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Assists researchers to perform evaluation of the pathogenic potential of DNA sequence alterations. MutationTaster is an online application that aims to determine the functional consequences of amino acid substitutions, short insertion and/or deletion (indel) mutations, variants spanning intron-exon borders, intronic and synonymous alterations. Moreover, this tool is able to categorize confirmed polymorphisms and known disease mutations.
SIFT / Sorting Intolerant From Tolerant
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Determines if an amino acid substitution is deleterious to protein function. SiFT can be employed to prioritize nonsynonymous or missense variants. It is able to deal with protein conservation with homologous sequences and the severity of the amino acid change. This tool can be applied to human genome and nonhuman organisms. It can run on a large number of protein sequences using a single graphical processing unit.
Predicts the effects of mutations while taking into account the interactions that occur between amino acids in proteins or bases in RNA. EVmutation predicts the relative favourability of unseen mutations by inferring context-dependent effects. It is able to better account for selective pressures than models that do not account for epistasis, for example, in the case of the toxin–antitoxin complex ParED. The tool was tested by comparing its predictions with outcomes of high-throughput mutagenesis experiments and measurements of human disease mutations.
PolyPhen / Polymorphism Phenotyping
Predicts the possible impact of an amino acid substitution on the structure and function of a human protein. PolyPhen predicts the functional significance of an allele replacement from its individual features by a Naïve Bayes classifier. The web application allows users to (i) predict the effect of a single-residue substitution or reference single nucleotide polymorphism SNP, (ii) analyze SNPs in a batch mode, and (iii) search in a database of precomputed predictions for the whole human exome sequence space.
An unsupervised spectral approach for scoring variants which does not make use of labeled training data. Eigen produces estimates of predictive accuracy for each functional annotation score, and subsequently uses these estimates of accuracy to derive the aggregate functional score for variants of interest as a weighted linear combination of individual annotations. The Eigen score is particularly useful in prioritizing likely causal variants in a region of interest when it is combined with population-level genetic data in the framework of a hierarchical model.
M-CAP / Mendelian Clinically Applicable Pathogenicity
Represents a clinical pathogenicity classifier. M-CAP aims to misclassify no more than 5% of pathogenic variants while aggressively reducing the list of variants of uncertain significance. This tool provides: (i) a method that combines amino acid conservation features with gradient boosting trees that can be applied to any variant training set and (ii) computed scores trained on mutations linked to Mendelian diseases that can be directly used by clinicians to interpret variants of uncertain consequences.
VEP / Variant Effect Predictor
Determines the effect of your variants (SNPs, insertions, deletions, CNVs or structural variants) on genes, transcripts, and protein sequence, as well as regulatory regions. Simply input the coordinates of your variants and the nucleotide changes to find out the genes and transcripts affected by the variants, location of the variants (e.g. upstream of a transcript, in coding sequence, in non-coding RNA, in regulatory regions), consequence of your variants on the protein sequence (e.g. stop gained, missense, stop lost, frameshift); known variants that match yours, and associated minor allele frequencies from the 1000 Genomes Project, SIFT and PolyPhen scores for changes to protein sequence.
An integrated framework for the analysis and interpretation of the consequences of variants in the human kinome. wKinMut web-server offers direct prediction of the potential pathogenicity of the mutations from a number of methods, including prediction method based on the combination of information from a range of diverse sources, including physicochemical properties and functional annotations from FireDB and Swissprot and kinase-specific characteristics such as the membership to specific kinase groups, the annotation with disease-associated GO terms or the occurrence of the mutation in PFAM domains, and the relevance of the residues in determining kinase subfamily specificity from S3Det.
CHASM / Cancer-specific High-throughput Annotation of Somatic Mutations
Utilizes machine learning to integrate missense mutation context at multiple scales. CHASM uses the Random Forest algorithm to discriminate somatic missense mutations (referred to hereafter as missense mutations) as either cancer drivers or passengers. This program can also serve for evaluating the statistical significance of cancer type-specific predictions for each of 32 cancer types from the Cancer Genome Atlas (TCGA), and pan-cancer predictions for all TCGA cancer types in aggregate.
CRAVAT / Cancer-Related Analysis of Variants Toolkit
Performs cancer-related analysis of variants. CRAVAT returns mutation interpretations in a dynamic interactive web environment for sorting, visualizing and inferring mechanism. The software (i) performs all projecting and assigns sequence ontology, (ii) predicts mutation impact using multiple bioinformatics classifiers normalized, (iii) allows for joint prioritization of all non-silent mutation types, organizes annotation from many sources on graphical displays of protein sequence and 3D structure, and (iv) facilitates dynamic filtering. It is suitable for both large and small studies and developed for easy integration with other software.
