NOTICE: Due to a lapse in annual appropriations, most of this website is not being updated. Learn more.
Form submissions will still be accepted but will not receive responses at this time. Sections of this site for programs using non-appropriated funds (such as NVLAP) or those that are excepted from the shutdown (such as CHIPS and NVD) will continue to be updated.
An official website of the United States government
Here’s how you know
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Variant calling and benchmarking in an era of complete human genome sequences
Published
Author(s)
Nathanael David Olson, Justin Wagner, Nathan Dwarshuis, Karen Miga, Marc L. Salit, Justin Zook
Abstract
Genetic variant calling from DNA sequencing has enabled understanding of germline variation in hundreds of thousands of humans. Sequencing technologies and variant-calling methods have advanced rapidly, routinely providing reliable variant calls in most of the human genome. We describe how advances in long reads, deep learning, de novo assembly and pangenomes have expanded access to variant calls in increasingly challenging, repetitive genomic regions, including medically relevant regions, and how new benchmark sets and benchmarking methods illuminate their strengths and limitations. Finally, we explore the possible future of more complete characterization of human genome variation in light of the recent completion of a telomere-to-telomere human genome reference assembly and human pangenomes, and we consider the innovations needed to benchmark their newly accessible repetitive regions and complex variants.
Olson, N.
, Wagner, J.
, Dwarshuis, N.
, Miga, K.
, Salit, M.
and Zook, J.
(2023),
Variant calling and benchmarking in an era of complete human genome sequences, Nature Reviews Genetics, [online], https://doi.org/10.1038/s41576-023-00590-0, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935532
(Accessed October 13, 2025)