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The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations

机译：SAAP管道和数据库：分析影响并预测突变的致病性的工具

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BackgroundUnderstanding and predicting the effects of mutations on protein structure and phenotype is an increasingly important area. Genes for many genetically linked diseases are now routinely sequenced in the clinic. Previously we focused on understanding the structural effects of mutations, creating the SAAPdb resource.ResultsWe have updated SAAPdb to include 41% more SNPs and 36% more PDs.Introducing a hydrophobic residue on the surface, or a hydrophilic residue in the core, no longer shows significant differences between SNPs and PDs. We have improved some of the analyses significantly enhancing the analysis of clashes and of mutations to-proline and from-glycine. A new web interface has been developed allowing users to analyze their own mutations. Finally we have developed a machine learning method which gives a cross-validated accuracy of 0.846, considerably out-performing well known methods including SIFT and PolyPhen2 which give accuracies between 0.690 and 0.785.ConclusionsWe have updated SAAPdb and improved its analyses, but with the increasing rate with which mutation data are generated, we have created a new analysis pipeline and web interface. Results of machine learning using the structural analysis results to predict pathogenicity considerably outperform other methods.
机译：背景技术了解和预测突变对蛋白质结构和表型的影响是一个日益重要的领域。现在，许多遗传相关疾病的基因已在临床中常规测序。以前我们专注于理解突变的结构效应，创建了SAAPdb资源。结果我们更新了SAAPdb，使其包含的SNP增加了41％，PD增加了36％，不再在表面引入疏水性残基或在核心中引入亲水性残基显示SNP和PD之间的显着差异。我们改进了一些分析，从而显着增强了对冲突以及脯氨酸和甘氨酸突变的分析。开发了一个新的Web界面，允许用户分析自己的突变。最后，我们开发了一种机器学习方法，该方法的交叉验证精度为0.846，大大优于包括SIFT和PolyPhen2在内的众所周知的方法，其准确度在0.690至0.785之间。结论我们更新了SAAPdb并改进了其分析，但是随着SAAPdb的增加，为了生成突变数据，我们创建了一个新的分析管道和Web界面。使用结构分析结果预测致病性的机器学习结果大大优于其他方法。

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