DATA REPORT article

Front. Bioinform., 19 September 2023

Sec. Genomic Analysis

Volume 3 - 2023 | https://doi.org/10.3389/fbinf.2023.1248732

VariBench, new variation benchmark categories and data sets

  • Department of Experimental Medical Science, Lund University, Lund, Sweden

1 Introduction

Genetic variation data is nowadays easy to generate. Variation interpretation means the description of the significance of variations, often in relation to disease. This is substantially more difficult a problem than sequence generation. Experimental methods provide verified interpretations; however, due to huge amounts of variations in every individual, computational approaches are widely used. The length of human genome is over 3 billion base pairs (Nurk et al., 2022). Due to individual genetic heterogeneity, 4.1–5.0 million sites differ from the reference genome (Auton et al., 2015). Various types of prediction methods are widely used to interpret the variations, see (Niroula and Vihinen, 2016). Benchmark studies have indicated large differences in the performance of methods developed for the same type of variation prediction tasks, see e.g., (Thusberg et al., 2011; Niroula and Vihinen, 2019; Zhang et al., 2019; Marabotti et al., 2021; Anderson and Lassmann, 2022). Both predictor development and performance assessment are largely dependent on high-quality data. One might think that there is a large number of verified variations as the genetic diagnosis is widely applied; however, that is not the case, especially when considering specific types of variations or mechanisms.

The development and testing of computational methods are dependent on experimental data. Accurate prediction methods can be developed only with reliable experimentally verified cases with a systematic approach and using relevant measures (Vihinen, 2012; Vihinen, 2013). Method performance has to be assessed in comparison to existing knowledge. For that purpose, benchmark data sets with known and verified outcomes are needed. Such data sets can be time-consuming and costly to collect and require many manual steps. Therefore, it is important that the produced data are distributed and reused.

In the variation interpretation field, two databases deliver such data sets. VariBench (Nair et al., 2013; Sarkar et al., 2020) and VariSNP (Schaafsma et al., 2015) contain variation benchmark data. VariSNP is a version of the dbSNP database (Sherry et al., 2001) for short variations from where known disease-causing variants have been filtered away. VariBench is a generic database that contains all types of variations with all kinds of effects. These resources have been widely used for prediction method training and testing.

What requirements and criteria should benchmark data sets fulfill in relation to variation interpretation and in general? We have defined five criteria, discussed in (Nair et al., 2013). They include relevance, representativeness, non-redundancy, inclusion of both positive and negative cases and reusability. VariBench subscribes to the criteria and collects data sets and distributes them freely. VariBench data sets are frequently used to train and test method performance. These sets facilitate also post-publication comparison of methods to published benchmarks (Sarkar et al., 2020).

The bottleneck in sequencing projects has shifted from sequencing to interpretation of obtained results. Experimental studies of variant effects are the gold standard approaches. They are not feasible in many instances and therefore, various computational approaches have been developed. We divide the prediction methods into five categories in VariBench.

First, pathogenicity, also called tolerance, predictions aim to identify disease-related alterations of various types (for details see Table 1).These methods aim just to detect harmful or disease-related variants. Second, effect-specific methods are for the prediction of various effects at DNA, RNA and protein levels. Third, there are also predictors specific for certain molecules or families of molecules, typically for proteins. Fourth, some methods are dedicated to certain diseases. Fifth, some tools predict the phenotype, typically the severity of the variant effect.

TABLE 1

Data setData sets in previous versionNew data sets
Variation type data sets
Insertions and deletions42
Substitutions coding region
Training data sets239
Test data sets53
Structure mapped variations
General structural data sets23
Transmembrane protein data sets04
Synonymous and unsense variants25
Benign variants20
Structural variants01
Effect specific data sets
DNA regulatory elements74
RNA splicing156
Protein aggregation20
Binding free energy21
Protein disorder11
Protein solubility11
Protein stability319
Single variants219
Double variants10
Protein folding rate05
Protein binding affinity
Generic protein-protein interactions113
Antibody-antigen affinity changes05
Protein-nucleic acid interactions07
Functional effects
Gain of function variants01
Deep mutational data sets07
Molecule-specific data sets187
Disease-specific data sets
Cancer variation data sets44
Other diseases82
Phenotype data sets11

Types of data sets in VariBench.

