Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER.
Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER.
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DOI：
10.1186/1471-2105-6-99
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发表时间：
2005-04-15
影响因子：
3
通讯作者：
Sonnhammer EL
中科院分区：
文献类型：
--
作者：
Wistrand M;Sonnhammer EL
隐马尔可夫模型（HMM）技术是蛋白质同源性检测中最有效的方法之一。然而，成功建模的关键特征尚不完全清楚。在目前的工作中，我们通过使用两个最流行的HMM包：SAM和HMM来解决这个问题。在基于SCOP/Pfam的测试集上比较了程序构建模型和评分序列的能力。对局部和全局HMM评分分别进行了比较。使用默认设置，SAM总体上更加敏感。SAM的模型估计更优，而HMMER的模型评分更准确。然后通过比较两个包的算法选择和参数来分析模型构建的关键特征。先验概率和多重对齐计数之间的权重是SAM模型构建优越的主要解释。我们的分析表明，HMMER对序列计数的权重过大。SAM的发射先验概率也更敏感。相对序列加权方案在两个包中不同，但执行等效。SAM模型估计更敏感，HMMER模型评分更准确。通过结合两个包的最佳算法特性，与默认性能相比，准确性大大提高。
Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring. Using default settings, SAM was overall more sensitive. SAM's model estimation was superior, while HMMER's model scoring was more accurate. Critical features for model building were then analysed by comparing the two packages' algorithmic choices and parameters. The weighting between prior probabilities and multiple alignment counts held the primary explanation why SAM's model building was superior. Our analysis suggests that HMMER gives too much weight to the sequence counts. SAM's emission prior probabilities were also shown to be more sensitive. The relative sequence weighting schemes are different in the two packages but performed equivalently. SAM model estimation was more sensitive, while HMMER model scoring was more accurate. By combining the best algorithmic features from both packages the accuracy was substantially improved compared to their default performance.
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DOI：
10.1089/cmb.1995.2.9
发表时间：
1995-01-01
期刊：
Journal of computational biology : a journal of computational molecular cell biology
影响因子：
--
作者：
Eddy, S R;Mitchison, G;Durbin, R
通讯作者：
Durbin, R
影响因子：
5.6
作者：
Park, J;Karplus, K;Chothia, C
通讯作者：
Chothia, C
影响因子：
5.8
作者：
Edgar, RC;Sjölander, K
通讯作者：
Sjölander, K
影响因子：
3.9
作者：
Mitchison, G;Durbin, R
通讯作者：
Durbin, R
影响因子：
5.6
作者：
Gough, J;Karplus, K;Chothia, C
通讯作者：
Chothia, C