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Annotates and predicts the effects of single nucleotide polymorphisms (SNPs). SnpEff features include: (1) the ability to make thousands of predictions per second; (2) the ability to add custom genomes and annotations; (3) the ability to integrate with Galaxy (4) compatibility with multiple species and multiple codon usage tables, (5) integration with Broad's Genome Analysis Toolkit (GATK) and (6) the ability to perform non-coding annotations. It enables rapid analyses of whole-genome sequencing data to be performed by an individual laboratory.
Provides a cloud-based system that allows users to directly process and analyze next-generation sequencing (NGS) data through a user-friendly graphical user interface (GUI). The first objectives of the GENESIS (formerly GEM.app) platform are: 1) to assist scientists/clinicians in transferring and processing genomic data, 2) to produce accurate, high quality, and reproducible results, 3) to provide a highly available and scalable analytical framework for analyzing variant data, and 4) to provide tools for user-driven data-sharing and collaboration. Hereby, GENESIS enables users of varying computational experience to iteratively test many different filtering strategies in a matter of seconds and to browse very large sets of full exomes and genomes in real-time.
SNAP / Screening for Non-Acceptable Polymorphisms
A neural-network based tool to be used for the evaluation of functional effects single amino acid substitutions in proteins. SNAP utilizes various biophysical characteristics of the substitution, as well as evolutionary information, some predicted—or when made available observed—structural features, and possibly annotations, to predict whether or not a mutation is likely to alter protein function (in either direction: gain or loss). SNAP identifies over 80% of the non-neutral mutations at 77% accuracy and over 76% of the neutral mutations at 80% accuracy at its default threshold.
GERP / Genomic Evolutionary Rate Profiling
Performs identification of constrained elements in multiple alignments, by quantifying substitution deficits. GERP is a bottom-up method for constrained element detection that identifies sites under evolutionary constraint, i.e., sites that show fewer substitutions than would be expected to occur during neutral evolution. The software then aggregates these sites into longer, potentially functional sequences called constrained element. It is suitable for high-throughput analysis of genomic data.
Represents a novel method for variant-effect prediction that integrates heterogeneous sources of biological information. DEOGEN analyzes protein and variant pair by combining different levels of contextualisation of the protein function with a Random Forest (RF) predictor. This method works about the prediction of the phenotypic effects of short in-frame genetic variants (INDELs). It supplies interactive visualisation approaches in order to (1) explain how the deleteriousness score relates to all other variants within that protein and (2) break down the origin of the score.
phyloP / phylogenetic P-values
Measures p-values for conservation or acceleration based on an alignment and a model of neutral evolution. phyloP processes p-values independently disregarding correlations between tests. Adjustments for several hypotheses are required when jointly interpreting the reported P-values for a collection of sites or elements. It was applied to multiple alignments of 36 species in the ENCODE regions and analyze patterns of conservation/acceleration for various annotation classes and clades of interest.
Allows prediction of the functional consequences of non-coding and coding single nucleotide variants (SNVs). FATHMM-XF is a method consisting in an improvement over the predictor FATHMM-MKL. The software was built using supervised machine learning with labeled examples ascribed to pathogenic (positive) or benign (neutral) mutations. It assigns a confidence score (a p-score) for every prediction to simplify interpretation and focus analysis on a subset of high-confidence predictions (cautious classification).
ALoFT / Annotation of Loss-of-Function Transcripts
Allows users to annotate and predict the disease-causing potential of loss of function (LoF) variants. ALoFT is composed of three mains characteristics: (i) functional domain annotations, (ii) evolutionary conservation; and (iii) biological networks. Additionally, it includes functionalities to help in identifying erroneous LoF calls, potential mismapping, and annotation errors as well as network features to determine, for instance, the number of disease genes connected to a gene in a protein–protein interaction (PPI) network.
MuD / Mutation Detector
A Random Forests-based classifier that utilizes structural and sequence-derived features to assess the impact of a given substitution on the protein function. MuD is unique in that user-reported protein-specific structural and functional information can be added at run-time, thereby enhancing the prediction accuracy further. MuD assigns a reliability score to every prediction, thus offering a useful tool for the prioritization of substitutions in proteins with an available 3D structure.
