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Improved sequence mapping using a complete reference genome and lift-over

Nature Methods volume 21, pages 41–49 (2024)Cite this article

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Abstract

Complete, telomere-to-telomere (T2T) genome assemblies promise improved analyses and the discovery of new variants, but many essential genomic resources remain associated with older reference genomes. Thus, there is a need to translate genomic features and read alignments between references. Here we describe a method called levioSAM2 that performs fast and accurate lift-over between assemblies using a whole-genome map. In addition to enabling the use of several references, we demonstrate that aligning reads to a high-quality reference (for example, T2T-CHM13) and lifting to an older reference (for example, Genome reference Consortium (GRC)h38) improves the accuracy of the resulting variant calls on the old reference. By leveraging the quality improvements of T2T-CHM13, levioSAM2 reduces small and structural variant calling errors compared with GRC-based mapping using real short- and long-read datasets. Performance is especially improved for a set of complex medically relevant genes, where the GRC references are lower quality.

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Fig. 1: Overview of levioSAM2.
Fig. 2: Small variant calling performance.
Fig. 3: Small variant calling performance in difficult regions.
Fig. 4: Small and SV calling using PacBio-HiFi reads from HG002.
Fig. 5: LevioSAM2 resolved large-scale mapping errors in medically relevant genes.
Fig. 6: Runtime of levioSAM2-lift and levioSAM2 workflows.

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Data availability

The Illumina data are from Baid et al.37. The PacBio-HiFi data are from Jarvis et al.41. The HG002 ONT data were sequenced at the Human Genome Sequencing Center, Baylor College of Medicine, and are available at https://www.ncbi.nlm.nih.gov/sra/PRJNA930475. Source data are provided with this paper.

Code availability

The software is available at https://github.com/milkschen/leviosam2 under the MIT license64. The experiments described in this paper are further described at https://github.com/milkschen/levioSAM2-experiments under the MIT license65.

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Acknowledgements

We thank T. Mun for his advice and contribution to the levioSAM2 programming infrastructure. We appreciate advice from H.-C. Chen on software deployment, C. Pockrandt on mappability resources, A. Shumate on gene lift-over and S. Zarate on T2T-CHM13 variant analysis. We also thank A. Carroll and P.-C. Chang for DeepVariant discussions, A. Rhie for T2T-CHM13 discussions and J. Zook for GIAB strata suggestions. N.-C.C. and B.L. were supported by National Institutes of Health (NIH) grants R01HG011392 and R35GM139602 to B.L. F.J.S. and L.F.P. were supported by NIH grants 1U01HG011758-01 and UM1HG008898. S.K. and A.M.P. were supported by the Intramural Research Program of the National Human Genome Research Institute (NHGRI), NIH. Part of this research project was conducted using computational resources at the Maryland Advanced Research Computing Center (MARCC). Prebuilt levioSAM2 resources for T2T-CHM13 to GRC references are made freely available on Amazon Web Services thanks to the AWS Public Dataset Program.

Author information

Authors and Affiliations

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

    Nae-Chyun Chen & Ben Langmead

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

    Luis F. Paulin & Fritz J. Sedlazeck

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

    Fritz J. Sedlazeck

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

    Sergey Koren & Adam M. Phillippy

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Contributions

N.-C.C., S.K., A.M.P. and B.L. designed the method. N.-C.C. wrote the software. N.-C.C. and L.F.P. performed the experiment. N.-C.C., L.F.P., F.J.S., S.K., A.M.P. and B.L. performed analysis and wrote the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Nae-Chyun Chen or Ben Langmead.

Ethics declarations

Competing interests

N.-C.C. is an employee of Exai Bio. L.F.P. received financial funds from Genentech. L.F.P. received travel funds to speak at events hosted by ONT. F.J.S. received research support from Genetech, Illumina, Pacbio and ONT. S.K. has received travel funds to speak at events hosted by Oxford Nanopore Technologies for ethics conflicts. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks Jan Korbel, Erik Garrison and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Lin Tang, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 Regions unique to T2T-CHM13 and variant calls within.

Regions unique to T2T-CHM13 compared to GRCh38 (blue) and high-quality HG002 variant calls from DeepVariant in these regions (red).

Extended Data Fig. 2 Mapping accuracy comparison using simulated data.

Mapping accuracy using simulated reads that carry GRCh38-based HG001 genotypes.

Extended Data Fig. 3 Peak memory usage comparison.

Peak memory usage of levioSAM2 and direct-to-GRC pipelines using a real 30 × WGS dataset from HG002. The alignment steps used BWA-MEM. The lift-over tasks (‘CHM13-to-GRCh38’ and ‘CHM13-to-GRCh37’) excluded the cost of the initial mapping step to T2T-CHM13 (‘CHM13’).

Extended Data Fig. 4 Small-variant calling performance using DeepVariant.

Small variant calling performance in difficult regions using DeepVariant. A. Small variant calling accuracy in major difficult genomic regions for HG002. B. GIAB stratified regions with top small variant calling error reduction densities by levioSAM2.

Source data

Extended Data Fig. 5 A disagreed SV call between the GIAB Tier 1 SV callset and personalized assemblies.

IGV visualization near chr5:21,543,010 for the HG002 PacBio-HiFi dataset. The reads were grouped using the allele at chr5:21,543,010. A 174-bp DEL was called when using direct-to-GRCh37, matching the GIAB Tier 1 SV callset. However, personalized whole-genome assemblies showed mappings of non-GRCh37 haplotypes in this region (the ‘2’ alignment in ‘HG002 Hap1’ and ‘HG002 Hap2’ tracks), suggesting collapsed mapping. The CHM13-to-GRCh37 mappings showed better concordance with the personalized HG002 assemblies.

Extended Data Fig. 6 An example of improved mapping using ONT data.

IGV visualization near chr7:125,400,000 (located in the KMT2C gene) for the HG002 ONT dataset. Four FP SV calls were made when aligning reads directly to GRCh38 because of large-scale mapping collapse. The levioSAM2 workflow (‘CHM13-to-GRCh38’) generated improved alignments and did not result in the FP SV calls.

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2, Figs. 1–9 and Tables 1–19.

Reporting Summary

Peer Review File

Source data

Source Data Fig. 2

Variant calling summary reports generated using hap.py for HG001, HG002 and HG005 Illumina data.

Source Data Fig. 3

Stratified variant calling (GATK) summary generated using hap.py for HG002 Illumina data.

Source Data Fig. 4

Variant calling summary reports of HG002 PacBio-HiFi data.

Source Data Fig. 6

Computational efficiency reports.

Source Data Extended Data Fig. 4

Stratified variant calling (DeepVariant) summary generated using hap.py for HG002 Illumina data.

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Chen, NC., Paulin, L.F., Sedlazeck, F.J. et al. Improved sequence mapping using a complete reference genome and lift-over. Nat Methods 21, 41–49 (2024). https://doi.org/10.1038/s41592-023-02069-6

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