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PAnno: A pharmacogenomics annotation tool for clinical genomic testing
Introduction: Next-generation sequencing (NGS) technologies have been widely used in clinical genomic testing for drug response phenotypes. However, the inherent limitations of short reads make accurate inference of diplotypes still challenging, which may reduce the effectiveness of genotype-guided...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9909284/ https://www.ncbi.nlm.nih.gov/pubmed/36778023 http://dx.doi.org/10.3389/fphar.2023.1008330 |
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author | Liu, Yaqing Lin, Zipeng Chen, Qingwang Chen, Qiaochu Sang, Leqing Wang, Yunjin Shi, Leming Guo, Li Yu, Ying |
author_facet | Liu, Yaqing Lin, Zipeng Chen, Qingwang Chen, Qiaochu Sang, Leqing Wang, Yunjin Shi, Leming Guo, Li Yu, Ying |
author_sort | Liu, Yaqing |
collection | PubMed |
description | Introduction: Next-generation sequencing (NGS) technologies have been widely used in clinical genomic testing for drug response phenotypes. However, the inherent limitations of short reads make accurate inference of diplotypes still challenging, which may reduce the effectiveness of genotype-guided drug therapy. Methods: An automated Pharmacogenomics Annotation tool (PAnno) was implemented, which reports prescribing recommendations and phenotypes by parsing the germline variant call format (VCF) file from NGS and the population to which the individual belongs. Results: A ranking model dedicated to inferring diplotypes, developed based on the allele (haplotype) definition and population allele frequency, was introduced in PAnno. The predictive performance was validated in comparison with four similar tools using the consensus diplotype data of the Genetic Testing Reference Materials Coordination Program (GeT-RM) as ground truth. An annotation method was proposed to summarize prescribing recommendations and classify drugs into avoid use, use with caution, and routine use, following the recommendations of the Clinical Pharmacogenetics Implementation Consortium (CPIC), etc. It further predicts phenotypes of specific drugs in terms of toxicity, dosage, efficacy, and metabolism by integrating the high-confidence clinical annotations in the Pharmacogenomics Knowledgebase (PharmGKB). PAnno is available at https://github.com/PreMedKB/PAnno. Discussion: PAnno provides an end-to-end clinical pharmacogenomics decision support solution by resolving, annotating, and reporting germline variants. |
format | Online Article Text |
id | pubmed-9909284 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99092842023-02-10 PAnno: A pharmacogenomics annotation tool for clinical genomic testing Liu, Yaqing Lin, Zipeng Chen, Qingwang Chen, Qiaochu Sang, Leqing Wang, Yunjin Shi, Leming Guo, Li Yu, Ying Front Pharmacol Pharmacology Introduction: Next-generation sequencing (NGS) technologies have been widely used in clinical genomic testing for drug response phenotypes. However, the inherent limitations of short reads make accurate inference of diplotypes still challenging, which may reduce the effectiveness of genotype-guided drug therapy. Methods: An automated Pharmacogenomics Annotation tool (PAnno) was implemented, which reports prescribing recommendations and phenotypes by parsing the germline variant call format (VCF) file from NGS and the population to which the individual belongs. Results: A ranking model dedicated to inferring diplotypes, developed based on the allele (haplotype) definition and population allele frequency, was introduced in PAnno. The predictive performance was validated in comparison with four similar tools using the consensus diplotype data of the Genetic Testing Reference Materials Coordination Program (GeT-RM) as ground truth. An annotation method was proposed to summarize prescribing recommendations and classify drugs into avoid use, use with caution, and routine use, following the recommendations of the Clinical Pharmacogenetics Implementation Consortium (CPIC), etc. It further predicts phenotypes of specific drugs in terms of toxicity, dosage, efficacy, and metabolism by integrating the high-confidence clinical annotations in the Pharmacogenomics Knowledgebase (PharmGKB). PAnno is available at https://github.com/PreMedKB/PAnno. Discussion: PAnno provides an end-to-end clinical pharmacogenomics decision support solution by resolving, annotating, and reporting germline variants. Frontiers Media S.A. 2023-01-26 /pmc/articles/PMC9909284/ /pubmed/36778023 http://dx.doi.org/10.3389/fphar.2023.1008330 Text en Copyright © 2023 Liu, Lin, Chen, Chen, Sang, Wang, Shi, Guo and Yu. 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 | Pharmacology Liu, Yaqing Lin, Zipeng Chen, Qingwang Chen, Qiaochu Sang, Leqing Wang, Yunjin Shi, Leming Guo, Li Yu, Ying PAnno: A pharmacogenomics annotation tool for clinical genomic testing |
title | PAnno: A pharmacogenomics annotation tool for clinical genomic testing |
title_full | PAnno: A pharmacogenomics annotation tool for clinical genomic testing |
title_fullStr | PAnno: A pharmacogenomics annotation tool for clinical genomic testing |
title_full_unstemmed | PAnno: A pharmacogenomics annotation tool for clinical genomic testing |
title_short | PAnno: A pharmacogenomics annotation tool for clinical genomic testing |
title_sort | panno: a pharmacogenomics annotation tool for clinical genomic testing |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9909284/ https://www.ncbi.nlm.nih.gov/pubmed/36778023 http://dx.doi.org/10.3389/fphar.2023.1008330 |
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