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Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome

Nature Biotechnology volume 37, pages 1155–1162 (2019)Cite this article

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Abstract

The DNA sequencing technologies in use today produce either highly accurate short reads or less-accurate long reads. We report the optimization of circular consensus sequencing (CCS) to improve the accuracy of single-molecule real-time (SMRT) sequencing (PacBio) and generate highly accurate (99.8%) long high-fidelity (HiFi) reads with an average length of 13.5 kilobases (kb). We applied our approach to sequence the well-characterized human HG002/NA24385 genome and obtained precision and recall rates of at least 99.91% for single-nucleotide variants (SNVs), 95.98% for insertions and deletions <50 bp (indels) and 95.99% for structural variants. Our CCS method matches or exceeds the ability of short-read sequencing to detect small variants and structural variants. We estimate that 2,434 discordances are correctable mistakes in the ‘genome in a bottle’ (GIAB) benchmark set. Nearly all (99.64%) variants can be phased into haplotypes, further improving variant detection. De novo genome assembly using CCS reads alone produced a contiguous and accurate genome with a contig N50 of >15 megabases (Mb) and concordance of 99.997%, substantially outperforming assembly with less-accurate long reads.

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Fig. 1: Sequencing HG002 with highly accurate long reads.
Fig. 2: Mappability of the human genome with CCS reads.
Fig. 3: Variant calling and phasing with CCS reads.
Fig. 4: Impact of read accuracy on de novo assembly.

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Data availability

Data are available in NCBI BioProject PRJNA529679. CCS reads are available on NCBI SRA with accession code SRX5327410. Small variant calls are available on NCBI dbSNP with accession codes ss3783301452–ss3798736595. Structural variant calls are available on NCBI dbVar with accession nstd167. The trio binned Canu assemblies are available on NCBI Assembly with accession codes GCA_004796485.1 (maternal) and GCA_004796285.1 (paternal). Alignments to GRCh37 are available at ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/PacBio_CCS_15kb/ or https://bit.ly/2RW1b3I. Additional data, including all assemblies and a track hub for the UCSC Genome Browser, are available at https://downloads.pacbcloud.com/public/publications/2019-HG002-CCS.

Code availability

Custom scripts are available at https://github.com/PacificBiosciences/hg002-ccs/. Google DeepVariant, a model trained on PacBio CCS reads, and instructions for use are available at https://github.com/google/deepvariant.

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Acknowledgements

We would like to thank J. Harting for assistance with HLA typing, K. Robertshaw for figure generation, J. Wilson and J. Ziegle for providing PacBio CLR datasets and J. Puglisi for critical reading of the manuscript. S.K. and A.M.P. were supported by the Intramural Research Program of the National Human Genome Research Institute, National Institutes of Health. This work utilized the computational resources of the NIH HPC Biowulf cluster (https://hpc.nih.gov). This work was supported by NIH grant no. 1R01HG010040 to H.L. and NSFC grant nos. 31571353 and 31822029 to J.R. M.C.S. is funded by the National Science Foundation (grant no. DBI-1350041) and National Institutes of Health (grant no. R01-HG006677). F.J.S. and M.M. are funded by NIH grant no. UM1 HG008898. T.M. acknowledges funding from the German Research Foundation (DFG) (grant nos. 391137747 and 395192176). N.D.O. and J.M.Z. were supported by intramural funding from the National Institute of Standards and Technology and an interagency agreement with the U.S. Food and Drug Administration. This work utilized computational resources of DNAnexus and Google to apply DeepVariant to CCS reads. Certain commercial equipment, instruments or materials are identified to specify adequate experimental conditions or reported results. Such identification does not imply recommendation or endorsement by the National Institute of Standards, nor does it imply that the equipment, instruments or materials identified are necessarily the best available for the purpose.

