Development of new computational methods and testing their performance has to be carried out using experimental data. Only in comparison to existing knowledge can method performance be assessed. For that purpose, benchmark datasets with known and verified outcome are needed. High-quality benchmark datasets are valuable and may be difficult, laborious and time consuming to generate. VariBench and VariSNP are the two existing databases for sharing variation benchmark datasets used mainly for variation interpretation. They have been used for training and benchmarking predictors for various types of variations and their effects. VariBench was updated with 419 new datasets from 109 papers containing altogether 329 014 152 variants; however, there is plenty of redundancy between the datasets. VariBench is freely available at http://structure.bmc.lu.se/VariBench/. The contents of the datasets vary depending on information in the original source. The available datasets have been categorized into 20 groups and subgroups. There are datasets for insertions and deletions, substitutions in coding and non-coding region, structure mapped, synonymous and benign variants. Effect-specific datasets include DNA regulatory elements, RNA splicing, and protein property for aggregation, binding free energy, disorder and stability. Then there are several datasets for molecule-specific and disease-specific applications, as well as one dataset for variation phenotype effects. Variants are often described at three molecular levels (DNA, RNA and protein) and sometimes also at the protein structural level including relevant cross references and variant descriptions. The updated VariBench facilitates development and testing of new methods and comparison of obtained performances to previously published methods. We compared the performance of the pathogenicity/tolerance predictor PON-P2 to several benchmark studies, and show that such comparisons are feasible and useful, however, there may be limitations due to lack of provided details and shared data. Database URL: http://structure.bmc.lu.se/VariBench.

中文翻译：

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变体基准数据集：更新，标准，质量和应用程序。

必须使用实验数据来开发新的计算方法并测试其性能。只有与现有知识相比，才能评估方法的性能。为此，需要具有已知和经过验证的结果的基准数据集。高质量的基准数据集很有价值，生成起来可能很困难，费力且耗时。VariBench和VariSNP是两个用于共享主要用于变异解释的变异基准数据集的现有数据库。它们已用于对各种类型的变化及其影响进行预测和基准预测。VariBench更新了109篇论文中的419个新数据集，共包含329 014 152个变体；但是，数据集之间有很多冗余。VariBench可从http://structure.bmc.lu免费获得。se / VariBench /。数据集的内容取决于原始来源中的信息而有所不同。可用数据集已分类为20个组和子组。有用于插入和删除，编码和非编码区域中的替换，结构映射，同义词和良性变体的数据集。特效数据集包括DNA调控元件，RNA剪接以及用于聚集，结合自由能，无序和稳定性的蛋白质特性。然后有几个针对分子和疾病特定应用的数据集，以及一个针对变异表型效应的数据集。变异体通常在三个分子水平（DNA，RNA和蛋白质）上进行描述，有时甚至在蛋白质结构水平上进行描述，包括相关的交叉引用和变异体描述。更新的VariBench有助于开发和测试新方法，并将获得的性能与以前发布的方法进行比较。我们将致病性/耐受性预测因子PON-P2的性能与几个基准研究进行了比较，并表明这种比较是可行和有用的，但是由于缺少提供的详细信息和共享数据，因此可能存在局限性。数据库URL：http：//structure.bmc.lu.se/VariBench。