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Constructing telomere-to-telomere diploid genome by polishing haploid nanopore-based assembly

Nature Methods volume 21, pages 574–583 (2024)Cite this article

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Abstract

Draft genomes generated from Oxford Nanopore Technologies (ONT) long reads are known to have a higher error rate. Although existing genome polishers can enhance their quality, the error rate (including mismatches, indels and switching errors between paternal and maternal haplotypes) can be significant. Here, we develop two polishers, hypo-short and hypo-hybrid to address this issue. Hypo-short utilizes Illumina short reads to polish an ONT-based draft assembly, resulting in a high-quality assembly with low error rates and switching errors. Expanding on this, hypo-hybrid incorporates ONT long reads to further refine the assembly into a diploid representation. Leveraging on hypo-hybrid, we have created a diploid genome assembly pipeline called hypo-assembler. Hypo-assembler automates the generation of highly accurate, contiguous and nearly complete diploid assemblies using ONT long reads, Illumina short reads and optionally Hi-C reads. Notably, our solution even allows for the production of telomere-to-telomere diploid genomes with additional manual steps. As a proof of concept, we successfully assembled a fully phased telomere-to-telomere diploid genome of HG00733, achieving a quality value exceeding 50.

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Fig. 1
Fig. 2: The pipeline of hypo-assembler.
Fig. 3: Various statistics of our HG00733 assembly.

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Data availability

All the assemblies and annotations are available in https://zenodo.org/doi/10.5281/zenodo.10494612. CHM13 are mostly taken from the CHM13 GitHub page https://github.com/marbl/CHM13: ONT reads, https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/nanopore/rel3/rel3.fastq.gz; Illumina reads, https://www.ncbi.nlm.nih.gov/sra/SRX1009644 [accn]; Reference: https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/chm13.draft_v1.1.fasta.gz; Annotation, https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/annotation/chm13.draft_v2.0.gene_annotation.gff3. HG002 are mostly taken from HG002 data freeze https://github.com/human-pangenomics/HG002_Data_Freeze_v1.0 with the only difference on ONT reads, where we take a newer set of Guppy pangenomics: ONT reads, https://s3-us-west-2.amazonaws.com/human-pangenomics/NHGRI_UCSC_panel/HG002/nanopore/Guppy_4.2.2/HG002_GIAB_MinION_GridION_Guppy_4.2.2.fastq.gz and https://s3-us-west-2.amazonaws.com/human-pangenomics/NHGRI_UCSC_panel/HG002/nanopore/Guppy_4.2.2/HG002_GIAB_PromethION_Guppy_4.2.2_prom.fastq.gz; Illumina reads, https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_indexes/AshkenazimTrio/sequence.index.AJtrio_Illumina300X_wgs_07292015_updated.HG002; HiFi reads, https://s3-us-west-2.amazonaws.com/human-pangenomics/NHGRI_UCSC_panel/HG002/hpp_HG002_NA24385_son_v1/PacBio_HiFi/15kb/m64012_190920_173625.Q20.fastq and https://s3-us-west-2.amazonaws.com/human-pangenomics/NHGRI_UCSC_panel/HG002/hpp_HG002_NA24385_son_v1/PacBio_HiFi/15kb/m64012_190921_234837.Q20.fastq; Hi-C reads, https://s3-us-west-2.amazonaws.com/human-pangenomics/index.html?prefix=NHGRI_UCSC_panel/HG002/hpp_HG002_NA24385_son_v1/hic/downsampled/; Reference, https://www.ncbi.nlm.nih.gov/assembly/GCA_021951015.1/ and https://www.ncbi.nlm.nih.gov/assembly/GCA_021950905.1. HG00733 are taken from human pangenomics: ONT reads, https://s3-us-west-2.amazonaws.com/human-pangenomics/index.html?prefix=NHGRI_UCSC_panel/HG00733/nanopore/Guppy_4.2.2/; Illumina reads, https://www.ncbi.nlm.nih.gov/sra/ERX4439205 [accn] and https://www.ncbi.nlm.nih.gov/sra/ERX4439180 [accn]; HiFi reads, https://www.ncbi.nlm.nih.gov/sra/ERX3831682; Hi-C reads, https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR11347815. HG003 data for HG002 (trio) are taken from https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_indexes/AshkenazimTrio/sequence.index.AJtrio_Illumina300X_wgs_07292015_updated.HG003. HG004 data for HG002 (trio) are taken from https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_indexes/AshkenazimTrio/sequence.index.AJtrio_Illumina300X_wgs_07292015_updated.HG004. HG00733 trio data (HG00731 and HG00732) are taken from 1000 genomes database https://www.internationalgenome.org/data-portal/sample/HG00732 and https://www.internationalgenome.org/data-portal/sample/HG00731.

Code availability

Hypo-assembler, related pipelines, and evaluation scripts are available on GitHub: https://github.com/kensung-lab/hypo-assembler.

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Acknowledgements

The authors received no specific funding for this work.

Author information

Authors and Affiliations

  1. School of Computing, National University of Singapore, Singapore, Singapore

    Joshua Casey Darian, Ritu Kundu & Wing-Kin Sung

  2. Genome Institute of Singapore, Singapore, Singapore

    Ramesh Rajaby & Wing-Kin Sung

  3. Department of Chemical Pathology, The Chinese University of Hong Kong, Hong Kong, China

    Wing-Kin Sung

  4. JC STEM Laboratory of Computational Genomics, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China

    Wing-Kin Sung

  5. Hong Kong Genome Institute, Hong Kong, China

    Wing-Kin Sung

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Contributions

J.C. did the computations and experiments. R.R. developed variant callers that are crucial to the result. R.K. developed the early versions of the hypo polisher that become the starting point of the work. W.-K.S. supervises the work and encouraged the idea of solid k-mers. All authors discussed the results and contributed to the final manuscript.

Corresponding author

Correspondence to Wing-Kin Sung.

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The authors declare no competing interests.

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Peer review information

Nature Methods thanks Kai Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Lei Tang and Lin Tang, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 CHM13 assembly benchmark.

Several measures of qualities of assembly of CHM13. (a) The number of errors per 100kbp and (b) YAK’s estimated quality value of various assemblies of CHM13.

Extended Data Fig. 2 HG002 assembly benchmark.

Several measures of qualities for diploid assemblies of HG002. (a) The number of errors per 100kbp. (b) YAK’s estimated quality value. (c) Kmer Mixture Rate (d) Switch Error Rate.

Extended Data Table 1 CHM13 assembly statistics
Full size table
Extended Data Table 2 CHM13 centromere evaluations
Full size table
Extended Data Table 3 CHM13 segmental duplication evaluations
Full size table
Extended Data Table 4 CHM13 BAC evaluation
Full size table
Extended Data Table 5 HG002 segmental duplication evaluations
Full size table
Extended Data Table 6 HG00733 centromere evaluations
Full size table

Supplementary information

Supplementary Information

Supplementary Sections A–O, Figs. 1–29 and Tables 1–30.

Reporting Summary

Supplementary Dataset

Data used to generate the box plots in Supplementary Figs. 16 and 17.

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Darian, J.C., Kundu, R., Rajaby, R. et al. Constructing telomere-to-telomere diploid genome by polishing haploid nanopore-based assembly. Nat Methods 21, 574–583 (2024). https://doi.org/10.1038/s41592-023-02141-1

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