PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations.
PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations.
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DOI：
10.1371/journal.pcbi.1003440
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发表时间：
2014-01
影响因子：
4.3
通讯作者：
Damborsky J
中科院分区：
文献类型：
--
作者：
Bendl J;Stourac J;Salanda O;Pavelka A;Wieben ED;Zendulka J;Brezovsky J;Damborsky J
单核苷酸变异是遗传变异的一种普遍形式。编码区的突变通常与各种遗传疾病的发生有关。用于预测突变对蛋白质功能的影响的计算工具对于单核苷酸变体的分析及其实验表征的优先级非常重要。许多计算工具已经被广泛用于这一目的。不幸的是，它们的比较和进一步改进受到训练数据集和基准数据集之间的大量重叠的阻碍，这导致了有偏见和过于乐观的报告性能。在这项研究中，我们构建了三个独立的数据集，通过删除所有重复，不一致和突变以前使用的训练评估工具。使用包含超过43，000个突变的基准数据集对八种已建立的预测工具进行无偏评估：MAPP，nsSNPAnalyzer，PANTHER，PhD-SNP，PolyPhen-1，PolyPhen-2，SIFT和SNAP。将六种表现最好的工具组合成共识分类器PredictSNP，从而显著提高了预测性能，同时返回了所有突变的结果，证实了共识预测代表了单个工具提供的预测的准确和稳健的替代方案。用户友好的Web界面可以轻松访问所有八种预测工具，共识分类器PredictSNP以及来自蛋白质突变数据库和UniProt数据库的注释。网络服务器和数据集可在http://loschmidt.chemi.muni.cz/predictsnp上免费提供给学术界。
Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.
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影响因子：
14.9
作者：
Bromberg Y;Rost B
通讯作者：
Rost B
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作者：
Collins, FS;Brooks, LD;Chakravarti, A
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Chakravarti, A
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Friedman, N;Ninio, M;Pupko, T
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Pupko, T
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Capriotti, Emidio;Nehrt, Nathan L.;Bromberg, Yana
通讯作者：
Bromberg, Yana
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9.5
作者：
Castaldi, Peter J.;Dahabreh, Issa J.;Ioannidis, John P. A.
通讯作者：
Ioannidis, John P. A.