSCar Univ Fed Sao Carlos, Dept Comp Sci, Sao Carlos, SP - Brazil
[5] Univ Sao Paulo, Dept Comp Sci, Sao Carlos, SP - Brazil
[6] Katholieke Univ Leuven, Dept Comp Sci, Leuven - Belgium
[7] KU Leuven Kulak, Dept Publ Hlth & Primary Care, Kortrijk - Belgium
Total Affiliations: 7
Document type: Journal article
Source: Bioinformatics; v. 31, n. 11, p. 1836-1838, JUN 1 2015.
Web of Science Citations: 1
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 one, after which an NT-based HMM is trained to be applied directly on a genome. (AU)

FAPESP's process: 12/24774-2 - Hidden Markov Models applied to transposable elements
Grantee:Carlos Norberto Fischer
Support type: Scholarships abroad - Research
FAPESP's process: 10/10731-4 - Evolutionary dynamics and transposable elements regulation in Drosophila populations and species
Grantee:Claudia Marcia Aparecida Carareto
Support type: Regular Research Grants