PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations

Abstract

Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.

Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation

“…PON-P was the first of these, integrating a conservation method (SIFT), a structural stabilitybased method (I-Mutant), and three combined machinelearning-based methods (SNAP, PolyPhen-2, PhD-SNP), using Random Forests to make a final prediction (Olatubosun et al 2012). Numerous other studies have since followed this general approach, such as Meta-SNP (Capriotti et al 2013a), CoVEC (Frousios et al 2013), PredictSNP (Bendl et al 2014), and Meta-SVM (Dong et al 2015). Taking the idea of producing a meta-prediction from multiple individual prediction algorithms yet a step further, methods such as CADD (Kircher et al 2014) treat the output of any NSV effect-prediction method as simply one type of "annotation" of a variant and then use an SVM-based approach to make a meta-prediction from a large list of diverse annotations.…”

The novel homozygous KCNJ10 c.986T>C (p.(Leu329Pro)) variant is pathogenic for the SeSAME/EAST homologue in Malinois dogs

“…I-Mutant2.0 28 was used as a predictor of protein stability changes upon variations. Computational prediction of disease-related variants was performed with the consensus tools CONDEL 29 (combines Logre, MAPP, Massessor, Pph2 and SIFT), Meta-SNP 30 (combines PANTHER, PhD-SNP, SIFT and SNAP), PredictSNP 31 (combines MAPP, PhD-SNP, Pph1, Pph2, SIFT and SNAP) and PON-P2. 32 …”

Structural Dynamics is a Determinant of the Functional Significance of Missense Variants

“…predictSNPSelected, and SwissVarSelected, which have been manually curated to minimize possible overlaps and proposed to serve as a benchmarking set (26). The latter three, indicated by the suffix "Selected", are subsets of VariBench (12), predictSNP (13), and SwissVar (14), respectively, obtained upon clearing entries already represented in the former two most populated datasets (i.e., HumVar and ExoVar). Such preliminary filtering has been performed to allow for a fair comparison of the performances of pathogenicity predictors and to remove "training bias"-that is, any bias that might originate from partial overlap between the corresponding training and testing datasets.…”

“…Such investigations greatly benefited from the creation of publicly available databases of mutations found in humans and computational tools developed for pathogenicity prediction (2,(11)(12)(13)(14). Sequence conservation/evolution analyses using machine learning methods is a common approach in those tools.…”