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

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Abstract

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).

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Automated inference of molecular mechanisms of disease from amino acid substitutions.

Biao Li, Vidhya G Krishnan, Matthew Mort … (2009)

Advances in high-throughput genotyping and next generation sequencing have generated a vast amount of human genetic variation data. Single nucleotide substitutions within protein coding regions are of particular importance owing to their potential to give rise to amino acid substitutions that affect protein structure and function which may ultimately lead to a disease state. Over the last decade, a number of computational methods have been developed to predict whether such amino acid substitutions result in an altered phenotype. Although these methods are useful in practice, and accurate for their intended purpose, they are not well suited for providing probabilistic estimates of the underlying disease mechanism. We have developed a new computational model, MutPred, that is based upon protein sequence, and which models changes of structural features and functional sites between wild-type and mutant sequences. These changes, expressed as probabilities of gain or loss of structure and function, can provide insight into the specific molecular mechanism responsible for the disease state. MutPred also builds on the established SIFT method but offers improved classification accuracy with respect to human disease mutations. Given conservative thresholds on the predicted disruption of molecular function, we propose that MutPred can generate accurate and reliable hypotheses on the molecular basis of disease for approximately 11% of known inherited disease-causing mutations. We also note that the proportion of changes of functionally relevant residues in the sets of cancer-associated somatic mutations is higher than for the inherited lesions in the Human Gene Mutation Database which are instead predicted to be characterized by disruptions of protein structure. http://mutdb.org/mutpred predrag@indiana.edu; smooney@buckinstitute.org.

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Accounting for human polymorphisms predicted to affect protein function.

Pauline C. Ng, Steven Henikoff (2002)

A major interest in human genetics is to determine whether a nonsynonymous single-base nucleotide polymorphism (nsSNP) in a gene affects its protein product and, consequently, impacts the carrier's health. We used the SIFT (Sorting Intolerant From Tolerant) program to predict that 25% of 3084 nsSNPs from dbSNP, a public SNP database, would affect protein function. Some of the nsSNPs predicted to affect function were variants known to be associated with disease. Others were artifacts of SNP discovery. Two reports have indicated that there are thousands of damaging nsSNPs in an individual's human genome; we find the number is likely to be much lower.

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The UCSC Genome Browser database: extensions and updates 2011

Timothy Dreszer, Donna Karolchik, Ann Zweig … (2011)

The University of California Santa Cruz Genome Browser (http://genome.ucsc.edu) offers online public access to a growing database of genomic sequence and annotations for a wide variety of organisms. The Browser is an integrated tool set for visualizing, comparing, analyzing and sharing both publicly available and user-generated genomic data sets. In the past year, the local database has been updated with four new species assemblies, and we anticipate another four will be released by the end of 2011. Further, a large number of annotation tracks have been either added, updated by contributors, or remapped to the latest human reference genome. Among these are new phenotype and disease annotations, UCSC genes, and a major dbSNP update, which required new visualization methods. Growing beyond the local database, this year we have introduced ‘track data hubs’, which allow the Genome Browser to provide access to remotely located sets of annotations. This feature is designed to significantly extend the number and variety of annotation tracks that are publicly available for visualization and analysis from within our site. We have also introduced several usability features including track search and a context-sensitive menu of options available with a right-click anywhere on the Browser's image.

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Journal

Journal ID (nlm-ta): Nucleic Acids Res

Journal ID (iso-abbrev): Nucleic Acids Res

Journal ID (publisher-id): nar

Journal ID (hwp): nar

Title: Nucleic Acids Research

Publisher: Oxford University Press

ISSN (Print): 0305-1048

ISSN (Electronic): 1362-4962

Publication date Collection: July 2012

Publication date (Print): July 2012

Publication date (Electronic): 9 June 2012

Publication date PMC-release: 9 June 2012

Volume: 40

Issue: Web Server issue

Pages: W452-W457

Affiliations

¹Computational and Systems Biology, Genome Institute of Singapore, ²Genomic Medicine, J. Craig Venter Institute, ³Department of Mathematics and Computer Science, Franklin & Marshall College, ⁴Howard Hughes Medical Institute & Basic Sciences Division, Fred Hutchinson Cancer Research Center and ⁵Biomolecular Function Discovery Division, Bioinformatics Institute of Singapore, Singapore

Author notes

*To whom correspondence should be addressed. Tel: +65 6808 8310; Fax: +65 6808 8292; Email: ngpc4@ 123456gis.a-star.edu.sg

Present addresses: Prateek Kumar, Dana-Farber Cancer Institute, 44 Binney St., Boston, MA 02115, USA. Pauline C. Ng, Genome Institute of Singapore, 60 Biopolis Street, Singapore 138672, Singapore.

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Publisher ID: gks539

DOI: 10.1093/nar/gks539

PMC ID: 3394338

PubMed ID: 22689647

SO-VID: cf5c5405-5325-4ea0-ab52-c7ad947a831f

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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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

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Genomic Prediction

Most cited references 21

Automated inference of molecular mechanisms of disease from amino acid substitutions.

Accounting for human polymorphisms predicted to affect protein function.

The UCSC Genome Browser database: extensions and updates 2011

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