SIFT: predicting amino acid changes that affect protein function

By Pauline Crystal Ng, Steven Henikoff. 2003

6,536 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Computational Biology & Bioinformatics

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Citations in the corpus

CADD: predicting the deleteriousness of variants throughout the human genome (2018, positive): “Examples of annotations include transcript information like distance to exon-intron boundaries, DNase hypersensitivity, transcription factor binding, expression levels in commonly studied cell lines and amino acid substitution scores for protein coding sequences like Grantham (20), SIFT (21) and Pol”
Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, background): “Critically important residues tend to be conserved, while unfavorable residues are observed less frequently.”
Better prediction of functional effects for sequence variants (2015, positive): “Therefore, we explicitly compared SNAP2 only to widely used methods that explicitly aim at the prediction of functional effects: SIFT [10] and PolyPhen-2 [13].”
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).”
Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. (2012, methodology): “The weightwj can be flexibly chosen using the observed data, such as a function of MAF, or external information, such as PolyPhen or SIFT score [24, 25].”
Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines (2017, background): “We found that Poly-phen [11] and SIFT [12] are cited most frequently, followed by MutationTaster [13], CADD [14], PROVEAN [15], Mutpred [16], and Condel [17].”
PANTHER-PSEP: predicting disease-causing genetic variants using position-specific evolutionary preservation (2016, methodology): “Most computational tools for predicting the functional consequences of NSVs, including SIFT (Ng and Henikoff, 2003), PolyPhen (Adzhubei et al.”
Pharmacogenomics of GPCR Drug Targets (2018, neutral): “Of these, 1,772 MVs map to the 2,036 known functional sites described above (i.e., (cid:1) 87% of the known functional site variations have been captured by SIFT or PolyPhen; permutation test; Z score = 24.5; p< 1 3 10 (cid”
A new disease-specific machine learning approach for the prediction of cancer-causing missense variants (2011, methodology): “In general, computational methods for the prediction of the impact of SAPs use empirical rules [15,16], HiddenMarkov Models [17], Neural Networks [18,19], Decision Tree [20,21], Random Forest [22–26] and Support Vector Machines [27–31], algorithms relying on amino acid sequence, structure and evolut”
Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information (2006, methodology): “When scoring our predictions we also compare with PolyPhen and SIFT (Table 4).”
The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations (2013, methodology): “The same procedure described above was used to avoid Nonetheless, our results from training and testing on HumVar mutations that map to structure considerably outperform other well-known individual methods including SIFT and PolyPhen2 as reported by González-Pérez and López-Bigas [18] (Accuracies be”