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Genomic variant benchmark: if you cannot measure it, you cannot improve it
Genomic benchmark datasets are essential to driving the field of genomics and bioinformatics. They provide a snapshot of the performances of sequencing technologies and analytical methods and highlight future challenges. However, they depend on sequencing technology, reference genome, and available...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10552390/ https://www.ncbi.nlm.nih.gov/pubmed/37798733 http://dx.doi.org/10.1186/s13059-023-03061-1 |
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author | Majidian, Sina Agustinho, Daniel Paiva Chin, Chen-Shan Sedlazeck, Fritz J. Mahmoud, Medhat |
author_facet | Majidian, Sina Agustinho, Daniel Paiva Chin, Chen-Shan Sedlazeck, Fritz J. Mahmoud, Medhat |
author_sort | Majidian, Sina |
collection | PubMed |
description | Genomic benchmark datasets are essential to driving the field of genomics and bioinformatics. They provide a snapshot of the performances of sequencing technologies and analytical methods and highlight future challenges. However, they depend on sequencing technology, reference genome, and available benchmarking methods. Thus, creating a genomic benchmark dataset is laborious and highly challenging, often involving multiple sequencing technologies, different variant calling tools, and laborious manual curation. In this review, we discuss the available benchmark datasets and their utility. Additionally, we focus on the most recent benchmark of genes with medical relevance and challenging genomic complexity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03061-1. |
format | Online Article Text |
id | pubmed-10552390 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105523902023-10-06 Genomic variant benchmark: if you cannot measure it, you cannot improve it Majidian, Sina Agustinho, Daniel Paiva Chin, Chen-Shan Sedlazeck, Fritz J. Mahmoud, Medhat Genome Biol Review Genomic benchmark datasets are essential to driving the field of genomics and bioinformatics. They provide a snapshot of the performances of sequencing technologies and analytical methods and highlight future challenges. However, they depend on sequencing technology, reference genome, and available benchmarking methods. Thus, creating a genomic benchmark dataset is laborious and highly challenging, often involving multiple sequencing technologies, different variant calling tools, and laborious manual curation. In this review, we discuss the available benchmark datasets and their utility. Additionally, we focus on the most recent benchmark of genes with medical relevance and challenging genomic complexity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03061-1. BioMed Central 2023-10-05 /pmc/articles/PMC10552390/ /pubmed/37798733 http://dx.doi.org/10.1186/s13059-023-03061-1 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 | Review Majidian, Sina Agustinho, Daniel Paiva Chin, Chen-Shan Sedlazeck, Fritz J. Mahmoud, Medhat Genomic variant benchmark: if you cannot measure it, you cannot improve it |
title | Genomic variant benchmark: if you cannot measure it, you cannot improve it |
title_full | Genomic variant benchmark: if you cannot measure it, you cannot improve it |
title_fullStr | Genomic variant benchmark: if you cannot measure it, you cannot improve it |
title_full_unstemmed | Genomic variant benchmark: if you cannot measure it, you cannot improve it |
title_short | Genomic variant benchmark: if you cannot measure it, you cannot improve it |
title_sort | genomic variant benchmark: if you cannot measure it, you cannot improve it |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10552390/ https://www.ncbi.nlm.nih.gov/pubmed/37798733 http://dx.doi.org/10.1186/s13059-023-03061-1 |
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