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Scalable Nanopore sequencing of human genomes provides a comprehensive view of haplotype-resolved variation and methylation

Nature Methods volume 20, pages 1483–1492 (2023)Cite this article

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Abstract

Long-read sequencing technologies substantially overcome the limitations of short-reads but have not been considered as a feasible replacement for population-scale projects, being a combination of too expensive, not scalable enough or too error-prone. Here we develop an efficient and scalable wet lab and computational protocol, Napu, for Oxford Nanopore Technologies long-read sequencing that seeks to address those limitations. We applied our protocol to cell lines and brain tissue samples as part of a pilot project for the National Institutes of Health Center for Alzheimer’s and Related Dementias. Using a single PromethION flow cell, we can detect single nucleotide polymorphisms with F1-score comparable to Illumina short-read sequencing. Small indel calling remains difficult within homopolymers and tandem repeats, but achieves good concordance to Illumina indel calls elsewhere. Further, we can discover structural variants with F1-score on par with state-of-the-art de novo assembly methods. Our protocol phases small and structural variants at megabase scales and produces highly accurate, haplotype-specific methylation calls.

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Fig. 1: Single flow cell ONT sequencing protocol.
Fig. 2: Small variant calling performance evaluation.
Fig. 3: SV evaluations.
Fig. 4: Combined, phased small variants and SVs improve the profiling of complex genomic regions.
Fig. 5: SV landscape summary.
Fig. 6: Haplotype-specific methylation profiling.

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Data availability

The cell line data (HG002, HG0073 and HG02723) are openly available through the AnVIL workspace: https://anvil.terra.bio/#workspaces/anvil-datastorage/ANVIL_NIA_CARD_Coriell_Cell_Lines_Open. Human brain sequencing datasets are under controlled access and require a dbGap application (phs001300.v4). Afterwards, the data will be available through the restricted AnVIL workspace: https://anvil.terra.bio/#workspaces/anvil-datastorage/ANVIL_NIA_CARD_LR_WGS_NABEC_GRU. Matching Illumina data used for cell line evaluations are available at: https://www.internationalgenome.org/data-portal/data-collection/30x-grch38. HPRC assemblies are available at: https://github.com/human-pangenomics/HPP_Year1_Data_Freeze_v1.0. GIAB benchmarks are available at: https://www.nist.gov/programs-projects/genome-bottle.

Code availability

The Napu implementation in WDL is available at: https://github.com/nanoporegenomics/napu_wf. Hapdup is available as a standalone tool at: https://github.com/KolmogorovLab/hapdup. Hapdiff is available at: https://github.com/KolmogorovLab/hapdiff.

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Acknowledgements

This work was supported in part by the Intramural Research Program of the National Cancer Institute (M.K.), the National Human Genome Research Institute (A.M.P.), the National Institute on Aging (B.J.T.) and the Center for Alzheimer’s and Related Dementias (C.B.), within the Intramural Research Program of the NIA and the National Institute of Neurological Disorders and Stroke (grant nos. ZIANS003154, ZIAAG000538), National Institutes of Health (grant no. AG000538). The Brain and Body Donation Program has been supported by the National Institute of Neurological Disorders and Stroke (grant no. U24 NS072026 National Brain and Tissue Resource for Parkinson’s Disease and Related Disorders), the National Institute on Aging (grant nos. P30AG19610 and P30AG072980, Arizona Alzheimer’s Disease Center), the Arizona Department of Health Services (contract 211002, Arizona Alzheimer’s Research Center), the Arizona Biomedical Research Commission (contracts 4001, 0011, 05–901 and 1001 to the Arizona Parkinson’s Disease Consortium) and the Michael J. Fox Foundation for Parkinson’s Research. B.P. was partly supported by NIH grant nos. R01HG010485, U24HG010262, U24HG011853, OT3HL142481, U01HG010961 and OT2OD033761. M. Mastoras was supported by NIH grant no. T32HG012344. K.D. was supported by the JSPS Research Fellowship for Japanese Biomedical and Behavioral Researchers at NIH. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We acknowledge the support of Oxford Nanopore Technologies staff in generating this dataset, in particular A. Markham. We acknowledge the support of the Circulomics Inc. team in generating this protocol, in particular K. Liu, J. Burke, M. Kim and D. Kilburn. We also acknowledge the Terra support team for their help with the data storage and cloud computing solutions. This work utilized the computational resources of the NIH HPC Biowulf cluster (https://hpc.nih.gov). We thank members of the North American Brain Expression Consortium (NABEC) for providing samples derived from brain tissue. We are grateful to the Banner Sun Health Research Institute Brain and Body Donation Program of Sun City, Arizona for the provision of human biological materials.

