Peer-reviewedCC BYCell
A complete diploid human genome benchmark for personalized genomics
40 affiliations
- 1Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- 2Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
- 3Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA; Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
- 4Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- 5University of California, Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
- 6UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA
- 7Department of Genetics, Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- 8Division of Medical Genetics, Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA
- 9Division of Medical Genetics, University of Washington School of Medicine, Seattle, WA, USA
- 10Institute for Systems Genomics, University of Connecticut, Storrs, CT, USA
- 11Department of Computer Science, Old Dominion University, Norfolk, VA, USA
- 12Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA
- 13Chan Zuckerberg Biohub, San Francisco, San Francisco, CA, USA
- 14Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
- 15UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
- 16Computational Genomics Unit, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- 17Graduate Program in Bioinformatics and Systems Biology, University of California, San Diego, La Jolla, CA, USA
- 18Department of Biology, Johns Hopkins University, Baltimore, MD, USA
- 19Google LLC, Mountain View, CA, USA
- 20Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing 100101, China
- 21Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing, 100101, China
- 22Core Unit Bioinformatics, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany; Center for Digital Medicine, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
- 23Center for Digital Medicine, Heinrich Heine University Dusseldorf, Germany
- 24Core Unit Bioinformatics, Medical Faculty and University Hospital Dusseldorf, Heinrich Heine University Dusseldorf, Germany
- 25Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- 26Broad Institute, Broad Clinical Labs, Burlington, MA, USA
- 27Vertebrate Genome Laboratory, the Rockefeller University, New York, NY, USA
- 28The Vertebrate Genome Laboratory, The Rockefeller University, NY, USA
- 29Bioinnovation and Genome Sciences, the Translational Genomics Research Institute (TGen), Phoenix, AZ 85004, USA
- 30Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
- 31Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA; Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK
- 32Department of Plant Sciences, University of Cambridge, Cambridge, UK, CB2 3EA
- 33Division of Animal Sciences, University of Missouri - Columbia, Columbia, MO, USA
- 34Division of Animal Sciences, University of Missouri, Columbia, MO, USA
- 35Oxford Nanopore Technologies, Oxford, UK
- 36Laboratory for Bioinformatics and Computational Biology, Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
- 37Coriell Institute for Medical Research, Camden, NJ, USA
- 38Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA; Genome Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany
- 39Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- 40Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, 69117, Germany
Abstract
Human genome resequencing typically involves mapping reads to a reference genome to call variants; however, this approach suffers from both technical and reference biases, leaving many duplicated and structurally polymorphic regions of the genome unmapped. Consequently, existing variant benchmarks, generated by the same methods, fail to assess these complex regions. To address this limitation, we present a telomere-to-telomere genome benchmark that achieves near-perfect accuracy (i.e. no detectable errors) across 99.4% of the complete, diploid HG002 genome. This benchmark adds 701.4 Mb of autosomal sequence and both sex chromosomes (216.8 Mb), totaling 15.3% of the genome that was absent from prior benchmarks. We also provide a diploid annotation of genes, transposable elements, segmental duplications, and satellite repeats, including 39,144 protein-coding genes across both haplotypes. To facilitate application of the benchmark, we developed tools for measuring the accuracy of sequencing reads, phased variant call sets, and genome assemblies against a diploid reference. Genome-wide analyses show that state-of-the-art de novo assembly methods resolve 2-7% more sequence and outperform variant calling accuracy by an order of magnitude, yielding just one error per 100 kb across 99.9% of the benchmark regions. Adoption of genome-based benchmarking is expected to accelerate the development of cost-effective methods for complete genome sequencing, expanding the reach of genomic medicine to the entire genome and enabling a new era of personalized genomics.
