SIFT missense predictions for genomes

By Robert Vaser, Swarnaseetha Adusumalli, Sim Ngak Leng, Mile Sikic, Pauline Crystal Ng. Nature Protocols, 2015

1,311 citations recorded by the source corpus.

The SIFT (sorting intolerant from tolerant) algorithm helps bridge the gap between mutations and phenotypic variations by predicting whether an amino acid substitution is deleterious. SIFT has been used in disease, mutation and genetic studies, and a protocol for its use has been previously published with Nature Protocols. This updated protocol describes SIFT 4G (SIFT for genomes), which is a faster version of SIFT that enables practical computations on reference genomes. Users can get predictions for single-nucleotide variants from their organism of interest using the SIFT 4G annotator with SIFT 4G's precomputed databases. The scope of genomic predictions is expanded, with predictions available for more than 200 organisms. Users can also run the SIFT 4G algorithm themselves. SIFT predictions can be retrieved for 6.7 million variants in 4 min once the database has been downloaded. If precomputed predictions are not available, the SIFT 4G algorithm can compute predictions at a rate of 2.6 s per protein sequence. SIFT 4G is available from http://sift-dna.org/sift4g.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/df28fba47e3cefc38f373e06452bf36a0ef9586b

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Citations in the corpus

Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations (2020, neutral): “Neither showed exceptional performance in this study, ranking 14 th and 25 th against the human DMS data (although SIFT4G, a genomic-conservation-based implementation of the SIFT algorithm (Vaser et al , 2016) ranked 9 th ).”
dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs (2020, background): “…FATHMM-XF [20], GenoCanyon [21], LINSIGHT [22], LIST-S2 [23], M-CAP [24], MPC [25], MutPred [26], MVP [27], PrimateAI [28], REVEL [29], SIFT4G [30]), one score for loss of function prediction (ALoFT [31]), and three conservation scores (phyloP17-way_primate [32], phastCons17way_primate [33],…”
The functional landscape of the human phosphoproteome (2019, methodology): “For example, evolutionary conservation was captured by independent features quantifying residue conservation (Vaser et al. 2016), spatial conservation of the phosphorylation within protein domain families (Strumillo et al.”