Genomic variant benchmark: if you cannot measure it, you cannot improve it

Genome Biology

Review

Daniel Paiva Agustinho¹  Fritz J. Sedlazeck²  Medhat Mahmoud³  Sina Majidian⁴  Chen-Shan Chin⁵ 

[1] Baylor College of Medicine, Human Genome Sequencing Center, 77030, Houston, TX, USA;Baylor College of Medicine, Human Genome Sequencing Center, 77030, Houston, TX, USA;Department of Computer Science, Rice University, 6100 Main Street, 77005, Houston, TX, USA;Baylor College of Medicine, Human Genome Sequencing Center, 77030, Houston, TX, USA;Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA;Department of Computational Biology, University of Lausanne, 1015, Lausanne, Switzerland;SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland;Sema4 OpCo, Inc., 06405, Stamford, CT, USA;

关键词: Genetic variation;    SNPs;    Indels;    Structural variant;    Benchmark datasets;    Medical genes;    Sequencing technology;   

DOI  :  10.1186/s13059-023-03061-1

 received in 2022-11-28, accepted in 2023-09-18,  发布年份 2023

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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