Evaluation of computational techniques for predicting non-synonymous single nucleotide variants pathogenicity.

The human genetic diseases associated with many factors, one of these factors is the non-synonymous Single Nucleotide Variants (nsSNVs) cause single amino acid change with another resulting in protein function change leading to disease. Many computational techniques have been released to expect the impacts of amino acid alteration on protein function and classify mutations as pathogenic or neutral. Here in this article, we assessed the performance of eight techniques; FATHMM, SIFT, Provean, iFish, Mutation Assessor, PANTHER, SNAP2, and PON- P2 using a VaribenchSelectedPure dataset of 2144 pathogenic variants and 3777 neutral variants extracted from the free standard database "Varibench." The first five techniques achieve (45.60-83.75) % specificity, (52.64-94.13) % sensitivity, (51.00-88.90) % AUC, and (49.76-88.24) % ACC on whole dataset, while all eight techniques achieve (36.54-77.88) % specificity, (50.00-75.00) % sensitivity, (51.00-76.40) % AUC, and (25.00-77.78) % ACC on random sample dataset. We also created a Meta classifier (CSTJ48) that combines FATHMM, iFish, and Mutation Assessor. It registers 96.33% specificity, 86.07% sensitivity, 91.20% AUC, and 91.89 ACC. By comparing the results, it's clear that FATHMM gives the highest performance over the seven individual techniques, where it achieves 83.75% and 77.88% specificity, 94.13%, and 75.00% sensitivity, 88.90% and 76.40% AUC, and 88.24% and 77.78% ACC on whole and random sample dataset, respectively. Also, the launched Meta classifier (CSTJ48) is outperforming over all the eight individual tools that compared here.

A review study: Computational techniques for expecting the impact of non-synonymous single nucleotide variants in human diseases.

Non-Synonymous Single-Nucleotide Variants (nsSNVs) and mutations can create a diversity effect on proteins as changing genotype and phenotype, which interrupts its stability. The alterations in the protein stability may cause diseases like cancer. Discovering of nsSNVs and mutations can be a useful tool for diagnosing the disease at a beginning stage. Many studies introduced the various predicting singular and consensus tools that based on different Machine Learning Techniques (MLTs) using diverse datasets. Therefore, we introduce the current comprehensive review of the most popular and recent unique tools that predict pathogenic variations and Meta-tool that merge some of them for enhancing their predictive power. Also, we scanned the several types computational techniques in the state-of-the-art and methods for predicting the effect both of coding and noncoding variants. We then displayed, the protein stability predictors. We offer the details of the most common benchmark database for variations including the main predictive features used by the different methods. Finally, we address the most common fundamental criteria for performance assessment of predictive tools. This review is targeted at bioinformaticians attentive in the characterization of regulatory variants, geneticists, molecular biologists attentive in understanding more about the nature and effective role of such variants from a functional point of views, and clinicians who may hope to learn about variants in human associated with a specific disease and find out what to do next to uncover how they impact on the underlying mechanisms.