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Variability in pathogenicity prediction programs: impact on clinical diagnostics
Current practice by clinical diagnostic laboratories is to utilize online prediction programs to help determine the significance of novel variants in a given gene sequence. However, these programs vary widely in their methods and ability to correctly predict the pathogenicity of a given sequence cha...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BlackWell Publishing Ltd
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4367082/ https://www.ncbi.nlm.nih.gov/pubmed/25802880 http://dx.doi.org/10.1002/mgg3.116 |
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author | Walters-Sen, Lauren C Hashimoto, Sayaka Thrush, Devon Lamb Reshmi, Shalini Gastier-Foster, Julie M Astbury, Caroline Pyatt, Robert E |
author_facet | Walters-Sen, Lauren C Hashimoto, Sayaka Thrush, Devon Lamb Reshmi, Shalini Gastier-Foster, Julie M Astbury, Caroline Pyatt, Robert E |
author_sort | Walters-Sen, Lauren C |
collection | PubMed |
description | Current practice by clinical diagnostic laboratories is to utilize online prediction programs to help determine the significance of novel variants in a given gene sequence. However, these programs vary widely in their methods and ability to correctly predict the pathogenicity of a given sequence change. The performance of 17 publicly available pathogenicity prediction programs was assayed using a dataset consisting of 122 credibly pathogenic and benign variants in genes associated with the RASopathy family of disorders and limb-girdle muscular dystrophy. Performance metrics were compared between the programs to determine the most accurate program for loss-of-function and gain-of-function mechanisms. No one program correctly predicted the pathogenicity of all variants analyzed. A major hindrance to the analysis was the lack of output from a significant portion of the programs. The best performer was MutPred, which had a weighted accuracy of 82.6% in the full dataset. Surprisingly, combining the results of the top three programs did not increase the ability to predict pathogenicity over the top performer alone. As the increasing number of sequence changes in larger datasets will require interpretation, the current study demonstrates that extreme caution must be taken when reporting pathogenicity based on statistical online protein prediction programs in the absence of functional studies. |
format | Online Article Text |
id | pubmed-4367082 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BlackWell Publishing Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-43670822015-03-23 Variability in pathogenicity prediction programs: impact on clinical diagnostics Walters-Sen, Lauren C Hashimoto, Sayaka Thrush, Devon Lamb Reshmi, Shalini Gastier-Foster, Julie M Astbury, Caroline Pyatt, Robert E Mol Genet Genomic Med Method Current practice by clinical diagnostic laboratories is to utilize online prediction programs to help determine the significance of novel variants in a given gene sequence. However, these programs vary widely in their methods and ability to correctly predict the pathogenicity of a given sequence change. The performance of 17 publicly available pathogenicity prediction programs was assayed using a dataset consisting of 122 credibly pathogenic and benign variants in genes associated with the RASopathy family of disorders and limb-girdle muscular dystrophy. Performance metrics were compared between the programs to determine the most accurate program for loss-of-function and gain-of-function mechanisms. No one program correctly predicted the pathogenicity of all variants analyzed. A major hindrance to the analysis was the lack of output from a significant portion of the programs. The best performer was MutPred, which had a weighted accuracy of 82.6% in the full dataset. Surprisingly, combining the results of the top three programs did not increase the ability to predict pathogenicity over the top performer alone. As the increasing number of sequence changes in larger datasets will require interpretation, the current study demonstrates that extreme caution must be taken when reporting pathogenicity based on statistical online protein prediction programs in the absence of functional studies. BlackWell Publishing Ltd 2015-03 2014-12-03 /pmc/articles/PMC4367082/ /pubmed/25802880 http://dx.doi.org/10.1002/mgg3.116 Text en © 2014 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals, Inc. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Method Walters-Sen, Lauren C Hashimoto, Sayaka Thrush, Devon Lamb Reshmi, Shalini Gastier-Foster, Julie M Astbury, Caroline Pyatt, Robert E Variability in pathogenicity prediction programs: impact on clinical diagnostics |
title | Variability in pathogenicity prediction programs: impact on clinical diagnostics |
title_full | Variability in pathogenicity prediction programs: impact on clinical diagnostics |
title_fullStr | Variability in pathogenicity prediction programs: impact on clinical diagnostics |
title_full_unstemmed | Variability in pathogenicity prediction programs: impact on clinical diagnostics |
title_short | Variability in pathogenicity prediction programs: impact on clinical diagnostics |
title_sort | variability in pathogenicity prediction programs: impact on clinical diagnostics |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4367082/ https://www.ncbi.nlm.nih.gov/pubmed/25802880 http://dx.doi.org/10.1002/mgg3.116 |
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