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Author notes

  1. These authors contributed equally: Aaron M. Wenger, Paul Peluso.

Authors and Affiliations

  1. Pacific Biosciences, Menlo Park, CA, USA

    Aaron M. Wenger, Paul Peluso, William J. Rowell, Richard J. Hall, Gregory T. Concepcion, Armin Töpfer, Yufeng Qian, David R. Rank & Michael W. Hunkapiller

  2. Google Inc., Mountain View, CA, USA

    Pi-Chuan Chang, Alexey Kolesnikov, Mark A. DePristo & Andrew Carroll

  3. Center for Bioinformatics, Saarland University, Saarbrücken, Germany

    Jana Ebler & Tobias Marschall

  4. Max Planck Institute for Informatics, Saarbrücken, Germany

    Jana Ebler & Tobias Marschall

  5. Graduate School of Computer Science, Saarland University, Saarbrücken, Germany

    Jana Ebler

  6. DNAnexus, Mountain View, CA, USA

    Arkarachai Fungtammasan & Chen-Shan Chin

  7. National Institute of Standards and Technology, Gaithersburg, MD, USA

    Nathan D. Olson & Justin M. Zook

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

    Michael Alonge & Michael C. Schatz

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

    Medhat Mahmoud & Fritz J. Sedlazeck

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

    Adam M. Phillippy & Sergey Koren

  11. Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany

    Gene Myers

  12. Agricultural Genomics Institute, Chinese Academy of Agricultural Sciences, Shenzhen, China

    Jue Ruan

  13. Dana-Farber Cancer Institute, Boston, MA, USA

    Heng Li

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  1. Aaron M. Wenger
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Contributions

A.M.W., D.R.R., M.W.H. and P.P. designed the study. D.R.R. and P.P. developed the sample preparation protocol and performed sample preparation. D.R.R., P.P. and Y.Q. performed sequencing. A.C., A.K., C-S.C., M.A.D. and P.C. adapted the algorithms and implementation of DeepVariant. A.C., A.F., A.K., A.M.P., A.M.W., A.T., C-S.C., D.R.R., F.J.S., G.M., G.T.C., H.L., J.E., J.M.Z., J.R., M.A., M.A.D., M.C.S., M.M., N.D.O., P.C., P.P., R.J.H., S.K., T.M. and W.J.R. performed analysis. A.C., A.M.P., C-S.C., D.R.R., F.J.S., J.M.Z., M.A.D., M.C.S. and M.W.H. supervised analysis. A.C., A.M.W., D.R.R., G.M., J.M.Z., P.P., R.J.H., S.K. and W.J.R. wrote the manuscript. See Supplementary Note for more detailed author contributions. All authors reviewed and approved the final manuscript.

Corresponding authors

Correspondence to David R. Rank or Michael W. Hunkapiller.

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Competing interests

A.M.W., A.T., D.R.R., G.T.C., M.W.H., P.P., R.J.H., W.J.R. and Y.Q. are employees and shareholders of Pacific Biosciences. A.C., A.K., M.A.D. and P.C. are employees and shareholders of Google. A.F. and C-S.C. are employees and shareholders of DNAnexus. A.C. is a shareholder and was an employee of DNAnexus for a portion of this work.

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Integrated supplementary information

Supplementary Figure 1 CCS protocol development.

(a) Distribution of polymerase read lengths for a 10 kb E.coli amplicon library and 30 kb E. coli whole genome library sequenced for 10 hours with identical conditions. (b) Distribution of polymerase read lengths for an 8 kb fragment from a BsaAI-digested lambda library sequenced for 4 hours with (5 hour) and without (0 hour) pre-extension to reduce “early-terminating” reads and select surviving polymerase-template complexes. (c) Sample preparation and sequencing workflow. (d) BioAnalyzer trace for the SMRTbell library, sheared to target 15–20 kb fragments. “FU” is fluorescence units. (e) BioAnalyzer trace for ELF fractions of the SMRTbell library. (f) The fraction centered around 15 kb was used for sequencing.

Supplementary Figure 2 CCS read accuracy and coverage uniformity.

(a) Distribution of accuracy predicted by the CCS algorithm for reads with fewer than 3 passes and at least 3 passes, which we consider a minimum pass count for CCS. Approximately half of reads have 3 or more passes; among those nearly all achieve Q20 predicted accuracy. (b) Distributions of HG002 concordance, measured against the GIAB benchmark, at levels of predicted read accuracy (R2 of median = 0.9980). Orange lines are medians; boxes extend from lower to upper quartiles; whiskers extend 1.5 interquartile distances; n=1,000 reads at each predicted accuracy. (c) Distribution of difference between concordance and predicted read accuracy shows that the prediction is well-calibrated to the empirical concordance. (d) Distribution of coverage in 500 bp windows at non-gap positions in GRCh37. (e) Coverage distributions at levels of [GC] content, measured in 500 bp windows. Orange lines are medians; boxes extend from lower to upper quartiles; whiskers extend 1.5 interquartile distances; n per distribution is listed above the plot.

