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Fast and accurate genomic analyses using genome graphs

Nature Genetics volume 51, pages 354–362 (2019)Cite this article

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Abstract

The human reference genome serves as the foundation for genomics by providing a scaffold for alignment of sequencing reads, but currently only reflects a single consensus haplotype, thus impairing analysis accuracy. Here we present a graph reference genome implementation that enables read alignment across 2,800 diploid genomes encompassing 12.6 million SNPs and 4.0 million insertions and deletions (indels). The pipeline processes one whole-genome sequencing sample in 6.5 h using a system with 36 CPU cores. We show that using a graph genome reference improves read mapping sensitivity and produces a 0.5% increase in variant calling recall, with unaffected specificity. Structural variations incorporated into a graph genome can be genotyped accurately under a unified framework. Finally, we show that iterative augmentation of graph genomes yields incremental gains in variant calling accuracy. Our implementation is an important advance toward fulfilling the promise of graph genomes to radically enhance the scalability and accuracy of genomic analyses.

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Fig. 1: The graph genome architecture and computational resource requirements.
Fig. 2: Read mapping accuracy using BWA-MEM and graph genomes.
Fig. 3: Variant calling benchmarking between Graph Genome Pipeline and BWA-GATK.
Fig. 4: SV genotyping using graph genomes.
Fig. 5: The effect of iteratively augmented graph genomes on variant calling.

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Code availability

Graph Genome Pipeline is freely available to academic users for non-commercial use. Compiled standalone tools and the License of Use can be accessed at https://www.sevenbridges.com/graph-genome-academic-release/. The source code of the Graph Genome Pipeline tools is not publicly available.

Data availability

Raw sequencing data for the 150 Coriell WGS samples (Figs. 1, 4 and 5) can be accessed from the European Nucleotide Archive under accession PRJEB20654. Raw sequencing data for the Qatari samples (Fig. 5) used can be found under NCBI SRA accessions SRP060765, SRP061943 and SRP061463. Genome in a Bottle data (Fig. 3) are available from the NCBI FTP site (ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data). The Sanger sequencing traces have been deposited in the European Nucleotide Archive under accession PRJEB26700.

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Acknowledgements

We are grateful for the members of the GA4GH Data Workgroup, Benchmarking, and Reference variation initiatives, in particular J. Zook, for insightful discussions and ideas. M. Huvet helped refine the treatment and presentation of ideas behind trio-based benchmarking. Research reported in this publication was supported in part by the UK Department of Health grant SBRI Genomics Competition: Enabling Technologies for Genomic Sequence Data Analysis and Interpretation administered by Genomics England.

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Author notes

  1. These authors contributed equally: Goran Rakocevic, Vladimir Semenyuk.

Authors and Affiliations

  1. Seven Bridges Genomics, Inc, Cambridge, MA, USA

    Goran Rakocevic, Vladimir Semenyuk, Wan-Ping Lee, James Spencer, John Browning, Ivan J. Johnson, Vladan Arsenijevic, Jelena Nadj, Kaushik Ghose, Maria C. Suciu, Sun-Gou Ji, Gülfem Demir, Lizao Li, Berke Ç. Toptaş, Alexey Dolgoborodov, Björn Pollex, Iosif Spulber, Irina Glotova, Péter Kómár, Andrew L. Stachyra, Yilong Li, Milos Popovic, Morten Källberg, Amit Jain & Deniz Kural

  2. Totient, Inc, Cambridge, MA, USA

    Goran Rakocevic, Vladimir Semenyuk, James Spencer, John Browning, Ivan J. Johnson, Vladan Arsenijevic, Jelena Nadj, Kaushik Ghose, Maria C. Suciu, Sun-Gou Ji, Gülfem Demir, Lizao Li, Berke Ç. Toptaş, Björn Pollex, Irina Glotova, Péter Kómár, Andrew L. Stachyra, Yilong Li, Milos Popovic, Amit Jain & Deniz Kural

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Contributions

G.R., V.S., W.-P.L., J.S., A.D., B.P., A.J., and I.S. developed the algorithms and implemented the tools for graph genome alignment. J.B. and I.J.J. developed the algorithms and implemented the tools for variant calling. K.G. implemented the simulation experiments and carried out the benchmarks based on simulated data. V.A., J.N., A.J., and G.R. devised and carried out the experiments with population-specific genome graphs. P.K. and B.C.T. developed the computational tools used for benchmarks based on related genomes, and A.J. and M.C.S. carried out the experiments. S.-G.J., G.D., L.L., and P.K. created the genome graph containing the structural variants, designed, and carried out all of related experiments. M.P. created the machine learning–based variant filters and carried out the related experiments. I.G. and M.K. aided in interpreting the results and worked on the manuscript. Y.L., G.R., and D.K. prepared the manuscript with input from all other authors. D.K. conceived and oversaw the project with assistance from A.J., A.L.S., and M.K.

Corresponding author

Correspondence to Deniz Kural.

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Competing interests

G.R., J.S., V.A., J.N., M.C.S., G.D., L.L., B.C.T., B.P., I.S., I.G., P.K., A.L.S., Y.L., M.P., W.-P.L., M.K., and D.K. were employed by Seven Bridges Genomics Inc. during the development of the described tools. V.S., J.B., I.J.J., K.G., S.-G.J., A.D., and A.J. are current employees of Seven Bridges Genomics Inc. G.R., V.S., J.S., J.B., I.J.J., V.A., K.G., S.-G.J., L.L., I.S., P.K., A.L.S., Y.L., A.J., M.P. and D.K. hold shares, stock options or restricted stock units in Seven Bridges Genomics Inc. D.K. is co-inventor on 12 patents (issued: 14/016,833; 14/811,057; 15/196,345; 14/041,850 14/157,759; 14/157,979; published: 14/517,406; 14/517,419; 14/517,513; 14/517,451; 14/744,536; 14/798,686). V.S. is inventor on four patents (pending: 15/061,235; 14/885,192; 15/598,404; 15/597,464). W.-P.L. is co-inventor on three patents (published: 14/994,385, pending: 15/353,105; 15/007874). B.P., I.S. and A.J. are co-inventors on one patent (pending: 15/452,963). I.J.J is inventor on one patent (62/630,347). Applicant for patents is Seven Bridges Genomics Inc.

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Supplementary information

Supplementary Text and Figures

Supplementary Note, Supplementary Tables 1–4, 6 and 17, and Supplementary Figures 1–22

Reporting Summary

Supplementary Table 5

Computational resource requirements of the Graph Genome Aligner and BWA-MEM

Supplementary Table 7

Precision FDA Truth Contest results vs. Graph Genome Pipeline

Supplementary Table 8

Variant calling benchmarking against genotyping using SNP arrays

Supplementary Table 9

Variant calling benchmarking results from simulated data

Supplementary Table 10

Genome in a Bottle benchmarking results

Supplementary Table 11

Trio benchmarking: inferred variant calling precision and recall

Supplementary Table 12

Trio benchmarking: Mendelian compliance rates with variant representation resolution

Supplementary Table 13

Trio benchmarking: Mendelian compliance rate without variant representation resolution

Supplementary Table 14

Validation of potentially false false positive variants in GiaB samples

Supplementary Table 15

Structure variation coordinates used in SV genotyping benchmarking experiments

Supplementary Table 16

Variant calling using global graph augmented by population-specific variants

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Rakocevic, G., Semenyuk, V., Lee, WP. et al. Fast and accurate genomic analyses using genome graphs. Nat Genet 51, 354–362 (2019). https://doi.org/10.1038/s41588-018-0316-4

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