Cargando…
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...
Autores principales: | , , , |
---|---|
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 |
_version_ | 1782324902587531264 |
---|---|
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. |
format | Online Article Text |
id | pubmed-4087249 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT yateschristopherm suspectenhancedpredictionofsingleaminoacidvariantsavphenotypeusingnetworkfeatures AT filippisioannis suspectenhancedpredictionofsingleaminoacidvariantsavphenotypeusingnetworkfeatures AT kelleylawrencea suspectenhancedpredictionofsingleaminoacidvariantsavphenotypeusingnetworkfeatures AT sternbergmichaelje suspectenhancedpredictionofsingleaminoacidvariantsavphenotypeusingnetworkfeatures |