High-quality variation data sets are difficult and laborious to generate. VariBench collects, organizes, and integrates additional information and distributes different types of variation data sets. It is a unique database. We have updated the resource with 143 new data sets, which include more than 90 million variants. During the update, some new categories of variations and effects have been included. There are currently variations in 5 main categories, 17 subgroups and 11 groups.

2 Data sets and quality

VariBench collects from literature, databases and predictors data sets, which have been used to train methods or assess their performance. There are no selection criteria for the inclusion of data sets. This is because of several reasons. The data sets can be used as such, or they can be further cleaned and pruned to use in additional tasks, be extended with new cases, etc. A good benchmark data set should fulfill several requirements (Vihinen, 2012; Vihinen, 2013), including good coverage, representativeness and containing both positive and negative cases that are experimentally determined. The representativeness of amino acid substitution data sets was investigated (Schaafsma and Vihinen, 2018) and found not to be optimal.

The quality of data sets in VariBench is variable. We include even known low-quality data sets, since they may be valuable when building new data sets and for other applications. We have performed some quality tests, including consistency; however, it is the duty of the users of the data to evaluate whether the data are suitable for intended use. One of the goals of VariBench is to provide existing data sets, even when problematic, e.g., for comparative purposes.

Systematics is an integral part of data and database quality. It is quite common that due to errors and lack of systematics, all variants in an existing data set cannot be reused as they cannot be mapped to reference sequences.

An example of the importance of data quality is in the field of protein stability predictions. Most of the existing predictors are based on a single database, ProTherm, which was shown to contain numerous problems (Yang et al., 2018). Recently, new and higher-quality databases have emerged in this field (Stourac et al., 2021; Turina et al., 2021).

3 Uses of VariBench data

VariBench data sets have been widely used especially to train and test variation interpretation predictors (pathogenicity/tolerance, protein stability, solubility, melting temperature, gene/protein/disease-specific predictors, and interaction and structural effects on folded and disordered regions and proteins), but also in the benchmarking performance of tools for various types and effects. In addition to human, plant and animal-related predictors and benchmarks have benefitted from VariBench (Yang et al., 2022). The data has also facilitated the interpretation of variants according to the guidelines of American College of Medical Genetics and Genomics, and the Association for Molecular Pathology (ACMG/AMP) (Richards et al., 2015) and benchmarking such annotations.

4 Data sets in VariBench

VariBench contains now 559 files for separate data sets from 295 studies and covers a wide range of variations (Tables 1, 2). The data sets were collected from literature, websites and databases. They have been used for predictive purposes, most often to develop novel predictors for different types or effects of variants. Some data sets have been specifically collected for benchmarking purposes.

