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Insights on variant analysis in silico tools for pathogenicity prediction
Molecular biology is currently a fast-advancing science. Sequencing techniques are getting cheaper, but the interpretation of genetic variants requires expertise and computational power, therefore is still a challenge. Next-generation sequencing releases thousands of variants and to classify them, r...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9774026/ https://www.ncbi.nlm.nih.gov/pubmed/36568376 http://dx.doi.org/10.3389/fgene.2022.1010327 |
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author | Garcia, Felipe Antonio de Oliveira de Andrade, Edilene Santos Palmero, Edenir Inez |
author_facet | Garcia, Felipe Antonio de Oliveira de Andrade, Edilene Santos Palmero, Edenir Inez |
author_sort | Garcia, Felipe Antonio de Oliveira |
collection | PubMed |
description | Molecular biology is currently a fast-advancing science. Sequencing techniques are getting cheaper, but the interpretation of genetic variants requires expertise and computational power, therefore is still a challenge. Next-generation sequencing releases thousands of variants and to classify them, researchers propose protocols with several parameters. Here we present a review of several in silico pathogenicity prediction tools involved in the variant prioritization/classification process used by some international protocols for variant analysis and studies evaluating their efficiency. |
format | Online Article Text |
id | pubmed-9774026 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97740262022-12-23 Insights on variant analysis in silico tools for pathogenicity prediction Garcia, Felipe Antonio de Oliveira de Andrade, Edilene Santos Palmero, Edenir Inez Front Genet Genetics Molecular biology is currently a fast-advancing science. Sequencing techniques are getting cheaper, but the interpretation of genetic variants requires expertise and computational power, therefore is still a challenge. Next-generation sequencing releases thousands of variants and to classify them, researchers propose protocols with several parameters. Here we present a review of several in silico pathogenicity prediction tools involved in the variant prioritization/classification process used by some international protocols for variant analysis and studies evaluating their efficiency. Frontiers Media S.A. 2022-11-29 /pmc/articles/PMC9774026/ /pubmed/36568376 http://dx.doi.org/10.3389/fgene.2022.1010327 Text en Copyright © 2022 Garcia, Andrade and Palmero. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Garcia, Felipe Antonio de Oliveira de Andrade, Edilene Santos Palmero, Edenir Inez Insights on variant analysis in silico tools for pathogenicity prediction |
title | Insights on variant analysis in silico tools for pathogenicity prediction |
title_full | Insights on variant analysis in silico tools for pathogenicity prediction |
title_fullStr | Insights on variant analysis in silico tools for pathogenicity prediction |
title_full_unstemmed | Insights on variant analysis in silico tools for pathogenicity prediction |
title_short | Insights on variant analysis in silico tools for pathogenicity prediction |
title_sort | insights on variant analysis in silico tools for pathogenicity prediction |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9774026/ https://www.ncbi.nlm.nih.gov/pubmed/36568376 http://dx.doi.org/10.3389/fgene.2022.1010327 |
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