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Chasing perfection: validation and polishing strategies for telomere-to-telomere genome assemblies

Nature Methods volume 19, pages 687–695 (2022)Cite this article

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Abstract

Advances in long-read sequencing technologies and genome assembly methods have enabled the recent completion of the first telomere-to-telomere human genome assembly, which resolves complex segmental duplications and large tandem repeats, including centromeric satellite arrays in a complete hydatidiform mole (CHM13). Although derived from highly accurate sequences, evaluation revealed evidence of small errors and structural misassemblies in the initial draft assembly. To correct these errors, we designed a new repeat-aware polishing strategy that made accurate assembly corrections in large repeats without overcorrection, ultimately fixing 51% of the existing errors and improving the assembly quality value from 70.2 to 73.9 measured from PacBio high-fidelity and Illumina k-mers. By comparing our results to standard automated polishing tools, we outline common polishing errors and offer practical suggestions for genome projects with limited resources. We also show how sequencing biases in both high-fidelity and Oxford Nanopore Technologies reads cause signature assembly errors that can be corrected with a diverse panel of sequencing technologies.

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Fig. 1: An overview of the evaluation and polishing strategy developed to achieve a complete human genome assembly.
Fig. 2: Sequencing biases in PacBio HiFi and Illumina reads.
Fig. 3: Errors corrected after polishing.
Fig. 4: Examples of the largest CHM13 regions with a copy number in the reference that differs from GRCh38 and most individuals.
Fig. 5: Errors made by automated polishing.

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Data availability

All data types and assemblies are available on https://github.com/marbl/CHM13/ and under NCBI BioProject PRJNA559484 with the Assembly GenBank accession GCA_009914755. Polishing edits, cataloged remaining issues and known heterozygous regions are available on https://github.com/marbl/CHM13-issues/. All the data in the two GitHub repositories are directly downloadable from https://s3-us-west-2.amazonaws.com/human-pangenomics/index.html?prefix=T2T/CHM13/ with no restrictions. The retrained PEPPER model used for telomere polishing is available to download at https://storage.cloud.google.com/pepper-deepvariant-public/pepper_models/PEPPER_HP_R941_ONT_V4_T2T.pkl. Source data for generating plots in this paper are available on https://github.com/arangrhie/T2T-Polish/tree/master/paper/2022_Mc_Cartney/.

Code availability

To facilitate usability of our evaluation and polishing strategy, we made the up-to-date version of tools that have been used within our workflows openly available on https://github.com/arangrhie/T2T-Polish/. Exact codes used for polishing CHM13v0.9 and CHM13v1.0 are available on https://github.com/marbl/CHM13-issues/. Both GitHub repositories are available through a public domain, and have been deposited to Zenodo60,61. Custom scripts used for merging small variants, and generating telomere edits are available at https://github.com/kishwarshafin/T2T_polishing_scripts/ and deposited to Zenodo62 under an MIT license.

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Acknowledgements

This work was supported by the Intramural Research Program of the National Human Genome Research Institute (NHGRI), National Institutes of Health (NIH 1ZIAHG200398; to A.M.M., C.J., S.K., A.M.P. and A.R.); National Science Foundation DBI-1350041 and IOS-1732253 (to M.A.); NIH/NHGRI R01HG010485, U41HG010972, U01HG010961, U24HG011853 and OT2OD026682 (to K.S. and B.P.); HHMI (to G.F.); Wellcome WT206194 (to K.H. and J.M.W.); NIGMS F32 GM134558 (to G.A.L.); NIH/NHGRI R01 1R01HG011274-01, NIH/NHGRI R21 1R21HG010548-01 and NIH/NHGRI U01 1U01HG010971 (to K.M.); St. Petersburg State University grant ID PURE73023672 (to A.M.); NIH/NHGRI R01HG006677 (to A.S.); Fulbright Fellowship (to D.C.S.); and Intramural funding at the National Institute of Standards and Technology (to J.Z.). This work utilized the computational resources of the NIH HPC Biowulf cluster (https://hpc.nih.gov/). Certain commercial equipment, instruments or materials are identified to specify adequately experimental conditions or reported results. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the equipment, instruments or materials identified are necessarily the best available for the purpose.

