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

By Jaroslav Bendl, Jan Stourac, Ondrej Salanda, Antonín Pavelka, Eric D. Wieben, Jaroslav Zendulka, Jan Brezovsky, Jirí Damborský. 2014

796 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Better prediction of functional effects for sequence variants (2015, neutral): “Methods such as PredictSNP [48], Condel [49], and MetaSNP [50] have been explicitly optimized to combine different methods, mostly to annotate dis-ease-variant relationships (as opposed to functional changes).”
Systematic Mapping of Protein Mutational Space by Prolonged Drift Reveals the Deleterious Effects of Seemingly Neutral Mutations (2015, background): “The algorithms are relatively accurate in predicting disease-causing mutations [8, 9].”
The Evaluation of Tools Used to Predict the Impact of Missense Variants Is Hindered by Two Types of Circularity (2015, methodology): “Combining them hence has the potential to boost their discriminative power, as reported in a number of studies [Gonz´alez-P´erez and L´opez-Bigas, 2011; Capriotti et al., 2013; Li et al., 2013; Bendl et al., 2014].”