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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...

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Autores principales: Garcia, Felipe Antonio de Oliveira, de Andrade, Edilene Santos, Palmero, Edenir Inez
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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.
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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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