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HMM-Kalign:一种用于生成次优隐马尔可夫模型比对的工具。

HMM-Kalign: a tool for generating sub-optimal HMM alignments.

作者信息

Becker Emmanuelle, Cotillard Aurélie, Meyer Vincent, Madaoui Hocine, Guérois Raphaël

机构信息

CEA, iBiTecS, URA 2096, SBSM, Laboratoire de Biologie Structurale et Radiobiologie, Gif sur Yvette, F-91191 France.

出版信息

Bioinformatics. 2007 Nov 15;23(22):3095-7. doi: 10.1093/bioinformatics/btm492. Epub 2007 Oct 6.

Abstract

Recent development of strategies using multiple sequence alignments (MSA) or profiles to detect remote homologies between proteins has led to a significant increase in the number of proteins whose structures can be generated by comparative modeling methods. However, prediction of the optimal alignment between these highly divergent homologous proteins remains a difficult issue. We present a tool based on a generalized Viterbi algorithm that generates optimal and sub-optimal alignments between a sequence and a Hidden Markov Model. The tool is implemented as a new function within the HMMER package called hmmkalign.

摘要

最近,利用多序列比对(MSA)或序列谱来检测蛋白质之间远距离同源性的策略取得了进展,这使得能够通过比较建模方法生成结构的蛋白质数量显著增加。然而,预测这些高度分化的同源蛋白质之间的最佳比对仍然是一个难题。我们提出了一种基于广义维特比算法的工具,该工具可生成序列与隐马尔可夫模型之间的最优和次优比对。该工具作为HMMER软件包中的一个新函数来实现,名为hmmkalign。

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