Deleterious SNP prediction: be mindful of your training data!
Deleterious SNP prediction: be mindful of your training data!
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DOI：
10.1093/bioinformatics/btl649
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发表时间：
2007-03-15
期刊：
影响因子：
5.8
通讯作者：
Westhead, David R.
中科院分区：
文献类型：
--
作者：
Care, Matthew A.;Needham, Chris J.;Westhead, David R.
动机：预测人类单核苷酸多态性（single nucleotide polymorphisms，SNPs）中哪些对基因功能有害或可能与疾病相关是一个重要的问题，文献中已报道了许多方法。所有方法都需要分类为“有害”或“中性”的突变数据集用于训练和/或验证。虽然不同的工作人员使用不同的数据集，但没有研究哪一个是最好的。在这里，分析了三个最常用的数据集。我们检查他们的内容，并与此相关的分类器，目的是揭示每个数据集的优势和缺陷，并建议一个最好的方法为今后的studies.Results：检查的数据集被证明是实质上不同的内容，特别是关于氨基酸取代，反映了不同的方式，它们是派生的。这导致了分类器的差异，并揭示了一些数据集的一些严重缺陷，使它们对于非同义SNP预测不太理想。
Motivation: To predict which of the vast number of human single nucleotide polymorphisms (SNPs) are deleterious to gene function or likely to be disease associated is an important problem, and many methods have been reported in the literature. All methods require data sets of mutations classified as 'deleterious' or 'neutral' for training and/or validation. While different workers have used different data sets there has been no study of which is best. Here, the three most commonly used data sets are analysed. We examine their contents and relate this to classifiers, with the aims of revealing the strengths and pitfalls of each data set, and recommending a best approach for future studies.Results: The data sets examined are shown to be substantially different in content, particularly with regard to amino acid substitutions, reflecting the different ways in which they are derived. This leads to differences in classifiers and reveals some serious pitfalls of some data sets, making them less than ideal for non-synonymous SNP prediction.