Supplementary Figure 3 CCS read pileups at HLA genes.

The 13.5 kb CCS reads provide phasing and full four-field resolution of HLA class I and II genes (Methods Mol. Biol. 1802, 135–153, 2018), including (a) HLA-A for which HG002 has alleles that differ in the first field, and (b) HLA-DPA1 for which HG002 has alleles that differ only in the fourth field from two intronic single nucleotide polymorphisms across 20 kb.

Supplementary Figure 4 Theoretical phase block N50 in HG002 at different read lengths.

To model the phase blocks achievable with a given read length, cuts were introduced between heterozygous variants in the GIAB trio-phased HG002 variant callset that are separated by more than the read length, which effectively assumes that adjacent heterozygous variants separated by less than the read length can be phased.

Supplementary Figure 5 Structural variant calling performance.

Precision, recall, and number of variant calls in the GIAB benchmark regions for the PacBio CCS mapping-based variant callers (a) pbsv and (b) Sniffles; the PacBio CCS assembly-based callers (c) paftools/Canu (polished) and (d) paftools/FALCON (unpolished); the PacBio CLR mapping-based callers (e) pbsv and (f) Sniffles; the Illumina short-read callers (g) Manta and (h) Delly; and the 10X Genomics callers (i) LongRanger and (j) paftools/Supernova. Negative length indicates a deletion; positive length indicates an insertion. The histogram bin size is 50 bp for variants shorter than 1 kb, and 500 bp for variants >1 kb. Precision and recall are measured with Truvari against the GIAB benchmark.

Supplementary Figure 6 Haplotype resolution in the Canu mixed assembly.

The Canu mixed assembly is larger than the haploid human genome size because it resolves some heterozygous loci into separate maternal and paternal haplotypes. (a, b) Loci where the long primary contig matches the paternal haplotype and a smaller contig matches the maternal haplotype. (c, d) Similar loci where the long primary contig matches the maternal haplotype and a smaller contig matches the paternal haplotype.

Supplementary Figure 7 Mis-phasing analysis of parental assemblies.

Parent-specific heterozygous SNVs were identified in the GIAB benchmark callset. The “Mis-phased SNVs fraction” is the fraction of parent-specific SNVs from the wrong parent (e.g. [SNVpat]/[SNVpat+SNVmat] in a maternal contig). No large contigs have a high mis-phased SNVs ratio, which suggests proper phasing of the (a) Canu paternal, (b) Canu maternal, (c) FALCON paternal, and (d) FALCON maternal assemblies.

Supplementary Figure 8 Coverage titration for variant calling, phasing, and assembly.

Precision and recall for (a) SNVs and (b) indels called with DeepVariant (CCS), subsampling in steps of 3%. (c) Precision and recall for structural variants called with pbsv, subsampling in steps of 10%. (d) Phase block N50 for phasing of the 28-fold DeepVariant (CCS) callset with WhatsHap, subsampling in steps of 10%. Phasing performance is similar with a callset produced at matched coverage (not shown). De novo assembly (e) completeness measured as total assembly size, (f) contiguity measured as contig N50, and (g) correctness measured as concordance to the HG002 GIAB benchmark for wtdbg2 assembly, subsampling reads in steps of 10%.

Supplementary Figure 9 Likely errors in the GIAB benchmark identified by CCS callsets.

Manual curation of discrepancies between the GIAB benchmark and CCS variant callsets identifies benchmark errors for all variant types that are correctable using the CCS variant callsets. Shown are four loci that the GIAB benchmark records as homozygous reference where CCS reads identify likely heterozygous variation: (a) Three SNVs supported by CCS reads and 6 kb matepair reads. (b) A 2 bp insertion supported by CCS reads, 10X Genomics reads, and 6 kb matepair reads. (c) A 328 bp insertion supported by CCS reads and assemblies. (d) An 83 bp deletion supported by CCS reads.

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Wenger, A.M., Peluso, P., Rowell, W.J. et al. Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome. Nat Biotechnol 37, 1155–1162 (2019). https://doi.org/10.1038/s41587-019-0217-9

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