Learning HMMs for nucleotide sequences from amino acid alignments

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Learning HMMs for nucleotide sequences from amino acid alignments

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 ...

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Sequence analysis Learning HMMs for nucleotide sequences from amino acid alignments

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 ...

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ژورنال

عنوان ژورنال: Bioinformatics

سال: 2015

ISSN: 1460-2059,1367-4803

DOI: 10.1093/bioinformatics/btv054