Cited by 277Genomics and Phylogenetic StudiesGenome Rearrangement AlgorithmsAlgorithms and Data Compression

Building pangenome graphs

Open access
Garrison, Erik, Guarracino, Andrea, Heumos, Simon, Villani, Flavia, Bao, Zhigui, Tattini, Lorenzo, Hagmann, Jörg, Vorbrugg, Sebastian +22 more·2024·Nature Methods

Published version of a preprint

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41592-024-02430-3

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 21

  1. Garrison, E., Sirén, J., Novak, A.M., Hickey, G., Eizenga, J.M., Dawson, E.T., Jones, W., Garg, S., Markello, C., Lin, M.F., Paten, B., Durbin, R.: Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nature Biotechnology 36(9), 875-879 (2018). https://doi.org/10.1038/nbt.4227 · DOI · Paper page
  2. Paten, B., Novak, A.M., Eizenga, J.M., Garrison, E.: Genome graphs and the evolution of genome inference. Genome Res. 27(5), 665-676 (2017). https://doi.org/10.1101/gr.214155.116 · DOI · Paper page
  3. Garrison, E.: Graphical pangenomics (2019). https://doi.org/10.17863/ CAM.41621 · DOI
  4. Guarracino, A., Heumos, S., Nahnsen, S., Prins, P., Garrison, E.: ODGI: understanding pangenome graphs. Bioinformatics (2022). https://doi. org/10.1093/bioinformatics/btac308 · DOI
  5. Noll, N., Molari, M., Shaw, L.P., Neher, R.A.: PanGraph: scalable bac- terial pan-genome graph construction (2022). https://doi.org/10.1101/ 2022.02.24.481757 · DOI
  6. Garrison, E., Guarracino, A.: Unbiased pangenome graphs. Bioinformatics 39(1) (2022). https://doi.org/10.1093/bioinformatics/btac743 · DOI
  7. Minkin, I., Pham, S., Medvedev, P.: TwoPaCo: an efficient algo- rithm to build the compacted de bruijn graph from many complete genomes. Bioinformatics 33(24), 4024-4032 (2016). https://doi.org/10. 1093/bioinformatics/btw609 · DOI
  8. Cagan, A., Theunert, C., Laayouni, H., Santpere, G., Pybus, M., Casals, F., Prüfer, K., Navarro, A., Marques-Bonet, T., Bertranpetit, J., Andrés, A.M.: Natural selection in the great apes. Mol Biol Evol 33(12), 3268- 3283 (2016). https://doi.org/10.1093/molbev/msw215 · DOI
  9. Logsdon, G.A., Vollger, M.R., Hsieh, P., Mao, Y., Liskovykh, M.A., Koren, S., Nurk, S., Mercuri, L., Dishuck, P.C., Rhie, A., de Lima, L.G., Dvorkina, T., Porubsky, D., Harvey, W.T., Mikheenko, A., Bzikadze, A.V., Kremitzki, M., Graves-Lindsay, T.A., Jain, C., Hoekzema, K., Murali, S.C., Munson, K.M., Baker, C., Sorensen, M., Lewis, A.M., Surti, U., Gerton, J.L., Larionov, V., Ventura, M., Miga, K.H., Phillippy, A.M., Eichler, E.E.: The structure, function and evolution of a com- plete human chromosome 8. Nature 593(7857), 101-107 (2021). https: //doi.org/10.1038/s41586-021-03420-7 · DOI
  10. Crysnanto, D., Leonard, A., Pausch, H.: Comparison of methods for build- ing pangenome graphs. In: Proceeding of 12th World Congress on Genetics Applied to Livestock Production (WCGALP) Technical and Species Ori- entated Innovations in Animal Breeding, and Contribution of Genetics to Solving Societal Challenges, pp. 1066-1069 (2022). Wageningen Academic Publishers
  11. Zhang, H., Wu, S., Aluru, S., Li, H.: Fast sequence to graph alignment using the graph wavefront algorithm. arXiv preprint arXiv:2206.13574 (2022) · arXiv
  12. Leonard, A.S., Crysnanto, D., Mapel, X.M., Bhati, M., Pausch, H.: Graph construction method impacts variation representation and analyses in a bovine super-pangenome (2022). https://doi.org/10.1101/2022.09.17. 508368 · DOI
  13. Marçais, G., Delcher, A.L., Phillippy, A.M., Coston, R., Salzberg, S.L., Zimin, A.: MUMmer4: A fast and versatile genome alignment system. PLoS Computational Biology 14(1), 1005944 (2018). https://doi.org/10. 1371/journal.pcbi.1005944 · DOI · Paper page
  14. Marco-Sola, S., Moure, J.C., Moreto, M., Espinosa, A.: Fast gap-affine pairwise alignment using the wavefront algorithm. Bioinformatics (2020). https://doi.org/10.1093/bioinformatics/btaa777 · DOI
  15. Marco-Sola, S., Eizenga, J.M., Guarracino, A., Paten, B., Garrison, E., Moreto, M.: Optimal gap-affine alignment in o(s) space. Bioinformatics 39(2) (2023). https://doi.org/10.1093/bioinformatics/btad074 · DOI · Paper page
  16. Jain, C., Koren, S., Dilthey, A., Phillippy, A.M., Aluru, S.: A fast adaptive algorithm for computing whole-genome homology maps. Bioinformatics 34(17), 748-756 (2018). https://doi.org/10.1093/bioinformatics/bty597 · DOI
  17. Bollobás, B.: The evolution of random graphs-the giant component. In: Random Graphs, vol. 184, pp. 130-59 (2001). Cambridge university press Cambridge
  18. Lee, C., Grasso, C., Sharlow, M.F.: Multiple sequence alignment using partial order graphs. Bioinformatics 18(3), 452-464 (2002). https://doi. org/10.1093/bioinformatics/18.3.452 · DOI
  19. Ewels, P., Magnusson, M., Lundin, S., Käller, M.: Multiqc: summarize analysis results for multiple tools and samples in a single report. Bioin- formatics 32(19), 3047 (2016). https://doi.org/10.1093/bioinformatics/ btw354 · DOI · Paper page
  20. Garrison, E., Kronenberg, Z.N., Dawson, E.T., Pedersen, B.S., Prins, P.: A spectrum of free software tools for processing the VCF variant call format: vcflib, bio-vcf, cyvcf2, hts-nim and slivar. PLoS Computational Biology 18(5), 1009123 (2022). https://doi.org/10.1371/journal.pcbi.1009123 · DOI
  21. Llamas, B., Narzisi, G., Schneider, V., Audano, P.A., Biederstedt, E., Blauvelt, L., Bradbury, P., Chang, X., Chin, C.-S., Fungtammasan, A., Clarke, W.E., Cleary, A., Ebler, J., Eizenga, J., Sibbesen, J.A., Markello, C.J., Garrison, E., Garg, S., Hickey, G., Lazo, G.R., Lin, M.F., Mahmoud, M., Marschall, T., Minkin, I., Monlong, J., Musunuri, R.L., Sagayaradj, S., Novak, A.M., Rautiainen, M., Regier, A., Sedlazeck, F.J., Siren, J., Souilmi, Y., Wagner, J., Wrightsman, T., Yokoyama, T.T., Zeng, Q., Zook, J.M., Paten, B., Busby, B.: A strategy for building and using a human reference pangenome. F1000Res 8, 1751 (2021). https://doi.org/ 10.12688/f1000research.19630.2 · DOI