Author information

Author notes

  1. These authors contributed equally: Mikhail Kolmogorov, Kimberley J. Billingsley.

Authors and Affiliations

  1. Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Mikhail Kolmogorov & Jeshuwin Prabakaran

  2. Center for Alzheimer’s and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA

    Kimberley J. Billingsley, Pilar Alvarez Jerez, Laksh Malik, Xylena Reed, Rylee M. Genner, Kensuke Daida & Cornelis Blauwendraat

  3. Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA

    Kimberley J. Billingsley, Ramita Dewan, Kensuke Daida, Bryan J. Traynor & Cornelis Blauwendraat

  4. UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA

    Mira Mastoras, Melissa Meredith, Jean Monlong, Ryan Lorig-Roach, Mobin Asri, Trevor Pesout, Karen H. Miga & Benedict Paten

  5. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

    Sairam Behera & Fritz J. Sedlazeck

  6. Google LLC, Mountain View, CA, USA

    Kishwar Shafin

  7. Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, USA

    Jeshuwin Prabakaran

  8. Chan Zuckerberg Initiative, Redwood City, CA, USA

    Paolo Carnevali

  9. Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA

    Jianzhi Yang & Mark Chaisson

  10. Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA

    Arang Rhie & Adam M. Phillippy

  11. Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA

    Sonja W. Scholz

  12. Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA

    Sonja W. Scholz & Bryan J. Traynor

  13. Department of Bioengineering, Northeastern University, Boston, MA, USA

    Miten Jain

  14. Department of Physics, Northeastern University, Boston, MA, USA

    Miten Jain

  15. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Winston Timp

  16. Department of Computer Science, Rice University, Houston, TX, USA

    Fritz J. Sedlazeck

Authors
  1. Mikhail Kolmogorov
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  2. Kimberley J. Billingsley
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Contributions

M.K., K.J.B., C.B. and B.P. conceptualized and designed the study. K.J.B., P.A.J., L.M., R.D., X.R., R.M.G., K.D. and M.J. were responsible for protocol optimization and sequencing. M.K., M. Mastoras, M. Meredith, J.M., M.A., K.S., T.P., J.P. and P.C. were responsible for algorithmic development. M.K., K.J.B., M. Mastoras, M. Meredith, J.M., R.L.-R., M.A., P.A.J., R.M.G., K.D., S.B., K.S., T.P., P.C., J.Y., A.R., M.J., W.T., M.C., F.J.S., C.B. and B.P. performed data analysis. M.K., K.J.B., S.W.S., B.J.T., K.H.M., M.J., W.T., A.M.P., M.C., F.J.S., C.B. and B.P. interpreted data and oversaw the study. M.K. and B.P. drafted the manuscript. All authors provided feedback and helped revise the manuscript.

Corresponding authors

Correspondence to Mikhail Kolmogorov, Kimberley J. Billingsley, Cornelis Blauwendraat or Benedict Paten.

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Competing interests

K.S. is an employee of Google LLC and owns Alphabet stock as part of the standard compensation package; authors from Google LLC did not have access to the cell line and brain tissue sample data. W.T. has two patents (8,748,091 and 8,394,584) licensed to Oxford Nanopore Technologies. F.J.S. received research support from Illumina, Pacific Biosciences and Oxford Nanopore Technologies. S.W.S. serves on the Scientific Advisory Council of the Lewy Body Dementia Association and the Multiple System Atrophy Coalition. S.W.S. and B.J.T. receive research support from Cerevel Therapeutics. B.J.T. holds patents on the clinical testing and therapeutic implications of the C9orf72 repeat expansion. The remaining authors declare no competing interests.

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Nature Methods thanks Justin Zook and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editors: Hui Hua and Lei Tang, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 Variant calling and methylation analysis using Napu.

Raw ONT sequencing reads are basecalled by Guppy 6.1.2, which simultaneously produces methylation tags. A diploid, de-novo phased assembly is produced using a combination of Shasta and Hapdup. These assemblies are used to call SVs with Hapdiff. Small variants are called against a reference genome with Pepper-Margin-DeepVariant. The phased alignment file generated by Margin is used to produce haplotype-resolved methylation calls. Small variants and SVs are jointly phased by Margin, producing a single harmonized vcf.