TABLE 2

Origin of dataaDataset first used forNumber of variants in each datasetNumber of different genes, transcripts or proteins in each datasetReferences
Variation type datasets
Insertions and deletions
HGMD, gnomADMutPredIndel231963, 4679, 12033556, 4679, 802Pagel et al. (2019)
HGMD, gnomADMutPredLof98095, 884013648, 1239Pagel et al. (2019)
Substitutions, coding region
Training datasets
VariBenchPON-All45573, 306, 5360, 324, 3836, 1109, 48176, 415414765, 232, 1261, 233, 704, 287, 13383, 1149Yang et al. (2022)
HumDiv, HumVar, MGI, Disease Ontology Database, OMIA, UniProtKB, EnsemblMammalian diseases377, 207, 62131, 315, 51Plekhanova et al. (2019)
http://www.arabidopsis.org, UniProt/Swiss-Prot, EnsemblArabidopsis thaliana13707999Kono et al. (2018)
UniProt, SwissProtArabidopsis4410994Kovalev et al. (2018)
HGMD, SwissVar, dbSNPMutPred220643Pejaver et al. (2020)
ClinVar, UniProtDeepSav43000, 430003386, 10974Pei et al. (2020)
dbNSFP, ClinVar, HumsaVar, HGMDVARITY157708, 1577083912, 3912Wu et al. (2021)
ClinVar, gnomADMutScore66037Quinodoz et al. (2022)
HGMD, gnomADMutFormer69159160Jiang et al. (2021)
Test datasets
ClinVar, HGMD, OMIM, gnomADBenchmarking with clinical data set1757Gunning et al. (2021)
ClinVar, VariBenchBenchmarking study35167, 291733349, 8562Anderson and Lassmann (2022)
ClinVarRett syndrome benchmark43543217Ganakammal and Alexov (2019)
Structure mapped variants
General structural datasets
ClinVar, ExAC, HumsaVarMissense3D1965, 2134Ittisoponpisan et al. (2019)
UniProtProtein structural analysis6025, 45363782, 8211Gao et al. (2015)
HumsaVarSolvent accessibility10760, 693851283, 12494Savojardo et al. (2020)
Transmembrane proteins
VariBench, ExACTransmembrane protein analysis2058, 5422, 508, 1289, 1289870, 5422, 508, 1289, 1289Orioli and Vihinen (2019)
PDBmCSM-membrane347, 138/38, 16Pires et al. (2020)
ClinVar, gnomADTMSNP2624, 196 705Garcia-Recio et al. (2021)
BorodaTM, PredMutHTP, TMSNPMutTMPredictor21379, 10031, 3706, 7374, 5463341, 2114, 1183, 1848, 62Ge et al. (2021)
Synonymous and unsense variations
1KGPSilva33Buske et al. (2013)
Silva, OMIMTraP75376, 96, 102Gelfman et al. (2017)
HGMD, dbDSMusDSM239358, 2400, 4502, 665, 5085Tang et al. (2021)
ClinVarEnsemble predictor243, 243Ganakammal and Alexov (2020)
1KGP, ExAC, gnomAD, generated dataPredictor review1048576Zeng and Bromberg (2019)
Structural variations
ClinVar, gnomAD, ape sequences, 1KGPStrVCTVRE76695119Sharo et al. (2022)
Effect-specific datasets
DNA regulatory elements
DNaseI-seq, ChIP-seq datadeltaSVM45Lee et al. (2015)
dbSNP, ClinVar, OMIMncVarDB7228, 722Biggs et al. (2020)
PRVCS, 1KGP, GTEx, GWAS catalogueregBase108, 67635, 796, 60393, 21725, 3105, 102, 7513, 61170, 5023, 11436, 61170Zhang et al. (2019)
HGMD, ClinVar, OregAnno, GWAS catalogWEVar2874, 29Wang et al. (2021)