Cited by

Sorted by citations

Authors
Garrison, E., Guarracino, A., Heumos, S., Villani, F., Bao, Z., Tattini, L., Hagmann, J., Vorbrugg, S., Marco-Sola, S., Kubica, C., Ashbrook, D. G., Thorell, K., Rusholme-Pilcher, R. L., Liti, G., Rudbeck, E., Golicz, A. A., Nahnsen, S., Yang, Z., Mwaniki, M. N., Nobrega, F. L., Wu, Y., Chen, H., de Ligt, J., Sudmant, P. H., Huang, S., Weigel, D., Soranzo, N., Colonna, V., Williams, R. W., & Prins, P.
Affiliations
  • University of Tennessee Health Science Center (US)
  • Universitätsklinikum Tübingen (DE)
  • Agricultural Genomics Institute at Shenzhen (CN)
  • Centre National de la Recherche Scientifique (FR)
  • Max Planck Institute for Biology (DE)
  • University of Gothenburg (SE)
  • Justus-Liebig-Universität Gießen (DE)
  • New Zealand Institute for Public Health and Forensic Science (NZ)
  • University of Pisa (IT)
  • University of Southampton (GB)
  • University of California, Berkeley (US)
  • University of Cambridge (GB)
Corresponding author
Garrison, Erik
Venue
Nature Methods
Published
2024-10-21
Volume
21
Issue
11
Pages
2008-2012
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
OpenAlex
W4403609447
Cited by
277
References
8
Citations by year
2026: 107 · 2025: 95 · 2024: 56 · 2023: 19
FWCI
33.57
Type
journal article
Language
en
Open access
Locations
  • Nature Methods (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, cc-by-nc-nd)
  • PubMed Central (submittedVersion, cc-by)
  • Figshare (submittedVersion)
Funders
  • National Science Foundation (2118709)
  • U.S. Department of Health and Human Services (U01DA047638, R01GM123489)
  • Alexander von Humboldt-Stiftung
  • European Commission (956229)
  • Deutsche Forschungsgemeinschaft
  • Bundesministerium für Bildung und Forschung (031A537C, 031A533A, 031A532B, 031A533A, 031A533B, 031A534A, 031A535A, 031A537A, 031A537B, 031A537C, 031A537D, 031A538A, 031A535A, 031A537A, 031A538A, 031A537D, 031A534A, 031A532B, 031A533B, 031A537B)
  • Nederlandse Organisatie voor Wetenschappelijk Onderzoek (2021.040)
  • Max-Planck-Gesellschaft
  • Bundesministerium für Wirtschaft und Energie
  • German Network for Bioinformatics Infrastructure (031A533A, 031A534A, 031A538A, 031A537 A, 031A537D, 031 A535 A, 031 A537B, 031 A532B, 031 A533B, #031A537C, 031A532B,031A533A,031A533B,031A534A,031A535A,031A537A,031A537B,031A537C,031A537D,031A538A)
  • National Institutes of Health
  • High Performance Research Computing, Texas A and M University
  • Biotechnology and Biological Sciences Research Council (BBS/E/ER/230003C, BBS/E/ER/230003A, BBS/E/ER/230002A, BBS/E/ER/230002B)
  • National Institute on Drug Abuse (NIDA U01DA047638, U01DA047638)
  • National Human Genome Research Institute (R01HG013017)
  • National Institute of General Medical Sciences (R01GM123489, R35GM142916)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genome Rearrangement Algorithms
  • Physical Sciences › Computer Science › Artificial Intelligence › Algorithms and Data Compression
Keywords
pangenome graphs, variation graphs, recombination detection, phylogenetic inference, genomic variation, complex sequences

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review