Abstract
Pangenome graphs can represent all variation between multiple reference genomes, but current approaches to build them exclude complex sequences or are based upon a single reference. In response, we developed the PanGenome Graph Builder, a pipeline for constructing pangenome graphs without bias or exclusion. The PanGenome Graph Builder uses all-to-all alignments to build a variation graph in which we can identify variation, measure conservation, detect recombination events and infer phylogenetic relationships.
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Data availability
Pangenomes are available at Zenodo at https://doi.org/10.5281/zenodo.7658895 (ref. 37). Scripts and source data URLs for downloading the pangenomes at https://github.com/pangenome/pggb-paper/blob/main/workflows/0.Preparation.md. Lists of all accession codes for all pangenomes are reported in Supplementary File 1.
Code availability
PGGB is available at https://github.com/pangenome/pggb. Code used for experiments can be accessed at https://github.com/pangenome/pggb-paper.
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Acknowledgements
The authors gratefully acknowledge support from National Institutes of Health (NIH)/NIDA U01DA047638 (E.G.), NIH/NIGMS R01GM123489 (E.G. and P.P.), NIH/NIGMS R35GM142916 (P.H.S.) and NSF PPoSS Award 2118709 (E.G. and P.P.) and the Center for Integrative and Translational Genomics (E.G.). S.H. acknowledges funding from the Central Innovation Program (ZIM) for SMEs of the Federal Ministry for Economic Affairs and Energy of Germany. This work was supported by the BMBF-funded de.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI) (031A532B, 031A533A, 031A533B, 031A534A, 031A535A, 031A537A, 031A537B, 031A537C, 031A537D and 031A538A). A.A.G. acknowledges the Alexander von Humboldt Foundation in the framework of Sofja Kovalevskaja Award and Deutsche Forschungsgemeinschaft (German Research Foundation) project no. 497667402. S.N. acknowledges support from iFIT funded by the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy—EXC 2180—390900677 and CMFI under EXC 2124-390838134. The authors also acknowledge funding from the Max Planck Society (Z.B., S.V., C.K. and D.W.). Co-financed by the Connecting Europe Facility of the European Union. M.N.M. is fully funded by the EU H2020 ALPACA ITN under the Marie Skłodowska-Curie grant agreement no. 956229. The authors thank members of the HPRC Pangenome Working Group for their insightful discussion and feedback and members of the HPRC production teams for their development of resources used in our exposition.
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Authors and Affiliations
Contributions
E.G. conceived the project. E.G., S.N., N.S., V.C., R.W.W. and P.P. provided project guidance. E.G., A.G., Simon H. and S.M.S. developed the software. E.G., A.G., Simon H., V.C., R.W.W. and P.P. edited the paper. E.G. designed the experiments. E.G., A.G., and L.T. evaluated quality. E.G., A.G., Simon H., F.V., Z.B., L.T., J.H., S.V., C.K., K.T., R.L.R.P., A.A.G., S.N., Z.Y., M.N.M., F.L.N., H.C., J.d.L. and P.H.S. conducted testing. A.G. executed the experiments. A.G. and Simon H. provided documentation. F.V., D.G.A., H.C. and V.C. worked on Mus musculus and Rattus norvegicus. Z.B. and Sanwen H. contributed to the tomato pangenome. L.T. and G.L. worked on Saccharomyces cerevisiae and S. paradoxus. J.H. contributed to the soy pangenome. S.V., C.K., Z.B. and D.W. worked on A. thaliana. S.V. determined parameter settings. K.T. and E.R. worked on Helicobacter pylori. A.A.G. contributed to Vicia fava. Z.Y. and J.d.L. worked on Neisseria mingitidis. F.L.N. and Y.W. worked on Escherichia coli and Coliphages. P.H.S. contributed to the primate pangenome. P.P. managed High Performance Computing.
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J.H. is employed by Computomics. D.W. holds equity in Computomics and consults for KWS SE. All other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 SMOOTHXG.
Overview of the algorithmic steps in SMOOTHXG.
Extended Data Fig. 2 Impact of graph normalization on the human chromosome 6 pangenome graph.
Comparison of the raw SEQWISH graph and its normalized version after SMOOTHXG. The normalization process locally compresses and simplifies the graph structure, resulting in lower node degree and graph depth. This effect is particularly pronounced in repetitive regions such as centromeres and satellite sequences. Node degree and graph depth are visualized for both the original SEQWISH graph and the normalized graph.
Extended Data Fig. 3 Impact of graph normalization on complement component 4 (C4) pangenome graph.
Each bar represents a haplotype and black lines on the bottom represent the graph topology. Paths are colored by using the Spectra color palette with four levels of node depths: white indicates no depth, while gray, red, and yellow indicate depths 1, 2, and greater than or equal to 3, respectively. a) C4 subgraph extracted from the chromosome 6 graph built without SMOOTHXG. b) C4 subgraph extracted from the chromosome 6 graph built without SMOOTHXG and sorted. c). C4 subgraph extracted from the chromosome 6 graph built with SMOOTHXG. The two references present two different allele copies of the C4 genes (red = 2X coverage), both of them including the HERV sequence. The entirely gray paths have one copy of these genes (gray = 1X coverage). HG01071#2 presents three copies of the locus (orange = 3X coverage), of which one contains the HERV sequence (gray in the middle of the orange).
Extended Data Fig. 4 Human chromosome 6 variant calling performance.
Precision, recall, and F1-score of small variants in the H. sapiens chromosome 6 pangenome graph relative to HiFi–DeepVariant calls. Comparisons are made whole-chromosome and then stratified by the GIAB (v.3.0) genomic context. The 44 samples evaluated are colored by superpopulation. AFR = African, AMR = Ad Mixed American, EAS = East Asian, SAS = South Asian.
Extended Data Fig. 5 A. thaliana variant calling performance.
Precision, recall, and F1-score of small variants in the A. thaliana pangenome graph relative to HiFi–DeepVariant calls. Comparisons are made whole-genome and then stratified by genomic context. Easy and Hard regions exclude and include, respectively, rDNA, centromere, and Trasposable Elements. The 64 samples evaluated are colored by population. The low precision for Lor-16 and Met-6 is due to the high heterozygosity of these 2 samples.
Extended Data Fig. 6 Tomato variant calling performance.
Precision, recall, and F1-score of small variants in the tomato pangenome graph relative to HiFi–DeepVariant calls. Comparisons are made whole-genome and then stratified by genomic context. Easy and Hard regions exclude and include, respectively, Transposable elements. The 5 samples evaluated are colored by group. BIG = S. lycopersicum, big-fruited tomato; CER = S. lycopersicum var. cerasiforme, cherry tomato; PIM = S. pimpinellifolium, the progenitor of cultivated tomatoes.
Supplementary information
Supplementary Information
Supplementary discussion and Figs. 1–9.
Supplementary Table
Tables with list of all accessions for all pangenomes.
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Garrison, E., Guarracino, A., Heumos, S. et al. Building pangenome graphs. Nat Methods 21, 2008–2012 (2024). https://doi.org/10.1038/s41592-024-02430-3
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DOI: https://doi.org/10.1038/s41592-024-02430-3
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