The GIAB genomic stratifications resource for human reference genomes

Nathan Dwarshuis, Divya Kalra, Jennifer McDaniel, Philippe Sanio, Pilar Alvarez Jerez, Bharati Jadhav, Wenyu (Eddy) Huang, Rajarshi Mondal, Ben Busby, Nathan D. Olson, Fritz J. Sedlazeck, Justin Wagner, Sina Majidian () and Justin M. Zook ()
Additional contact information
Nathan Dwarshuis: National Institute of Standards and Technology
Divya Kalra: Baylor College of Medicine
Jennifer McDaniel: National Institute of Standards and Technology
Philippe Sanio: University of Applied Sciences Upper Austria - FH Hagenberg
Pilar Alvarez Jerez: National Institutes of Health
Bharati Jadhav: Hess Center for Science and Medicine
Wenyu (Eddy) Huang: Rice University
Rajarshi Mondal: Pondicherry University
Ben Busby: DNA Nexus
Nathan D. Olson: National Institute of Standards and Technology
Fritz J. Sedlazeck: Baylor College of Medicine
Justin Wagner: National Institute of Standards and Technology
Sina Majidian: University of Lausanne
Justin M. Zook: National Institute of Standards and Technology

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Despite the growing variety of sequencing and variant-calling tools, no workflow performs equally well across the entire human genome. Understanding context-dependent performance is critical for enabling researchers, clinicians, and developers to make informed tradeoffs when selecting sequencing hardware and software. Here we describe a set of “stratifications,” which are BED files that define distinct contexts throughout the genome. We define these for GRCh37/38 as well as the new T2T-CHM13 reference, adding many new hard-to-sequence regions which are critical for understanding performance as the field progresses. Specifically, we highlight the increase in hard-to-map and GC-rich stratifications in CHM13 relative to the previous references. We then compare the benchmarking performance with each reference and show the performance penalty brought about by these additional difficult regions in CHM13. Additionally, we demonstrate how the stratifications can track context-specific improvements over different platform iterations, using Oxford Nanopore Technologies as an example. The means to generate these stratifications are available as a snakemake pipeline at https://github.com/usnistgov/giab-stratifications . We anticipate this being useful in enabling precise risk-reward calculations when building sequencing pipelines for any of the commonly-used reference genomes.

Date: 2024
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DOI: 10.1038/s41467-024-53260-y

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