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Consistency of the Tools That Predict the Impact of Single Nucleotide Variants (SNVs) on Gene Functionality: The BRCA1 Gene
Single nucleotide variants (SNVs) occurring in a protein coding gene may disrupt its function in multiple ways. Predicting this disruption has been recognized as an important problem in bioinformatics research. Many tools, hereafter p-tools, have been designed to perform these predictions and many o...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7175253/ https://www.ncbi.nlm.nih.gov/pubmed/32244891 http://dx.doi.org/10.3390/biom10030475 |
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author | Murillo, Javier Spetale, Flavio Guillaume, Serge Bulacio, Pilar Garcia Labari, Ignacio Cailloux, Olivier Destercke, Sebastien Tapia, Elizabeth |
author_facet | Murillo, Javier Spetale, Flavio Guillaume, Serge Bulacio, Pilar Garcia Labari, Ignacio Cailloux, Olivier Destercke, Sebastien Tapia, Elizabeth |
author_sort | Murillo, Javier |
collection | PubMed |
description | Single nucleotide variants (SNVs) occurring in a protein coding gene may disrupt its function in multiple ways. Predicting this disruption has been recognized as an important problem in bioinformatics research. Many tools, hereafter p-tools, have been designed to perform these predictions and many of them are now of common use in scientific research, even in clinical applications. This highlights the importance of understanding the semantics of their outputs. To shed light on this issue, two questions are formulated, (i) do p-tools provide similar predictions? (inner consistency), and (ii) are these predictions consistent with the literature? (outer consistency). To answer these, six p-tools are evaluated with exhaustive SNV datasets from the BRCA1 gene. Two indices, called [Formula: see text] and [Formula: see text] , are proposed to quantify the inner consistency of pairs of p-tools while the outer consistency is quantified by standard information retrieval metrics. While the inner consistency analysis reveals that most of the p-tools are not consistent with each other, the outer consistency analysis reveals they are characterized by a low prediction performance. Although this result highlights the need of improving the prediction performance of individual p-tools, the inner consistency results pave the way to the systematic design of truly diverse ensembles of p-tools that can overcome the limitations of individual members. |
format | Online Article Text |
id | pubmed-7175253 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-71752532020-04-28 Consistency of the Tools That Predict the Impact of Single Nucleotide Variants (SNVs) on Gene Functionality: The BRCA1 Gene Murillo, Javier Spetale, Flavio Guillaume, Serge Bulacio, Pilar Garcia Labari, Ignacio Cailloux, Olivier Destercke, Sebastien Tapia, Elizabeth Biomolecules Article Single nucleotide variants (SNVs) occurring in a protein coding gene may disrupt its function in multiple ways. Predicting this disruption has been recognized as an important problem in bioinformatics research. Many tools, hereafter p-tools, have been designed to perform these predictions and many of them are now of common use in scientific research, even in clinical applications. This highlights the importance of understanding the semantics of their outputs. To shed light on this issue, two questions are formulated, (i) do p-tools provide similar predictions? (inner consistency), and (ii) are these predictions consistent with the literature? (outer consistency). To answer these, six p-tools are evaluated with exhaustive SNV datasets from the BRCA1 gene. Two indices, called [Formula: see text] and [Formula: see text] , are proposed to quantify the inner consistency of pairs of p-tools while the outer consistency is quantified by standard information retrieval metrics. While the inner consistency analysis reveals that most of the p-tools are not consistent with each other, the outer consistency analysis reveals they are characterized by a low prediction performance. Although this result highlights the need of improving the prediction performance of individual p-tools, the inner consistency results pave the way to the systematic design of truly diverse ensembles of p-tools that can overcome the limitations of individual members. MDPI 2020-03-20 /pmc/articles/PMC7175253/ /pubmed/32244891 http://dx.doi.org/10.3390/biom10030475 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Murillo, Javier Spetale, Flavio Guillaume, Serge Bulacio, Pilar Garcia Labari, Ignacio Cailloux, Olivier Destercke, Sebastien Tapia, Elizabeth Consistency of the Tools That Predict the Impact of Single Nucleotide Variants (SNVs) on Gene Functionality: The BRCA1 Gene |
title | Consistency of the Tools That Predict the Impact of Single Nucleotide Variants (SNVs) on Gene Functionality: The BRCA1 Gene |
title_full | Consistency of the Tools That Predict the Impact of Single Nucleotide Variants (SNVs) on Gene Functionality: The BRCA1 Gene |
title_fullStr | Consistency of the Tools That Predict the Impact of Single Nucleotide Variants (SNVs) on Gene Functionality: The BRCA1 Gene |
title_full_unstemmed | Consistency of the Tools That Predict the Impact of Single Nucleotide Variants (SNVs) on Gene Functionality: The BRCA1 Gene |
title_short | Consistency of the Tools That Predict the Impact of Single Nucleotide Variants (SNVs) on Gene Functionality: The BRCA1 Gene |
title_sort | consistency of the tools that predict the impact of single nucleotide variants (snvs) on gene functionality: the brca1 gene |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7175253/ https://www.ncbi.nlm.nih.gov/pubmed/32244891 http://dx.doi.org/10.3390/biom10030475 |
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