RNA splicing
BICEX-SKIP and HOT-SKIP74, 42Raponi et al. (2011)
ClinVar, literatureSQUIRLS8322Danis et al. (2021)
ClinVar, literature, InSiGHTCancer gene analysis12, 347, 183, 32, 13Moles-Fernández et al. (2018)
HGMD, SpliceDisease, DBASSscdbNSFP2959, 45Jian et al. (2014)
Experimental dataSPiCE142, 163,902, 2, 9Leman et al. (2018)
ClinVarCADD-Splice1688852, 14011296, 1688852, 14011296Rentzsch et al. (2021)
Binding free energy
Skempi, literatureSAAMBE2041, 132781, 43Petukh et al. (2016)
Protein disorder
SwissProt, VariBenchIDRMutPred3348, 559, 5794, 5027321, 26, 2562, 2390Zhou et al. (2020)
Protein solubility
VariBench, literaturePON-Sol25666, 46, 66266, 9, 34Yang et al. (2021)
Protein stability
Single variants
ProThermPreTherMut836, 2530Tian et al. (2010)
ProThermiStable3131Chen et al. (2013)
Experimental dataCAGI frataxin benchmark8Strokach et al. (2021)
ProThermiStable21564, 1495, 759, 265, 363, 129Chen et al. (2020)
VariBench, ProtThermBenchmarking study1024Marabotti et al. (2021)
ProThermThermonet3214, 3214, 3214, 1744, 1744, 1744148, 148, 148, 127, 127, 127Li et al. (2020)
ProTherm, literatureACDC-NN[2197, 2050, 2046, 2231, 2042, 2094, 2300, 1933, 2007, 2284] [268, 183, 415, 187, 230, 376, 178, 170, 545, 96] [183, 415, 187, 230, 376, 178, 170, 545, 96, 268] [5, 199, 21, 75, 7, 1, 33 ] [5, 1, 199, 21, 75, 7, 1, 33] [1013, 813, 924, 1080, 1157, 1296, 1219, 1235, 1180] [268, 176, 398, 65, 143, 164, 66, 25, 143, 9] [176, 398, 65, 143, 164, 66, 25, 143, 9, 198][104, 107, 105, 103, 103, 103, 107, 111, 109, 104] [15, 13, 12, 15, 14, 15, 14, 11, 10, 13] [13, 12, 15, 14, 15, 14, 11, 10, 13, 15] [1, 4, 2, 2, 2, 1, 2] [5, 1, 199, 21, 75, 7, 1, 33] [63, 60, 60, 55, 56, 65, 65, 69, 69] [16, 7, 11, 7, 9, 14, 8, 5, 8, 1] [7, 11, 7, 9, 14, 8, 5, 8, 1, 8]Benevenuta et al. (2021)
ThermoMutDB, ProTherm, VariBenchBenchmarking study352Pancotti et al. (2022)
Protein folding rate
Experimental dataKinetic data806Naganathan and Muñoz (2010)
Literature, PFD, kineticDBKD-FREEDOM46715, 4Huang and Gromiha (2010)
PFD, kineticDBFora467, 154Huang and Gromiha (2012)
PFD, kineticDB, literatureFREEDOM467Huang (2014)
LiteratureUnfoldingRaCe and FoldingRaCe790, 16, 6026, 10, 5Chaudhary et al. (2015),Chaudhary et al. (2016)
Protein interaction
Generic protein-protein interactions
LiteratureCC/PBSA582, 5929, 57Benedix et al. (2009)
SKEMPI, literatureProtein-protein binding affinity123, 242, 574,18445, 9, 29, 81Li et al. (2014)
SKEMPIMutaBind1925Li et al. (2016)
SKEMPIBindProfX1 402Xiong et al. (2017)
DACUM, SKEMPI, literatureiSEE1102Geng et al. (2019)
SKEMPI, ABbind, PROXiMATE, dbMPIKTmCSM-PPI24196, 378319, 19Rodrigues et al. (2019)
SKEMPI, literatureMutaBind24191, 1707319, 19Zhang et al. (2020)
SKEMPI, CAPRISSIPe1470, 734, 888, 190, 152319, 19Huang et al. (2020)
