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MAGI-ACMG: Algorithm for the Classification of Variants According to ACMG and ACGS Recommendations
We have developed MAGI-ACMG, a classification algorithm that allows the classification of sequencing variants (single nucleotide or small indels) according to the recommendations of the American College of Medical Genetics (ACMG) and the Association for Clinical Genomic Science (ACGS). The MAGI-ACMG...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10454715/ https://www.ncbi.nlm.nih.gov/pubmed/37628650 http://dx.doi.org/10.3390/genes14081600 |
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author | Cristofoli, Francesca Daja, Muharrem Maltese, Paolo Enrico Guerri, Giulia Tanzi, Benedetta Miotto, Roberta Bonetti, Gabriele Miertus, Jan Chiurazzi, Pietro Stuppia, Liborio Gatta, Valentina Cecchin, Stefano Bertelli, Matteo Marceddu, Giuseppe |
author_facet | Cristofoli, Francesca Daja, Muharrem Maltese, Paolo Enrico Guerri, Giulia Tanzi, Benedetta Miotto, Roberta Bonetti, Gabriele Miertus, Jan Chiurazzi, Pietro Stuppia, Liborio Gatta, Valentina Cecchin, Stefano Bertelli, Matteo Marceddu, Giuseppe |
author_sort | Cristofoli, Francesca |
collection | PubMed |
description | We have developed MAGI-ACMG, a classification algorithm that allows the classification of sequencing variants (single nucleotide or small indels) according to the recommendations of the American College of Medical Genetics (ACMG) and the Association for Clinical Genomic Science (ACGS). The MAGI-ACMG classification algorithm uses information retrieved through the VarSome Application Programming Interface (API), integrates the AutoPVS1 tool in order to evaluate more precisely the attribution of the PVS1 criterion, and performs the customized assignment of specific criteria. In addition, we propose a sub-classification scheme for variants of uncertain significance (VUS) according to their proximity either towards the “likely pathogenic” or “likely benign” classes. We also conceived a pathogenicity potential criterion (P_POT) as a proxy for segregation criteria that might be added to a VUS after posterior testing, thus allowing it to upgrade its clinical significance in a diagnostic reporting setting. Finally, we have developed a user-friendly web application based on the MAGI-ACMG algorithm, available to geneticists for variant interpretation. |
format | Online Article Text |
id | pubmed-10454715 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104547152023-08-26 MAGI-ACMG: Algorithm for the Classification of Variants According to ACMG and ACGS Recommendations Cristofoli, Francesca Daja, Muharrem Maltese, Paolo Enrico Guerri, Giulia Tanzi, Benedetta Miotto, Roberta Bonetti, Gabriele Miertus, Jan Chiurazzi, Pietro Stuppia, Liborio Gatta, Valentina Cecchin, Stefano Bertelli, Matteo Marceddu, Giuseppe Genes (Basel) Article We have developed MAGI-ACMG, a classification algorithm that allows the classification of sequencing variants (single nucleotide or small indels) according to the recommendations of the American College of Medical Genetics (ACMG) and the Association for Clinical Genomic Science (ACGS). The MAGI-ACMG classification algorithm uses information retrieved through the VarSome Application Programming Interface (API), integrates the AutoPVS1 tool in order to evaluate more precisely the attribution of the PVS1 criterion, and performs the customized assignment of specific criteria. In addition, we propose a sub-classification scheme for variants of uncertain significance (VUS) according to their proximity either towards the “likely pathogenic” or “likely benign” classes. We also conceived a pathogenicity potential criterion (P_POT) as a proxy for segregation criteria that might be added to a VUS after posterior testing, thus allowing it to upgrade its clinical significance in a diagnostic reporting setting. Finally, we have developed a user-friendly web application based on the MAGI-ACMG algorithm, available to geneticists for variant interpretation. MDPI 2023-08-08 /pmc/articles/PMC10454715/ /pubmed/37628650 http://dx.doi.org/10.3390/genes14081600 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cristofoli, Francesca Daja, Muharrem Maltese, Paolo Enrico Guerri, Giulia Tanzi, Benedetta Miotto, Roberta Bonetti, Gabriele Miertus, Jan Chiurazzi, Pietro Stuppia, Liborio Gatta, Valentina Cecchin, Stefano Bertelli, Matteo Marceddu, Giuseppe MAGI-ACMG: Algorithm for the Classification of Variants According to ACMG and ACGS Recommendations |
title | MAGI-ACMG: Algorithm for the Classification of Variants According to ACMG and ACGS Recommendations |
title_full | MAGI-ACMG: Algorithm for the Classification of Variants According to ACMG and ACGS Recommendations |
title_fullStr | MAGI-ACMG: Algorithm for the Classification of Variants According to ACMG and ACGS Recommendations |
title_full_unstemmed | MAGI-ACMG: Algorithm for the Classification of Variants According to ACMG and ACGS Recommendations |
title_short | MAGI-ACMG: Algorithm for the Classification of Variants According to ACMG and ACGS Recommendations |
title_sort | magi-acmg: algorithm for the classification of variants according to acmg and acgs recommendations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10454715/ https://www.ncbi.nlm.nih.gov/pubmed/37628650 http://dx.doi.org/10.3390/genes14081600 |
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