Extended Data Fig. 2 Assemblies of 14 brain tissues and 3 cell lines generated by Shasta+Hapdup.

(A) NG50 and NGA50 contiguity measured using QUAST. Sample 06_66 had the lowest contiguity due to the decreased sequencing yield. (B) Assembly length. (C) Mean assemblies QV computed using yak. (D) Contiguity of phased blocks, broken at phase switches. An increased value for HG02723 suggests an increased heterozygosity rate. Cell lines marked with asterisks.

Extended Data Fig. 3 Assembly metrics comparison against HG002 assemblies produced in Jarvis et. al (2022).

Our assemblies are highlighted in green. Flye (ONT+trio) were produced using standard ONT reads at 60x coverage and Illumina parental information; Flye (ONT UL + trio) is similar, but using ultra-long ONT extraction. HiCanu and hifiasm used 34x HiFi reads and Illumina parental sequencing. DipAsm used 34x HiFi reads and 60x Hi-C reads. Original evaluations from Jarvis et al. are shown. See Supplementary Table 5 for more detail.

Extended Data Fig. 4 TT-Mars evaluation of Hapdup and Sniffles2 calls.

SV calls from Hapdup and Sniffles2 were compared to the assemblies from the HPRC for HG002 (top), HG00733 (middle), and HG02723 (bottom) with TT-Mars. The calls were either validated by the alignment (green), not validated (orange), or couldn’t be annotated by TT-Mars (blue). We evaluated all SVs across the genome (left), as well as the subset of SVs that don’t overlap centromeres or segmental duplications larger than 10 Kbp (right).

Extended Data Fig. 5 Flagger results based on HiFi alignments to cell line CARD and HPRC-Y1 assemblies.

The y-axis of each panel indicates the unreliability percentages which are the total number of bases flagged as misassembly divided by the total assembly length and multiplied by one hundred.

Extended Data Fig. 6 Flagger results based on ONT alignments to cell line CARD and HPRC-Y1 assemblies.

The y-axis of each panel indicates the unreliability percentages which are the total number of bases flagged as misassembly divided by the total assembly length and multiplied by one hundred.

Extended Data Fig. 7 Lenient SV catalog.

Similar to Fig. 5a but including SVs close to centromeres, telomeres, or within segmental duplications were removed. Number of SVs across samples. In the left panel, SVs were annotated with three SV catalogs (the gnomAD-SV database, a long-read-based SV catalog, and the HPRC v1.0 SV catalog). SVs are matched if they have at least 10% genomic overlap. The colors highlight the maximum frequency across these catalogs, the lighter blue showing ‘rare’ SVs (with an allele frequency below 1%) in the catalogs, or unmatched. SVs may be unmatched, either because they are novel or due to the difficulties in the database comparison. The right panel shows the number of rare SVs in protein-coding genes, grouped by their impact on the gene structure.

Extended Data Fig. 8 IGV view of a 4.2 Kbp heterozygous deletion of a transcription start site and exon of RBFOX1.

The coverage histogram (dark grey) shows the drop in read coverage. The alignment of about half of the reads, labelled by strand (red/blue), support the deletion. The GENCODE track, ENCODE candidate cis-regulatory elements, and conservation tracks are shown at the bottom.

Extended Data Fig. 9 Comparison of R9 and R10 sequencing runs using three cell lines.

Benchmarks were performed similarly to those described in Figs. 2–4. ‘Indel no HP/TD’ corresponds to indels outside of homopolymers and tandem repeats. Assembly SV F1 scores were computed outside of centromeres and segmental duplications. Additional statistics are given in Supplementary Table 13.

Extended Data Fig. 10 F1-score for SV inside clusters of different sizes.

The HiFi calls for HG002 genome were used as reference, and calls within 2 kbp were clustered using single linkage clustering. The number of true positive calls in each category is shown as text. When VNTR grouping is enabled, all insertions and deletions within the same haplotype in a single VNTR are combined into a single call. A substantial portion of the reduced Sniffles2 concordance is explained by the differences in representation of SV clusters by the assembly-based and mapping-based approaches.

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Kolmogorov, M., Billingsley, K.J., Mastoras, M. et al. Scalable Nanopore sequencing of human genomes provides a comprehensive view of haplotype-resolved variation and methylation. Nat Methods 20, 1483–1492 (2023). https://doi.org/10.1038/s41592-023-01993-x

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