Improving profile HMM discrimination by adapting transition probabilities
Improving profile HMM discrimination by adapting transition probabilities
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DOI：
10.1016/j.jmb.2004.03.023
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发表时间：
2004-05-07
影响因子：
5.6
通讯作者：
Sonnhammer, ELL
中科院分区：
文献类型：
--
作者：
Wistrand, M;Sonnhammer, ELL
隐马尔可夫模型(HMM)用于对蛋白质家族进行建模，并用于检测蛋白质之间的进化关系。这种简档HMM通常由一组相关序列的多重比对构成。HMM中的转移概率参数被用来对比对中的插入和删除进行建模。我们在这里表明，在估计转移概率参数时考虑无关序列有助于为全局/局部比对模式构建更具区分性的模型。在正常HMM训练之后，采用简单的启发式方法，其根据所观察到的训练集中相对于不相关(噪声)集的转移来调整匹配和删除状态之间的转移概率。该方法被称为自适应转移概率(ATP)，并且基于HMMER包实现。它在基于Pfam和SCOP分类的两个远程同源性测试中进行了基准测试。与HMMER默认程序相比，在两个测试中以及所有级别的错误率中，错别率都显著降低。(C)2004爱思唯尔有限公司。保留所有权利。
Profile hidden Markov models (HMMs) are used to model protein families and for detecting evolutionary relationships between proteins. Such a profile HMM is typically constructed from a multiple alignment of a set of related sequences. Transition probability parameters in an HMM are used to model insertions and deletions in the alignment. We show here that taking into account unrelated sequences when estimating the transition probability parameters helps to construct more discriminative models for the global/local alignment mode. After normal HMM training, a simple heuristic is employed that adjusts the transition probabilities between match and delete states according to observed transitions in the training set relative to the unrelated (noise) set. The method is called adaptive transition probabilities (ATP) and is based on the HMMER package implementation. It was benchmarked in two remote homology tests based on the Pfam and the SCOP classifications. Compared to the HMMER default procedure, the rate of misclassification was reduced significantly in both tests and across all levels of error rate. (C) 2004 Elsevier Ltd. All rights reserved.