The GIAB genomic stratifications resource for human reference genomes

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.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2024-10-18
DOI
https://doi.org/10.1038/s41467-024-53260-y
Citations
42
Primary Topic
Genomics and Phylogenetic Studies
Type
article
Field-Weighted Citation Impact
9.59

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The GIAB genomic stratifications resource for human reference genomes

Bharati Jadhav, Sina Majidian, Pilar Álvarez Jerez, Rajarshi Mondal et al.
42 citations
Nature Communications
Genomics and Phylogenetic Studies
9.59
article

The GIAB genomic stratifications resource for human reference genomes

Bharati Jadhav, Sina Majidian, Pilar Álvarez Jerez, Rajarshi Mondal, Fritz J. Sedlazeck, Jennifer McDaniel, Philippe Sanio, Justin M. Zook, Nathan D. Olson, Ben Busby, Nathan Dwarshuis, Justin Wagner, Wenyu Huang, Divya Kalra
article en
42 citations

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.

Nature CommunicationsVol. 15(1)
SIB Swiss Institute of Bioinformatics (CH), National Institutes of Health (US), National Institute of Standards and Technology (US), Pondicherry University (IN), Baylor College of Medicine (US), National Institute of Neurological Disorders and Stroke (US), Child Health and Development Institute (US), University of Applied Sciences Upper Austria (AT), National Institute on Aging (US), Material Measurement Laboratory (US), National Hospital for Neurology and Neurosurgery (GB), University College London (GB), Rice University (US), University of Lausanne (CH)
National Institute of Standards and Technology
Openalex Percentile: Top 1%
Genomics and Phylogenetic Studies
9.59
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.