Table 1.
List with examples of widely used tools to perform an NGS functional filter.
| Software | Short Description | Ref. |
|---|---|---|
| PhyloP Phylogenetic p-values |
Based on a model of neutral evolution, the patterns of conservation (positive scores)/acceleration (negative scores) are analyzed for various annotation classes and clades of interest. | [146] |
| SIFT Sorting Intolerant from Tolerant |
Predicts based on sequence homology, if an AA substitution will affect protein function and potentially alter the phenotype. Scores less than 0.05 indicating a variant as deleterious. | [112] |
| PolyPhen-2 Polymorphism Phenotyping v2 |
Predicts the functional impact of an AA replacement from its individual features using a naive Bayes classifier. Includes two tools HumDiv (designed to be applied in complex phenotypes) and HumVar (designed to diagnostic of Mendelian diseases). Higher scores (>0.85) predicts, more confidently, damaging variants. | [113] |
| CADD Combined Annotation Dependent Depletion |
Integrates diverse genome annotations and scores all human SNV and Indel. It prioritizes functional, deleterious, and disease causal variants according to functional categories, effect sizes and genetic architectures. Scores above 10 should be applied as a cut-off for identifying pathogenic variants. | [114] |
| MutationTaster | Analyses evolutionary conservation, splice-site changes, loss of protein features and changes that might affect the amount of mRNA. Variants are classified, as polymorphism or disease-causing | [147] |
| Human Splice Finder | Predict the effects of mutations on splicing signals or to identify splicing motifs in any human sequence. | [133] |
| nsSNPAnalyzer | Extracts structural and evolutionary information from a query nsSNP and uses a machine learning method (Random Forest) to predict its phenotypic effect. Classifies the variant as neutral and disease. | [148] |
| TopoSNP Topographic mapping of SNP |
Analyze SNP based on its geometric location and conservation information, produces an interactive visualization of disease and non-disease associated with each SNP. | [149] |
| Condel Consensus Deleteriousness |
Condel integrates the output of different methods to predict the impact of nsSNP on protein function. The algorithm based on the weighted average of the normalized scores classifies the variants as neutral or deleterious. | [115] |
| ANNOVAR * Annotate Variation |
Annotates the variants based on several parameters, such as identification whether SNPs or CNVs affect the protein (gene-based), identification of variants in specific genomic regions outside protein-coding regions (region-based) and identification of known variants documented in public and licensed database (filter-based) | [116] |
| VEP * Variant Effect Predictor |
Determines the effect of multiple variants (SNPs, insertions, deletions, CNVs or structural variants) on genes, transcripts and protein sequence, as well as regulatory regions. | [117] |
| snpEff * | Annotation and classification of SNV based on their effects on annotated genes, such as synonymous/nsSNP, start or stop codon gains or losses, their genomic locations, among others. Considered as a structural based tool for annotation. | [118] |
| SeattleSeq * | Provides annotation of SNVs and small indels, by providing to each the dbSNP rs IDs, gene names and accession numbers, variation functions, protein positions and AA changes, conservation scores, HapMap frequencies, PolyPhen predictions and clinical association. | [119] |
AA—amino acid; SNV—single nucleotide variant, Indel—small insertion/deletion variants, SNP—single nucleotide polymorphism, nsSNP—nonsynonymous SNP; CNV—copy number variation; * these tools, although also able to filter variants, are primarily responsible for variant annotation.