Variation Benchmark Datasets: Update, Criteria, Quality and Applications

Abstract

ABSTRACTDevelopment of new computational methods and testing their performance has to be done on 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. They have been used for training and benchmarking predictors for various types of variations and their effects. There are 419 new datasets from 109 papers containing altogether 329003373 variants; however there is plenty of redundancy between the datasets. VariBench is freely available athttp://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 predictions 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 performance to previously published methods. We compared the performance of the pathogenicity/tolerance predictor PON-P2 to several benchmark studies, and showed that such comparisons are feasible and useful, however, there may be limitations due to lack of provided details and shared data.AUTHOR SUMMARYA prediction method performance can only be assessed in comparison to existing knowledge. 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. We collected variation datasets from literature, website and databases. There are 419 separate new datasets, which however contain plenty of redundancy. VariBench is freely available athttp://structure.bmc.lu.se/VariBench/. 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 predictions 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. The updated VariBench facilitates development and testing of new methods and comparison of obtained performance to previously published methods. We compared the performance of the pathogenicity/tolerance predictor PON-P2 to several benchmark studies and showed that such comparisons are possible and useful when the details of studies and the datasets are shared.