SKEMPINetTree645, 1131, 4947, 4169, 8338, 78729, 112, 319, 319, 319, 21Wang et al. (2020)
PROXiMATEProAffiMuSeq1061, 112104, 53Jemimah et al. (2020)
ClinVar, ProTherm, SKEMP, literatureELASPIC216189, 256314227, 2378Strokach et al. (2019)
SKEMPImmCSM-PPI1340, 595, 272296, 68, 24Rodrigues et al. (2021)
TCGA, ICGCe-MutPath59712Li et al. (2021a)
Antibody-antigen affinity
AB-BindmCSM-AB558Pires and Ascher (2016)
LiteratureSiPMAB212Sulea et al. (2016)
LiteratureFree energy perturbation method200Clark et al. (2019)
SiPMABConsensus predictor46Kurumida et al. (2020)
AB-BIND, PROXiMATE, SKEMPImCSM-AB21810Myung et al. (2020)
Protein-nucleic acid interactions
ProNITmCSM-NA662369Pires and Ascher (2017)
ProNITSAMPDI10413Peng et al. (2018)
ProNIT, dbAMEPNIPremPDI21949Zhang et al. (2018)
ENCODE, POSTAR2DeepClip8132Grønning et al. (2020)
dbAMEMPNIiPNHOT293105Zhu et al. (2020)
ProNIT, dbAMEMPNISAMPDI-3D101, 463, 200, 419, 22726, 30, 49, 96, 18Li et al. (2021b)
PDB, litertureNabe2506473Liu et al. (2021)
Functional effects
Gain of function data sets
LiteraturefuNCion3794, 6930Heyne et al. (2020)
Deep mutational data sets
LiteratureDeepSequence71221831Riesselman et al. (2018)
LiteraturefuNTRp303, 75, 102, 286, 56Miller et al. (2019)
LiteratureFunctional effects183204Reeb et al. (2020)
LiteratureDeep mutational landscape6357, 6357Dunham and Beltrao (2021)
LiteratureBenchmarking study23003310Livesey and Marsh (2020)
LiteratureLacI102, 43031, 1Miller et al. (2017)
LiteratureLiver pyruvate kinase1261Martin et al. (2020)
Molecule-specific data sets
CFTR-MetaPred1899, 1210Rychkova et al. (2017)
LiteratureCYSMA141Sasorith et al. (2020)
SwissProt, BTKbaseKinMutRF3689459Pons et al. (2016)
SwissVar, HumsaVar, Ensembl Variation, ClinVarCardiac sodium channel variants13921Tarnovskaya et al. (2020)
LiteratureSCN9A variants851Toffano et al. (2020)
LiteratureTroponin variants1361Shakur et al. (2021)
Literature, ClinVar, HGMDIDUA1471Borges et al. (2021)
Disease-specific data sets
Cancer variation data sets
LiteraturedbCID57, 153, 72822, 39, 46Yue et al. (2019)
LiteraturedbCPM108, 863, 110911, 71, 130Yue et al. (2018)
ICGC, TCGA, Pediatric Cancer Genome ProjectMutaGene527658Goncearenco et al. (2017)
UMD_TP53, TP53MULTLOADTP53_PROF1362, 12951, 1Ben-Cohen et al. (2022)
Other diseases
ClinVar, gnomAD, literatureCardioBoost1237, 215, 154, 308, 532 218, 289, 2003,2578 218, 289, 2003, 2578 347, 463, 170 106, 106, 35 157, 227, 75 157, 227, 757, 6,6,7, 9 16, 16, 16, 21 16, 16, 16, 21 12, 8, 11 1, 1, 1 1, 1, 1 1, 1, 1Zhang et al. (2021)
HGMD, dbSNPSteroid metabolism diseases79712Chan (2013)
COSMICBenchmarking cancer variants16411Petrosino et al. (2021)
Phenotype data sets
ClinVarVusPrize45749, 25080, 684, 4843, 510912106, 1615, 244, 1239, 2828Mahecha et al. (2022)

