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SuSPect: Enhanced Prediction of Single Amino Acid Variant (SAV) Phenotype Using Network Features

Whole-genome and exome sequencing studies reveal many genetic variants between individuals, some of which are linked to disease. Many of these variants lead to single amino acid variants (SAVs), and accurate prediction of their phenotypic impact is important. Incorporating sequence conservation and...

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Detalles Bibliográficos
Autores principales: Yates, Christopher M., Filippis, Ioannis, Kelley, Lawrence A., Sternberg, Michael J.E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4087249/
https://www.ncbi.nlm.nih.gov/pubmed/24810707
http://dx.doi.org/10.1016/j.jmb.2014.04.026
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author Yates, Christopher M.
Filippis, Ioannis
Kelley, Lawrence A.
Sternberg, Michael J.E.
author_facet Yates, Christopher M.
Filippis, Ioannis
Kelley, Lawrence A.
Sternberg, Michael J.E.
author_sort Yates, Christopher M.
collection PubMed
description Whole-genome and exome sequencing studies reveal many genetic variants between individuals, some of which are linked to disease. Many of these variants lead to single amino acid variants (SAVs), and accurate prediction of their phenotypic impact is important. Incorporating sequence conservation and network-level features, we have developed a method, SuSPect (Disease-Susceptibility-based SAV Phenotype Prediction), for predicting how likely SAVs are to be associated with disease. SuSPect performs significantly better than other available batch methods on the VariBench benchmarking dataset, with a balanced accuracy of 82%. SuSPect is available at www.sbg.bio.ic.ac.uk/suspect. The Web site has been implemented in Perl and SQLite and is compatible with modern browsers. An SQLite database of possible missense variants in the human proteome is available to download at www.sbg.bio.ic.ac.uk/suspect/download.html.
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spelling pubmed-40872492014-07-15 SuSPect: Enhanced Prediction of Single Amino Acid Variant (SAV) Phenotype Using Network Features Yates, Christopher M. Filippis, Ioannis Kelley, Lawrence A. Sternberg, Michael J.E. J Mol Biol Article Whole-genome and exome sequencing studies reveal many genetic variants between individuals, some of which are linked to disease. Many of these variants lead to single amino acid variants (SAVs), and accurate prediction of their phenotypic impact is important. Incorporating sequence conservation and network-level features, we have developed a method, SuSPect (Disease-Susceptibility-based SAV Phenotype Prediction), for predicting how likely SAVs are to be associated with disease. SuSPect performs significantly better than other available batch methods on the VariBench benchmarking dataset, with a balanced accuracy of 82%. SuSPect is available at www.sbg.bio.ic.ac.uk/suspect. The Web site has been implemented in Perl and SQLite and is compatible with modern browsers. An SQLite database of possible missense variants in the human proteome is available to download at www.sbg.bio.ic.ac.uk/suspect/download.html. Elsevier 2014-07-15 /pmc/articles/PMC4087249/ /pubmed/24810707 http://dx.doi.org/10.1016/j.jmb.2014.04.026 Text en © 2014 Published by Elsevier Ltd. http://creativecommons.org/licenses/by/3.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Yates, Christopher M.
Filippis, Ioannis
Kelley, Lawrence A.
Sternberg, Michael J.E.
SuSPect: Enhanced Prediction of Single Amino Acid Variant (SAV) Phenotype Using Network Features
title SuSPect: Enhanced Prediction of Single Amino Acid Variant (SAV) Phenotype Using Network Features
title_full SuSPect: Enhanced Prediction of Single Amino Acid Variant (SAV) Phenotype Using Network Features
title_fullStr SuSPect: Enhanced Prediction of Single Amino Acid Variant (SAV) Phenotype Using Network Features
title_full_unstemmed SuSPect: Enhanced Prediction of Single Amino Acid Variant (SAV) Phenotype Using Network Features
title_short SuSPect: Enhanced Prediction of Single Amino Acid Variant (SAV) Phenotype Using Network Features
title_sort suspect: enhanced prediction of single amino acid variant (sav) phenotype using network features
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4087249/
https://www.ncbi.nlm.nih.gov/pubmed/24810707
http://dx.doi.org/10.1016/j.jmb.2014.04.026
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