Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Majidian, Sina, Agustinho, Daniel Paiva, Chin, Chen-Shan, Sedlazeck, Fritz J., Mahmoud, Medhat
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
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
_version_ 1785115952723001344
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
work_keys_str_mv AT majidiansina genomicvariantbenchmarkifyoucannotmeasureityoucannotimproveit
AT agustinhodanielpaiva genomicvariantbenchmarkifyoucannotmeasureityoucannotimproveit
AT chinchenshan genomicvariantbenchmarkifyoucannotmeasureityoucannotimproveit
AT sedlazeckfritzj genomicvariantbenchmarkifyoucannotmeasureityoucannotimproveit
AT mahmoudmedhat genomicvariantbenchmarkifyoucannotmeasureityoucannotimproveit