Author information

Author notes

  1. These authors contributed equally: Ann M. Mc Cartney, Kishwar Shafin, Micheal Alonge.

Authors and Affiliations

  1. Genome Informatics Section, Computational and Statistical Genomics Branch, NHGRI, NIH, Bethesda, MD, USA

    Ann M. Mc Cartney, Chirag Jain, Sergey Koren, Adam M. Phillippy & Arang Rhie

  2. UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA

    Kishwar Shafin, Karen H. Miga & Benedict Paten

  3. Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA

    Michael Alonge

  4. Graduate Program in Bioinformatics and Systems Biology, University of California, San Diego, La Jolla, CA, USA

    Andrey V. Bzikadze

  5. Laboratory of Neurogenetics of Language and The Vertebrate Genome Lab, The Rockefeller University, New York, NY, USA

    Giulio Formenti

  6. DNAnexus, Mountain View, CA, USA

    Arkarachai Fungtammasan

  7. Wellcome Sanger Institute, Cambridge, UK

    Kerstin Howe & Jonathan M. D. Wood

  8. Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India

    Chirag Jain

  9. Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA

    Glennis A. Logsdon

  10. Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA

    Karen H. Miga

  11. Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, Saint Petersburg State University, Saint Petersburg, Russia

    Alla Mikheenko

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

    Alaina Shumate

  13. Genome Center, MIND Institute, Department of Biochemistry and Molecular Medicine, University of California, Davis, CA, USA

    Daniela C. Soto

  14. Pacific Biosciences, Menlo Park, CA, USA

    Ivan Sović

  15. Digital BioLogic d.o.o., Ivanić-Grad, Croatia

    Ivan Sović

  16. Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD, USA

    Justin M. Zook

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  1. Ann M. Mc Cartney
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Contributions

A.R. and A.M.P. conceived and supervised the project. A.M.M., K.S., G.F., K.H., J.M.D.W. and A.R. performed the pre-polishing evaluation. K.S., M.A., A.V.B., A.F., C.J., A.M., B.P. and A.R. aligned reads and called variants. A.M.M., K.S., M.A., G.F., A.F., K.H.M., A.M., J.M.Z. and A.R. manually validated variant calls. D.C.S. and J.M.Z. performed the gene collapse and expansion analysis. K.S., M.A., A.V.B., G.A.L., K.H.M., A.M. and A.R. identified and curated heterozygous and ‘issues’ loci. K.S., M.A., S.K. and B.P. patched and polished the telomeres. A.M.M., M.A., A.S. and I.S. performed automated polishing. A.M.M., K.S., M.A. and A.R. wrote the manuscript, with assistance from all authors. All authors approved the final manuscript.

Corresponding authors

Correspondence to Adam M. Phillippy or Arang Rhie.

Ethics declarations

Competing interests

I.S. is an employee of PacBio. A.F. is an employee of DNAnexus. S.K. has received travel funds to speak at symposia organized by Oxford Nanopore. The remaining authors declare no competing interests.

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Nature Methods thanks Kai Wang and Jue Ruan for their contribution to the peer review of this work. Primary Handling Editor: Lin Tang, in collaboration with the Nature Methods team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Sequencing biases observed in missing k-mers.

a, missing k-mers with its GA composition. b-d, v0.9 assembly and k-mer copy number spectrum from HiFi, Illumina, and hybrid k-mer sets (left) and per-chromosome missing (likely error) k-mer counts from the HiFi derived consensus or patches (right). Most missing k-mers in HiFi overlapped sequences from patched regions. No missing k-mer was found on chromosomes indicated with red arrows.

Extended Data Fig. 2 Error detection and polishing pipeline.