Related
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- Comparative mitochondrial genomics of the medicinal mushroom Phellinus tuberculosus reveals conserved core genes and dynamic genome evolution within Hymenochaetaceae
BibTeX
@article{hansen2026complete,
title = {{A complete diploid human genome benchmark for personalized genomics}},
author = {Nancy Fisher Hansen and Nathan J. Dwarshuis and Hyun Joo Ji and Arang Rhie and Hailey Loucks and Glennis A. Logsdon and Mitchell Robert Vollger and Jessica M. Storer and Juhyun Kim and Eleni Adam and Nicolas Altemose and Dmitry Antipov and Mobin Asri and Sofia N. Barreira and Stephanie C. Bohaczuk and Andrey V. Bzikadze and Sara A. Carioscia and Andrew W. Carroll and Kuan-Hao Chao and Yanan Chu and Arun Das and Peter Ebert and Adam C. English and Mark Fleharty and Laura E. Fleming and Giulio Formenti and Andrea Guarracino and Gabrielle A. Hartley and Katharine M. Jenike and Jenna Kalleberg and Yu Kang and Robert King and Josipa Lipovac and Mira Mastoras and Matthew W. Mitchell and Shloka Negi and Nathan D. Olson and Keisuke K. Oshima and Luis F. Paulin and Brandon D. Pickett and David Porubskỳ and Jane E. Ranchalis and Desh Ranjan and Mikko Rautiainen and Harold C. Riethman and Robert D. Schnabel and Fritz Joachim Sedlazeck and Kishwar Shafin and Mile Šikić and Steven J. Solar and Alexander P. Sweeten and Winston Timp and Justin Wagner and DongAhn Yoo and Ying Zhou and Erik P. Garrison and Evan E. Eichler and Michael C. Schatz and Andrew B. Stergachis and Rachel J. Waugh O'Neill},
journal = {Cell},
year = {2026},
month = aug,
doi = {10.1016/j.cell.2026.06.016},
url = {https://doi.org/10.1016/j.cell.2026.06.016},
note = {CC BY},
}
RIS
TY - JOUR
TI - A complete diploid human genome benchmark for personalized genomics
AU - Nancy Fisher Hansen
AU - Nathan J. Dwarshuis
AU - Hyun Joo Ji
AU - Arang Rhie
AU - Hailey Loucks
AU - Glennis A. Logsdon
AU - Mitchell Robert Vollger
AU - Jessica M. Storer
AU - Juhyun Kim
AU - Eleni Adam
AU - Nicolas Altemose
AU - Dmitry Antipov
AU - Mobin Asri
AU - Sofia N. Barreira
AU - Stephanie C. Bohaczuk
AU - Andrey V. Bzikadze
AU - Sara A. Carioscia
AU - Andrew W. Carroll
AU - Kuan-Hao Chao
AU - Yanan Chu
AU - Arun Das
AU - Peter Ebert
AU - Adam C. English
AU - Mark Fleharty
AU - Laura E. Fleming
AU - Giulio Formenti
AU - Andrea Guarracino
AU - Gabrielle A. Hartley
AU - Katharine M. Jenike
AU - Jenna Kalleberg
AU - Yu Kang
AU - Robert King
AU - Josipa Lipovac
AU - Mira Mastoras
AU - Matthew W. Mitchell
AU - Shloka Negi
AU - Nathan D. Olson
AU - Keisuke K. Oshima
AU - Luis F. Paulin
AU - Brandon D. Pickett
AU - David Porubskỳ
AU - Jane E. Ranchalis
AU - Desh Ranjan
AU - Mikko Rautiainen
AU - Harold C. Riethman
AU - Robert D. Schnabel
AU - Fritz Joachim Sedlazeck
AU - Kishwar Shafin
AU - Mile Šikić
AU - Steven J. Solar
AU - Alexander P. Sweeten
AU - Winston Timp
AU - Justin Wagner
AU - DongAhn Yoo
AU - Ying Zhou
AU - Erik P. Garrison
AU - Evan E. Eichler
AU - Michael C. Schatz
AU - Andrew B. Stergachis
AU - Rachel J. Waugh O'Neill
PY - 2026
DA - 2026/08/01
JO - Cell
DO - 10.1016/j.cell.2026.06.016
UR - https://doi.org/10.1016/j.cell.2026.06.016
AB - Human genome resequencing typically involves mapping reads to a reference genome to call variants; however, this approach suffers from both technical and reference biases, leaving many duplicated and structurally polymorphic regions of the genome unmapped. Consequently, existing variant benchmarks, generated by the same methods, fail to assess these complex regions. To address this limitation, we present a telomere-to-telomere genome benchmark that achieves near-perfect accuracy (i.e. no detectable errors) across 99.4% of the complete, diploid HG002 genome. This benchmark adds 701.4 Mb of autosomal sequence and both sex chromosomes (216.8 Mb), totaling 15.3% of the genome that was absent from prior benchmarks. We also provide a diploid annotation of genes, transposable elements, segmental duplications, and satellite repeats, including 39,144 protein-coding genes across both haplotypes. To facilitate application of the benchmark, we developed tools for measuring the accuracy of sequencing reads, phased variant call sets, and genome assemblies against a diploid reference. Genome-wide analyses show that state-of-the-art de novo assembly methods resolve 2-7% more sequence and outperform variant calling accuracy by an order of magnitude, yielding just one error per 100 kb across 99.9% of the benchmark regions. Adoption of genome-based benchmarking is expected to accelerate the development of cost-effective methods for complete genome sequencing, expanding the reach of genomic medicine to the entire genome and enabling a new era of personalized genomics.
SN - 0092-8674
SN - 1097-4172
ER -