New data sets in VariBench.

a

Abbreviations: 1KGP, thousand genomes project; HGMD, human gene mutation database; ICGC, international cancer genome consortium; PDB, protein data bank; TCGA, the cancer genome atlas.

There are 247 new data files that contain total 90,886,959 variants. Together with previous versions, there are 105,181,219 variants, the increase is more than seven-fold from the original number of 14,294,260 variants. The number of data sets is high because many articles contain more than one data set. Many of the data sets are redundant as they contain data from the same origin. The most common sources of variants are ClinVar (Landrum et al., 2018) database of variants and their disease relationship, ProTherm thermodynamic database (Kumar et al., 2006), and VariBench itself. The number of unique variants is significantly lower than the sum of the variants in the data sets.

The data sets are divided into 5 categories, 17 subgroups and 11 groups (Table 1). The amount of data items varies for independent sets and is dependent on the original data. Data items irrelevant to VariBench (i.e., not describing variants or their effects) were removed when sets were included to the database. In many data sets, variants are described at three molecular levels (DNA, RNA and protein) and sometimes also at protein structural level. One of the aims of VariBench is to facilitate the reuse of existing data sets, therefore the data are provided in as many levels as possible. Further, the data can be used for various purposes, beyond the original application, such as benchmarking, developing different types of predictors, bioinformatics reviews and analyses of variation types, clinical variation interpretation, etc. When doing such an extension, the users must be cautious and aware of the possible limitations of the data sets and to understand how they have been collected.

The main categories of variation type data sets are insertions and deletions, substitutions in coding and non-coding regions, structure-mapped variants, synonymous and unsense variants, benign variants, and DNA structural variants (See Tables 1, 2). Unsense variants are a new category for exonic alterations that may look synonymous, but affect the protein or its expression, typically due to aberrant splicing or miRNA binding alterations (Vihinen, 2022; Vihinen, 2023a; Vihinen, 2023b). Effect-specific data sets include DNA regulatory elements, RNA splicing, and protein property for aggregation, binding free energy, disorder, solubility, stability, folding rate, interactions, and functional effects. Molecule- and disease-specific data sets include information for individual genes, proteins, gene/protein families or diseases. Phenotype data sets are for a disease feature, severity of the phenotype.

Almost all the categories contain new data sets. In addition, we have 6 new variation categories including structural variations in DNA (1 data set), protein folding rate (5 data sets in six publications), antibody-antigen affinity changes (5 articles and sets), protein-nucleic acid interactions (6 articles), gain of function variants (Nurk et al., 2022), and deep mutational data sets (7 studies).

One of the new categories is for functional effects under the effect-specific category. These sets are mainly for massively parallel reporter assays (saturation mutagenesis) experiments. Users of these data have to be careful since the included data sets display a measured effect; however, their relevance to biological effect is not always clear, see (Vihinen, 2021). The functional effect does not necessarily mean biological effect. One would likely say that a reduction of more than 50% of e.g., enzyme activity has a functional effect. There are several diseases where 90% or more of the normal activity has to be lost for an individual to have a disease and show the effect on biological activity (Vihinen, 2021). Examples include hemophilias due to factor II, VII, IX, X or XII variations and severe immunodeficiency caused by adenosine deaminase alterations.

Statements

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: http://structure.bmc.lu.se/VariBench.

Author contributions

MV conceived the project; NS collected the data sets and developed the web site; NS and MV wrote the manuscript. All authors contributed to the article and approved the submitted version.

Funding

Financial support from Vetenskapsrådet (2019-01403) and the Swedish Cancer Society (grant number CAN 20 1350) is gratefully acknowledged.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Summary

Keywords

variation, mutation, benchmark, method performance assessment, data sets, variation database

Citation

Shirvanizadeh N and Vihinen M (2023) VariBench, new variation benchmark categories and data sets. Front. Bioinform. 3:1248732. doi: 10.3389/fbinf.2023.1248732

Received

27 June 2023

Accepted

08 September 2023

Published

19 September 2023

Volume

3 - 2023

Edited by

Marcelo Reis, State University of Campinas, Brazil

Reviewed by

Castrense Savojardo, University of Bologna, Italy

Carlos Rodrigues, Baker Heart and Diabetes Institute, Australia

Seyed Jamalaldin Haddadi, State University of Campinas, Brazil

Updates

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© 2023 Shirvanizadeh and Vihinen.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Mauno Vihinen,

† Present address: Niloofar Shirvanizadeh, Cancer Genomics and Proteomics, Karolinska University Hospital, Huddinge, Sweden

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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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