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MmisAT and MmisP: an efficient and accurate suite of variant analysis toolkit for primary mitochondrial diseases
Recent advances in next-generation sequencing (NGS) technology have greatly accelerated the need for efficient annotation to accurately interpret clinically relevant genetic variants in human diseases. Therefore, it is crucial to develop appropriate analytical tools to improve the interpretation of...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10683248/ https://www.ncbi.nlm.nih.gov/pubmed/38012712 http://dx.doi.org/10.1186/s40246-023-00557-6 |
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author | Huang, Shuangshuang Wu, Zhaoyu Wang, Tong Yu, Rui Song, Zhijian Wang, Hao |
author_facet | Huang, Shuangshuang Wu, Zhaoyu Wang, Tong Yu, Rui Song, Zhijian Wang, Hao |
author_sort | Huang, Shuangshuang |
collection | PubMed |
description | Recent advances in next-generation sequencing (NGS) technology have greatly accelerated the need for efficient annotation to accurately interpret clinically relevant genetic variants in human diseases. Therefore, it is crucial to develop appropriate analytical tools to improve the interpretation of disease variants. Given the unique genetic characteristics of mitochondria, including haplogroup, heteroplasmy, and maternal inheritance, we developed a suite of variant analysis toolkits specifically designed for primary mitochondrial diseases: the Mitochondrial Missense Variant Annotation Tool (MmisAT) and the Mitochondrial Missense Variant Pathogenicity Predictor (MmisP). MmisAT can handle protein-coding variants from both nuclear DNA and mtDNA and generate 349 annotation types across six categories. It processes 4.78 million variant data in 76 min, making it a valuable resource for clinical and research applications. Additionally, MmisP provides pathogenicity scores to predict the pathogenicity of genetic variations in mitochondrial disease. It has been validated using cross-validation and external datasets and demonstrated higher overall discriminant accuracy with a receiver operating characteristic (ROC) curve area under the curve (AUC) of 0.94, outperforming existing pathogenicity predictors. In conclusion, the MmisAT is an efficient tool that greatly facilitates the process of variant annotation, expanding the scope of variant annotation information. Furthermore, the development of MmisP provides valuable insights into the creation of disease-specific, phenotype-specific, and even gene-specific predictors of pathogenicity, further advancing our understanding of specific fields. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40246-023-00557-6. |
format | Online Article Text |
id | pubmed-10683248 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106832482023-11-30 MmisAT and MmisP: an efficient and accurate suite of variant analysis toolkit for primary mitochondrial diseases Huang, Shuangshuang Wu, Zhaoyu Wang, Tong Yu, Rui Song, Zhijian Wang, Hao Hum Genomics Research Recent advances in next-generation sequencing (NGS) technology have greatly accelerated the need for efficient annotation to accurately interpret clinically relevant genetic variants in human diseases. Therefore, it is crucial to develop appropriate analytical tools to improve the interpretation of disease variants. Given the unique genetic characteristics of mitochondria, including haplogroup, heteroplasmy, and maternal inheritance, we developed a suite of variant analysis toolkits specifically designed for primary mitochondrial diseases: the Mitochondrial Missense Variant Annotation Tool (MmisAT) and the Mitochondrial Missense Variant Pathogenicity Predictor (MmisP). MmisAT can handle protein-coding variants from both nuclear DNA and mtDNA and generate 349 annotation types across six categories. It processes 4.78 million variant data in 76 min, making it a valuable resource for clinical and research applications. Additionally, MmisP provides pathogenicity scores to predict the pathogenicity of genetic variations in mitochondrial disease. It has been validated using cross-validation and external datasets and demonstrated higher overall discriminant accuracy with a receiver operating characteristic (ROC) curve area under the curve (AUC) of 0.94, outperforming existing pathogenicity predictors. In conclusion, the MmisAT is an efficient tool that greatly facilitates the process of variant annotation, expanding the scope of variant annotation information. Furthermore, the development of MmisP provides valuable insights into the creation of disease-specific, phenotype-specific, and even gene-specific predictors of pathogenicity, further advancing our understanding of specific fields. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40246-023-00557-6. BioMed Central 2023-11-27 /pmc/articles/PMC10683248/ /pubmed/38012712 http://dx.doi.org/10.1186/s40246-023-00557-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Huang, Shuangshuang Wu, Zhaoyu Wang, Tong Yu, Rui Song, Zhijian Wang, Hao MmisAT and MmisP: an efficient and accurate suite of variant analysis toolkit for primary mitochondrial diseases |
title | MmisAT and MmisP: an efficient and accurate suite of variant analysis toolkit for primary mitochondrial diseases |
title_full | MmisAT and MmisP: an efficient and accurate suite of variant analysis toolkit for primary mitochondrial diseases |
title_fullStr | MmisAT and MmisP: an efficient and accurate suite of variant analysis toolkit for primary mitochondrial diseases |
title_full_unstemmed | MmisAT and MmisP: an efficient and accurate suite of variant analysis toolkit for primary mitochondrial diseases |
title_short | MmisAT and MmisP: an efficient and accurate suite of variant analysis toolkit for primary mitochondrial diseases |
title_sort | mmisat and mmisp: an efficient and accurate suite of variant analysis toolkit for primary mitochondrial diseases |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10683248/ https://www.ncbi.nlm.nih.gov/pubmed/38012712 http://dx.doi.org/10.1186/s40246-023-00557-6 |
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