隐马尔可夫模型
计算机科学
序列数据库
序列(生物学)
多序列比对
灵敏度(控制系统)
序列比对
算法
人工智能
模式识别(心理学)
语音识别
生物
肽序列
遗传学
工程类
电子工程
基因
作者
Michael Remmert,A. Biegert,Andreas Hauser,Johannes Söding
出处
期刊:Nature Methods
[Nature Portfolio]
日期:2011-12-25
卷期号:9 (2): 173-175
被引量:2240
摘要
HHblits is a protein sequence search tool that works by iterative pairwise comparison of profile hidden Markov models. It outperforms existing tools in terms of speed, sensitivity and alignment quality. Sequence-based protein function and structure prediction depends crucially on sequence-search sensitivity and accuracy of the resulting sequence alignments. We present an open-source, general-purpose tool that represents both query and database sequences by profile hidden Markov models (HMMs): 'HMM-HMM–based lightning-fast iterative sequence search' (HHblits; http://toolkit.genzentrum.lmu.de/hhblits/ ). Compared to the sequence-search tool PSI-BLAST, HHblits is faster owing to its discretized-profile prefilter, has 50–100% higher sensitivity and generates more accurate alignments.
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