Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm
By Prateek Kumar, Steven Henikoff, Pauline Crystal Ng. 2009
6,626 citations recorded by the source corpus.
Subfields: Molecular Biology
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In Molecular Biology
- Burkhard Rost
- Yang Zhang
- Yana Bromberg
- Eric Boerwinkle
- Jian Peng
- Xiaoming Liu
- Maximilian Hecht
- Bonnie Berger
Citations in the corpus
- Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, background): “Computational variant effect predictors are useful for assessing the effect of point mutations (Gray et al., 2018; Adzhubei et al., 2013; Kumar et al., 2009; Hecht et al., 2015; Rentzsch et al., 2018).”
- SIFT web server: predicting effects of amino acid substitutions on proteins (2012, methodology): “We have incorporated various ways to submit inputs to the SIFT web server for predictions (e.g. protein ids, batch protein and protein sequence submissions) (30,31).”
- An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge (2014, background): “SIFT and Polyphen were overwhelm-ingly used to predict pathogenicity of missense changes.”
- A survey of tools for variant analysis of next-generation genome sequencing data (2013, methodology): “NGS–SNP and VEP provide three scores: Condel [130], PolyPhen and SIFT.”
- Performance of computational tools in evaluating the functional impact of laboratory-induced amino acid mutations (2012, background): “Contact: s.kumar@asu.edu Received on February 8, 2012; revised on May 9, 2012; accepted on June 6, 2012”
- MuSiC: Identifying mutational significance in cancer genomes (2012, background): “…mutations in MuSiC through the integration of published solutions, such as Mutation Assessor (Reva et al. 2011) and PolyPhen-2 (Adzhubei et al. 2010), as well as through the design of new modules which take advantage of databases that categorize such effects, such as SIFT (Kumar et al. 2009).”
- Pathogenic germline variants in 10,389 adult cancers (2018, methodology): “We used evidence from SIFT (Kumar et al., 2009) and PolyPhen (Adzhubei et al.”