SIFT web server: predicting effects of amino acid substitutions on proteins

By Ngak-Leng Sim, Prateek Kumar, Jing Hu, Steven Henikoff, Georg Schneider, Pauline Crystal Ng. 2012

2,279 citations recorded by the source corpus.

The Sorting Intolerant from Tolerant (SIFT) algorithm predicts the effect of coding variants on protein function. It was first introduced in 2001, with a corresponding website that provides users with predictions on their variants. Since its release, SIFT has become one of the standard tools for characterizing missense variation. We have updated SIFT’s genome-wide prediction tool since our last publication in 2009, and added new features to the insertion/deletion (indel) tool. We also show accuracy metrics on independent data sets. The original developers have hosted the SIFT web server at FHCRC, JCVI and the web server is currently located at BII. The URL is http://sift-dna.org (24 May 2012, date last accessed).

Subfields: Genetics, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Quantitative missense variant effect prediction using large-scale mutagenesis data (2017, neutral): “Thus, variant effect predictors such as PolyPhen2 (Adzhubei et al., 2010), SIFT (Sim et al., 2012), SNAP2 (Hecht et al., 2015), Evolutionary Action (Katsonis and Lichtarge, 2014), CADD (Kircher et al., 2014), and a host of others (Tang and Thomas, 2016) will continue to be widely used to predict…”
UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches (2014, methodology): “BLAST searches against UniRef are available at http://www.uniprot.org/blast/ Contact: huang@dbi.udel.edu”
Language models enable zero-shot prediction of the effects of mutations on protein function (2021, methodology): “SIFT [44] is a first order approach using a position-specific-scoring-matrix.”
Genic Intolerance to Functional Variation and the Interpretation of Personal Genomes (2013, positive): “While other elaborations of this multidimensional approach are possible, including higher dimensions that incorporate additional variant-level quantitative scores, such as SIFT, GERP ++ , MAPP, etc., here we aim to provide the simplest proof-of-concept for how this can be conceptualized, and ultimat”
Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations (2020, methodology): “DMS ranked first in 9202 trials, while DeepSe-quence came top in 600 trials, REVEL in 154, MutPred in 34, SNPs&GO in 7, SIFT4G in 2 and PhD-SNP in 1.”
The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies (2017, positive): “…a more complex, i.e. digenic or in-cis inheritance pattern), additional reports comprising functional characterisation, further phenotypic information, comparative biochemical parameters, evolutionary conservation and SIFT (Sim et al. 2012) and MutPred (Li et al. 2009) pathogenicity predictions.”
Computational approaches in target identification and drug discovery (2016, background): “It provides users with predictions on their variants and is widely used for characterizing missense variations [36].”
Protein design and variant prediction using autoregressive generative models (2021, methodology): “However, these state-of-art methods and established methods 35 – 38 rely on sequence families and alignments, and alignment-based methods are inherently unsuitable for the statistical description of the variable length, hypermutated complementarity determining regions (CDRs) of antibody sequences,…”
Comprehensive Analysis of Tissue-wide Gene Expression and Phenotype Data Reveals Tissues Affected in Rare Genetic Disorders (2017, background): “…variation has been approached through breaches of constraints on conservation and biochemistry (Adzhubei et al., 2013; Moreau and Tranchevent, 2012; Sim et al., 2012; Thusberg et al., 2011), protein interaction network properties (Barabási et al., 2011), guilt-by-association in multi-omic studies…”
The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine (2013, positive): “…with a more complex, i.e. a digenic or SNP in-cis inheritance pattern), additional reports comprising functional characterisation, further phenotypic information, comparative biochemical parameters, evolutionary conservation and SIFT (Sim et al. 2012) and MutPred (Li et al., 2009) predictions.”
SIFT missense predictions for genomes (2015, methodology): “At this speed, we had to precompute SIFT scores for every human protein and store them in a database in order to provide SIFT predictions for human genome variants 13 .”
Using high-resolution variant frequencies to empower clinical genome interpretation (2016, methodology): “Our method is designed to be complementary to and used alongside other gene and variant level methods to filter and prioritize candidate variants (e.g., gene level constraint, 25 amino acid conservation 26,27 and missense prediction algorithms 28,29 ) along with segregation and functional data.”
Large-scale whole-genome sequencing of three diverse Asian populations in Singapore (2018, positive): “Because VEP only annotates deleterious effects predicted by Polyphen(36) and SIFT(37) for SNPs, we separately annotated INDELs using the SIFT web server.(38)”
Ensembl 2018 (2017, methodology): “Additionally, the VEP now predicts the impact of missense variants on the protein function of RefSeq transcripts using SIFT (26) and PolyPhen 2 (27).”