SATCHMO: sequence alignment and tree construction using hidden Markov models

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SATCHMO: Sequence Alignment and Tree Construction Using Hidden Markov Models

MOTIVATION Aligning multiple proteins based on sequence information alone is challenging if sequence identity is low or there is a significant degree of structural divergence. We present a novel algorithm (SATCHMO) that is designed to address this challenge. SATCHMO simultaneously constructs a tree and a set of multiple sequence alignments, one for each internal node of the tree. The alignment ...

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Simultaneous Sequence Alignment and Tree Construction Using Hidden Markov Models

We present a new algorithm (SATCHMO) that simultaneously estimates a tree and generates a set of multiple sequence alignments given a set of protein sequences. Alignments are constructed for each node in the tree. These alignments predict the structurally conserved elements of the sequences in a subtree and are therefore of different lengths, and represent different amino acid preferences, at d...

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Hidden Markov Models for Protein Sequence Alignment

Motivation: Protein homology detection and sequence alignment are at the basis of protein structure prediction, function prediction and evolutionary analysis. This work investigates the use of pair HMMs in pairwise protein sequence alignment. It uses a newly-written local software called HMMoc to perform the task. The resulting alignments are evaluated against the HOMSTRAD database of structural

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SATCHMO-JS: a webserver for simultaneous protein multiple sequence alignment and phylogenetic tree construction

We present the jump-start simultaneous alignment and tree construction using hidden Markov models (SATCHMO-JS) web server for simultaneous estimation of protein multiple sequence alignments (MSAs) and phylogenetic trees. The server takes as input a set of sequences in FASTA format, and outputs a phylogenetic tree and MSA; these can be viewed online or downloaded from the website. SATCHMO-JS is ...

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Multiple Alignment Using Hidden Markov Models

A simulated annealing method is described for training hidden Markov models and producing multiple sequence alignments from initially unaligned protein or DNA sequences. Simulated annealing in turn uses a dynamic programming algorithm for correctly sampling suboptimal multiple alignments according to their probability and a Boltzmann temperature factor. The quality of simulated annealing alignm...

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ژورنال

عنوان ژورنال: Bioinformatics

سال: 2003

ISSN: 1367-4803,1460-2059

DOI: 10.1093/bioinformatics/btg158