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Haplotype-aware variant calling with PEPPER-Margin-DeepVariant enables high accuracy in nanopore long-reads

Nature Methods volume 18, pages 1322–1332 (2021)Cite this article

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Abstract

Long-read sequencing has the potential to transform variant detection by reaching currently difficult-to-map regions and routinely linking together adjacent variations to enable read-based phasing. Third-generation nanopore sequence data have demonstrated a long read length, but current interpretation methods for their novel pore-based signal have unique error profiles, making accurate analysis challenging. Here, we introduce a haplotype-aware variant calling pipeline, PEPPER-Margin-DeepVariant, that produces state-of-the-art variant calling results with nanopore data. We show that our nanopore-based method outperforms the short-read-based single-nucleotide-variant identification method at the whole-genome scale and produces high-quality single-nucleotide variants in segmental duplications and low-mappability regions where short-read-based genotyping fails. We show that our pipeline can provide highly contiguous phase blocks across the genome with nanopore reads, contiguously spanning between 85% and 92% of annotated genes across six samples. We also extend PEPPER-Margin-DeepVariant to PacBio HiFi data, providing an efficient solution with superior performance over the current WhatsHap-DeepVariant standard. Finally, we demonstrate de novo assembly polishing methods that use nanopore and PacBio HiFi reads to produce diploid assemblies with high accuracy (Q35+ nanopore-polished and Q40+ PacBio HiFi-polished).

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Fig. 1: Nanopore variant-calling results.
Fig. 2: Comparison between Nanopore, Illumina and PacBio HiFi variant calling performance.
Fig. 3: Margin and WhatsHap phasing results.
Fig. 4: Gene analysis.
Fig. 5: Diploid assembly-polishing results.

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Data availability

We have made the analysis data available publicly (variant calling outputs, genome assemblies) in: https://console.cloud.google.com/storage/browser/pepper-deepvariant-public/analysis_data. The source data for the main figures can be found in: https://console.cloud.google.com/storage/browser/pepper-deepvariant-public/figure_source_data/Figure_source_data/.

For sequencing data, we used several publicly available datasets:

• GIAB consortium26,28: https://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/

• Human Pangenome Reference Consortium (HPRC): https://s3-us-west-2.amazonaws.com/human-pangenomics/index.html

• Telomere-to-telomere consortium11,12: https://github.com/nanopore-wgs-consortium/CHM13

Please see the Supplementary Notes to find specific links to the sequencing data that we used for our analysis. Source data are provided with this paper.

Code availability

The modules of PEPPER-Margin-DeepVariant are publicly available in these repositories:

• PEPPER: https://github.com/kishwarshafin/pepper

• Margin: https://github.com/UCSC-nanopore-cgl/margin

• DeepVariant: https://github.com/google/deepvariant

The PEPPER-Margin-DeepVariant software57 is available at https://doi.org/10.5281/zenodo.5275510, and we used r0.4 version for the evaluation presented in this manuscript. For simpler use, we have also created a publicly available docker container, kishwars/pepper_deepvariant:r0.4, that can run our variant-calling and polishing pipelines.

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Acknowledgements

Research reported in this publication was supported by the National Human Genome Research Institute of the National Institutes of Health under award numbers U41HG010972, R01HG010485, U01HG010961, and OT2OD026682 (K.S., T.P., M.K., J.M.E, K.H.M., M.J., B.P.). We thank Circulomics Inc. for sharing HG001 Nanopore data. We thank J. Zook and J. Wagner from the National Institute of Standards and Technology (NIST) for providing a draft version of the HG005 v4.2.1 benchmark. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Author notes

  1. These authors contributed equally: Kishwar Shafin, Trevor Pesout, Pi-Chuan Chang.

Authors and Affiliations

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

    Kishwar Shafin, Trevor Pesout, Mikhail Kolmogorov, Jordan M. Eizenga, Karen H. Miga, Miten Jain & Benedict Paten

  2. Google Inc, Mountain View, CA, USA

    Pi-Chuan Chang, Maria Nattestad, Alexey Kolesnikov, Sidharth Goel, Gunjan Baid & Andrew Carroll

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

    Paolo Carnevali

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Contributions

B.P. and A.C. designed and executed the study. K.S. developed PEPPER. T.P. developed Margin. P.-C.C. designed candidate import functionality in DeepVariant. K.S., T.P., and P.-P.C. contributed equally to the methods development and core analysis presented. M.N. designed alt-event alignment in DeepVariant, A.K. contributed to haplotype sorting and improvements on DeepVariant runtime, S. G. contributed to candidate import module of DeepVariant, G.B. designed and executed the post-processing model to improve multiallelic variant accuracy. M.K. designed and evaluated assembly polishing. J.M.E. designed local phasing metric and contributed to phasing evaluation. K.H.M. provided experimental design guidance, and P.C. generated assemblies and provided guidance on assembly polishing. M.J. performed nanopore sequencing, quality control and helped to design and execute analysis. All authors approve of the final manuscript.

Corresponding authors

Correspondence to Andrew Carroll or Benedict Paten.

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Competing interests

K.S. has performed paid internships at NVIDIA Corp and Google. P.C., M.N., A.K., S.G., G.B., and A.C. are employees of Google and own Alphabet stock as part of the standard compensation package. M.J. has received reimbursement for travel, accommodation, and conference fees to speak at events organized by ONT. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Methods thanks Ruibang Luo and the other, anonymous, reviewers for their contribution to the peer review of this work. Peer reviewer reports are available. Lin Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Shafin, K., Pesout, T., Chang, PC. et al. Haplotype-aware variant calling with PEPPER-Margin-DeepVariant enables high accuracy in nanopore long-reads. Nat Methods 18, 1322–1332 (2021). https://doi.org/10.1038/s41592-021-01299-w

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