A detailed overview of the polishing pipeline along with the number of errors identified and polished at each step. Additionally, data type and polishing tools utilized are highlighted. Illumina, 100X PCR-free library Illumina reads; HiFi, 35x PacBio HiFi reads; ONT, 120x Oxford Nanopore reads.

Extended Data Fig. 3 Number of SV-like errors and globally unique single copy k-mers used for marker assisted alignment.

a. Number of SV-like errors called from long-read platforms. b. Range of k-mer counts defined as ‘single-copy’ markers from Illumina reads and in the assembly. The cutoffs were chosen to minimize inclusion of low-frequency erroneous k-mers and 2-copy k-mers. c. Number of markers in every 10 kb window. d. Cumulative number of bases covered by the number of markers in each 10 kb window.

Extended Data Fig. 4 Post-polishing evaluation.

a. Left, genotype quality and number of reads supporting the reference and alternate alleles from the combined Illumina-hifi hybrid and ONT homozygous variant calls, with AF > 0.5. Right, balanced insertion (red) and deletion (blue) length distribution from the Illumina-HiFi hybrid DeepVariant heterozygous calls in CHM13v1.0. b. Number of errors detected in each chromosome, before and after polishing. c. Polishing inside and outside of repeats. The distribution of CHM13v0.9 polishing rates within and without repeats.

Extended Data Fig. 5 Three SV-like errors corrected.

HiFi and ONT marker assisted alignments, post correction of the 3 large SV-like edits visualized with IGV. HiFi coverage track is shown in data range up to 60, ONT up to 150. Clipped reads are flagged for >100 bp. INDELs smaller than 10 bp are not shown. Reads are colored by strands; positive in red and negative in blue.

Extended Data Fig. 6 Telomere polishing.

a. An illustration of Chr. 2 telomere sequence reads from HiFi, ONT and CLR platform. b. Distribution of maximum perfect match to the canonical k-mer observed at each position in the telomere before (CHM13v1.0) and after (CHM13v1.1) polishing the telomeres.

Extended Data Fig. 7 Mapping biases found and corrected.

On simulated HiFi reads, we found excessive clippings in highly identical satellite repeats in Minimap and Winnowmap by the time of evaluation. We have addressed this issue in Winnowmap 2.01 + . Clipped (%) indicates the percentage of reads clipped in every 1,024 bp window, shown in 0~40% range with a midline of 10%.

Extended Data Fig. 8 HiFi, CLR, ONT read coverage, alignment identity, and read length from Winnowmap2 v2.01 alignments and Bionano DLE-1 molecule coverage from Bionano Solve.

Upper panel shows a zoomed in region of Chromosome 9, while the upper panel shows the whole-genome alignment view. HiFi, CLR, ONT, and Bionano coverage are shown up to 70x, 70x, 200x, and 250x, respectively. Median read identity in every 1,024 bp is shown in 80-100% range. Median read length in every 1024 bp is shown in 0-100 kb range. Read identity was the worst in CLR, and between HiFi and ONT. Bionano molecules were lacking coverage in most of the centromeric repeats.

Extended Data Fig. 9 Collapsed simple tandem repeat.

The collapse in the Intronic sequences of gene FAM227A was undetected, due to the variable insertion breakpoints and insertion length in the HiFi and ONT alignments. The panels above the alignments show marker density and percent microsatellites (GA / AT / TC / GC) in each 64 bp window, which indicates this region is highly repetitive with GA enriched sequences, which later alternates with AT enriched sequences.

Extended Data Fig. 10 Chimeric junction of two haplotypes.

In the shown above regions, both HiFi and ONT reads indicate that the consensus has a chimeric junction of the two haplotypes.

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Mc Cartney, A.M., Shafin, K., Alonge, M. et al. Chasing perfection: validation and polishing strategies for telomere-to-telomere genome assemblies. Nat Methods 19, 687–695 (2022). https://doi.org/10.1038/s41592-022-01440-3

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