VarMod / Variant Modeller
Utilises both protein sequence and structural features to predict nsSNVs that alter protein function. VarMod develops recent observations that functional nsSNVs are enriched at protein–protein interfaces and protein–ligand binding sites and uses these characteristics to make predictions. In benchmarking on a set of nearly 3000 nsSNVs VarMod performance is comparable to an existing state of the art method. The VarMod web server provides extensive resources to investigate the sequence and structural features associated with the predictions including visualisation of protein models and complexes via an interactive JSmol molecular viewer.
StructMAn / Structural Mutation Annotation
A web-based tool for annotation of human and non-human (non-synonymous) single nucleotide variant (nsSNVs) in the structural context. StructMAn analyzes the spatial location of the amino acid residue corresponding to nsSNVs in the three-dimensional (3D) protein structure relative to other proteins, nucleic acids and low molecular-weight ligands. We make use of all experimentally available 3D structures of query proteins, and also, unlike other tools in the field, of structures of proteins with detectable sequence identity to them. This allows us to provide a structural context for around 20% of all nsSNVs in a typical human sequencing sample, for up to 60% of nsSNVs in genes related to human diseases and for around 35% of nsSNVs in a typical bacterial sample. Each nsSNV can be visualized and inspected by the user in the corresponding 3D structure of a protein or protein complex.
Searches the mutational landscape of a given RNA sequence for mutations that will significantly alter the RNA's secondary structure. corRna has several potential uses. It can be used to predict potential deleterious mutations in regulatory RNAs. It could also help guide the design of mutational analysis experiments. Finally, corRna could be useful in the synthetic RNA design for sequences which need to have a robust secondary structure. corRna works in two steps. First, it uses the RNAmutants framework to compute a sample set of candidate deleterious mutations. This search can be aided either by a structural or mutation heuristic to prune the RNA mutational landscape. Then, corRna ranks the samples by the strength of their deleterious effect. corRna is freely available online.
IMHOTEP / Integrating Molecular Heuristics and Other Tools for Effect Prediction
Predicts the functional consequences of mutations of human pathogenetics. IMHOTEP integrates nine popular prediction tools (PolyPhen-2, SNPs&GO, MutPred, SIFT, MutationTaster2, Mutation Assessor and FATHMM as well as conservation based Grantham Score and PhyloP). The tool provides a wide range of statistical modeling techniques, drawing upon 10 029 disease causing single nucleotide variants (SNVs) from Human Gene Mutation Database and 10 002 putatively “benign” non-synonymous SNVs from UCSC. The best results were obtained with integrative methods random forest, decision tree and logistic regression, single tool prediction with MutationTaster2 was also found to work exceptionally well.
GeMSTONE / Germline Mutation Scoring Tool fOr Next-Generation sEquencing data
Allows an accessible, collaborative, replicable and holistic analysis of genetic variants. GeMSTONE permits to eliminate the time and space burdens associated with modern variant analysis tools. It saves users dozens of gigabytes of potential disk space per run for the same workflow on a medium sized dataset. The tool encourages the growth of the genomics research community. It will automate the (re)analysis of genome-wide genetic variation data and enhance the reproducibility of large-scale genomic studies.
MAPPIN / Method for Annotating Predicting Pathogenicity and mode of Inheritance for Nonsynonymous variants
Predicts pathogenicity and mode of inheritance for protein-truncating variants including premature stop and frameshift indel mutations. MAPPIN is a method to predict pathogenicity and mode of inheritance for nonsynonymous single nucleotide variants (nsSNVs). It classifies variants into three classes: dominant (disease-causing as heterozygotes), recessive (disease-causing when homozygous or compound heterozygous) and benign using a random forest (RF) classifier trained on known benign and deleterious variants.
Condel / CONsensus DELeteriousness score of missense SNVs
Evaluates the probability that a set of missense single nucleotide variants (SNVs) is deleterious. Condel integrates the output of different methods and can be applied to any array. It computes a weighted approach of missense mutations from the complementary cumulative distributions of scores of deleterious and neutral mutations. This tool can provide some insight into the impact of the mutation on the biological activity of the proteins.
Consensus classifiers for prediction of disease-related mutations. PredictSNP1 offers its users a consensus score based on the output of six different amino acid-based predictors. Because of the nature of the tools whose results are combined to generate its consensus, PredictSNP1 can only be used to analyze substitutions in an amino acid sequence. PredictSNP2 complements PredictSNP1 by evaluating the effects of nucleotide variants located in any region of the genome. PredictSNP2 represents the first unified platform for nucleotide-based predictions of deleterious variants.
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