VariSNP, A Benchmark Database for Variations From dbSNP

Schaafsma, Gerard C. P.; Vihinen, Mauno

doi:10.1002/humu.22727

Cited by 52 publications

(38 citation statements)

References 19 publications

(22 reference statements)

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“…Usually, the datasets have been different but recently benchmark datasets of sufficient size have become available and are being used increasingly. The VariBench database [Nair and Vihinen, 2013] contains positive and negative datasets for several types and effects of variants, whereas the VariSNP database contains only benign variants extracted from dbSNP [Schaafsma and Vihinen, 2015].…”

Section: Predictors For Amino Acid Substitutionsmentioning

confidence: 99%

Variation Interpretation Predictors: Principles, Types, Performance, and Choice

2016

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Next-generation sequencing methods have revolutionized the speed of generating variation information. Sequence data have a plethora of applications and will increasingly be used for disease diagnosis. Interpretation of the identified variants is usually not possible with experimental methods. This has caused a bottleneck that many computational methods aim at addressing. Fast and efficient methods for explaining the significance and mechanisms of detected variants are required for efficient precision/personalized medicine. Computational prediction methods have been developed in three areas to address the issue. There are generic tolerance (pathogenicity) predictors for filtering harmful variants. Gene/protein/disease-specific tools are available for some applications. Mechanism and effect-specific computer programs aim at explaining the consequences of variations. Here, we discuss the different types of predictors and their applications. We review available variation databases and prediction methods useful for variation interpretation. We discuss how the performance of methods is assessed and summarize existing assessment studies. A brief introduction is provided to the principles of the methods developed for variation interpretation as well as guidelines for how to choose the optimal tools and where the field is heading in the future.

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Section: Predictors For Amino Acid Substitutionsmentioning

confidence: 99%

Variation Interpretation Predictors: Principles, Types, Performance, and Choice

2016

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“…Genetic differences can be manifested at different levels as a Single Nucleotide Polymorphism (SNPs), which is a genetic change of single nucleotide or as non‐synonymous SNP (nsSNP), which results in amino acid change in the corresponding transcribed product. In this work we focus on substitutions of single amino acid in the corresponding protein and following the literature such a change is termed single amino acid variation (SAV) . The SAV can affect the corresponding protein's function and thus may be associated with human diseases .…”

Section: Introductionmentioning

confidence: 99%

Investigating the linkage between disease-causing amino acid variants and their effect on protein stability and binding

Peng

Alexov

2016

Proteins

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Single amino acid variations (SAV) occurring in human population result in natural differences between individuals or cause diseases. It is well understood that the molecular effect of SAV can be manifested as changes of the wild type characteristics of the corresponding protein, among which are the protein stability and protein interactions. Typically the effect of SAV on protein stability and interactions is assessed via the changes of the wild type folding and binding free energies. However, in terms of SAV affecting protein functionally and disease susceptibility, one wants to know to what extend the wild type function is perturbed by the SAV. Here we demonstrate that relative, rather than the absolute, change of the folding and binding free energy serves as a good indicator for SAV association with disease. Using HumVar as a source for disease-causing SAV and experimentally determined free energy changes from ProTherm and SKEMPI databases, we achieved correlation coefficients (CC) between the disease index (Pd) and relative folding ( Ppr,f and binding ( Ppr,b) probability indexes, respectively. The obtained CCs demonstrate the applicability of the proposed approach and serves as good indicators for SAV association with disease.

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“…We have collected benchmark variation datasets for various prediction tasks to VariBench (Nair & Vihinen, ). VariSNP is another benchmark database containing neutral variants from dbSNP after filtering out disease‐causing and cancer variants (Schaafsma & Vihinen, ). When using benchmark datasets for testing the performance of machine learning (ML) tools, the training and test datasets have to be disjoint (Vihinen, ; Walsh, Pollastri, & Tosatto, ).…”

Section: Performance Assessment and Measuresmentioning

confidence: 99%

“…Our group has a long experience and interest in investigating variants and their effects and includes protein engineering experiments to improve enzyme properties (Nera, Brockmann, Vihinen, Smith, & Mattsson, ; Rasila, Vihinen, Paulin, Haapa‐Paananen, & Savilahti, ; Vihinen et al., ; Vihinen & Mäntsälä, ; Vihinen, Helin, & Mäntsälä, ; Vihinen, Peltonen, Iitia, Suominen, & Mäntsälä, ), variant collection and distribution on locus‐specific variation databases (LSDBs) (Piirilä, Väliaho, & Vihinen, ; Väliaho, Smith, & Vihinen, ; Vihinen et al., ), interpretation of variants and their effects (Lee et al., ; Väliaho, Faisal, Ortutay, Smith, & Vihinen, ; Vihinen et al., ), and the development of recommendations and standards for variation data (Celli, Dalgleish, Vihinen, Taschner, & den Dunnen, ; Vihinen et al., ; Vihinen, den Dunnen, Dalgleish, and Cotton, ) as well as the development of various prediction tools to filter and interpret harmful variants (Ali, Olatubosun, & Vihinen, ; Niroula & Vihinen, ; Niroula & Vihinen, ; Niroula, Urolagin, & Vihinen, ; Olatubosun, Väliaho, Härkönen, Thusberg, & Vihinen, ; Yang, Niroula, Shen, & Vihinen, ). In addition, we have promoted the importance of systematic performance assessments (Khan & Vihinen, ; Thusberg et al., ), systematic measures and reporting of prediction methods (Vihinen, ; Vihinen, ), and the need for benchmark datasets (Nair & Vihinen, ; Schaafsma & Vihinen, ) and for systematics and nomenclature for describing variants (Byrne et al., ; Vihinen, ; Vihinen, ; Vihinen, ). Currently, we curate about 130 LSDBs, mainly for primary immunodeficiencies (PIDs) (Piirilä et al., ; Schaafsma & Vihinen, ) and also for protein kinase and Src homology 2 (SH2) domain variants (Lappalainen, Thusberg, Shen, & Vihinen, ; Ortutay, Väliaho, Stenberg, & Vihinen, ).…”

Section: Introductionmentioning

confidence: 99%

PON‐P and PON‐P2 predictor performance in CAGI challenges: Lessons learned

Niroula

Vihinen

2017

Human Mutation

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Computational tools are widely used for ranking and prioritizing variants for characterizing their disease relevance. Since numerous tools have been developed, they have to be properly assessed before being applied. Critical Assessment of Genome Interpretation (CAGI) experiments have significantly contributed towards the assessment of prediction methods for various tasks. Within and outside the CAGI, we have addressed several questions that facilitate development and assessment of variation interpretation tools. These areas include collection and distribution of benchmark datasets, their use for systematic large scale method assessment, and the development of guidelines for reporting methods and their performance. For us, CAGI has provided a chance to experiment with new ideas, test the application areas of our methods, and network with other prediction method developers. In this article, we discuss our experiences and lessons learned from the various CAGI challenges. We describe our approaches, their performance, and impact of CAGI on our research. Finally, we discuss some of the possibilities that CAGI experiments have opened up and make some suggestions for future experiments.

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VariSNP, A Benchmark Database for Variations From dbSNP

Cited by 52 publications

References 19 publications

Variation Interpretation Predictors: Principles, Types, Performance, and Choice

Variation Interpretation Predictors: Principles, Types, Performance, and Choice

Investigating the linkage between disease-causing amino acid variants and their effect on protein stability and binding

PON‐P and PON‐P2 predictor performance in CAGI challenges: Lessons learned

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