Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Pangenome graph construction from genome alignments with Minigraph-Cactus

Nature Biotechnology volume 42, pages 663–673 (2024)Cite this article

Subjects

Abstract

Pangenome references address biases of reference genomes by storing a representative set of diverse haplotypes and their alignment, usually as a graph. Alternate alleles determined by variant callers can be used to construct pangenome graphs, but advances in long-read sequencing are leading to widely available, high-quality phased assemblies. Constructing a pangenome graph directly from assemblies, as opposed to variant calls, leverages the graph’s ability to represent variation at different scales. Here we present the Minigraph-Cactus pangenome pipeline, which creates pangenomes directly from whole-genome alignments, and demonstrate its ability to scale to 90 human haplotypes from the Human Pangenome Reference Consortium. The method builds graphs containing all forms of genetic variation while allowing use of current mapping and genotyping tools. We measure the effect of the quality and completeness of reference genomes used for analysis within the pangenomes and show that using the CHM13 reference from the Telomere-to-Telomere Consortium improves the accuracy of our methods. We also demonstrate construction of a Drosophila melanogaster pangenome.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Minigraph-Cactus pangenome construction.
Fig. 2: Evaluating GRCh38-based and T2T-CHM13-based human pangenomes.
Fig. 3: Comparing pangenome SV genotyping.
Fig. 4: A D. melanogaster pangenome.

Similar content being viewed by others

Data availability

All data, software versions and commands are available at https://github.com/ComparativeGenomicsToolkit/cactus/tree/master/doc/mc-paper.

HPRC graphs can be downloaded from https://s3-us-west-2.amazonaws.com/human-pangenomics/index.html?prefix=pangenomes/freeze/freeze1/minigraph-cactus/. Consult the Data Portal for explanations of the different files: https://github.com/human-pangenomics/hpp_pangenome_resources/. Variant calls can be downloaded from https://s3-us-west-2.amazonaws.com/human-pangenomics/index.html?prefix=publications/mc_2022/hprc-human/. SV genotyping results are available at https://doi.org/10.5281/zenodo.7669083. D. melanogaster graphs can be downloaded from https://s3-us-west2.amazonaws.com/human-pangenomics/index.html?prefix=publications/mc_2022/mc_pangenomes/16-fruitfly-mc-2022-05-26/. Consult the Data Portal for explanations of the different files: https://github.com/ComparativeGenomicsToolkit/cactus/tree/master/doc/mc-pangenomes. D. melanogaster mapping and calling results can be downloaded from https://s3-us-west-2.amazonaws.com/human-pangenomics/index.html?prefix=publications/mc_2022/fruitfly/.

Code availability

All source code for the Minigraph-Cactus pangenome pipeline, as well as release binaries, Docker images and user manuals, can be found at https://github.com/ComparativeGenomicsToolkit/cactus.

References

  1. Eizenga, J. M. et al. Pangenome graphs. Annu. Rev. Genomics Hum. Genet. 21, 139–162 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Miga, K. H. & Wang, T. The need for a human pangenome reference sequence. Annu. Rev. Genomics Hum. Genet. 22, 81–102 (2021).

    PubMed  PubMed Central  Google Scholar 

  3. Garrison, E. et al. Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nat. Biotechnol. 36, 875–879 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Abel, H. J. et al. Mapping and characterization of structural variation in 17,795 human genomes. Nature 583, 83–89 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Hickey, G. et al. Genotyping structural variants in pangenome graphs using the vg toolkit. Genome Biol. 21, 35 (2020).

    PubMed  PubMed Central  Google Scholar 

  6. Sirén, J. et al. Pangenomics enables genotyping of known structural variants in 5202 diverse genomes. Science 374, abg8871 (2021).

    PubMed  PubMed Central  Google Scholar 

  7. Paten, B. et al. Superbubbles, ultrabubbles, and cacti. J. Comput. Biol. 25, 649–663 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Rautiainen, M. et al. Telomere-to-telomere assembly of diploid chromosomes with Verkko. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01435-7 (2023).

  9. Just, W. Computational complexity of multiple sequence alignment with SP-score. J. Comput. Biol. 8, 615–623 (2004).

    Google Scholar 

  10. Kille, B., Balaji, A., Sedlazeck, F. J., Nute, M. & Treangen, T. J. Multiple genome alignment in the telomere-to-telomere assembly era. Genome Biol. 23, 182 (2022).

    PubMed  PubMed Central  Google Scholar 

  11. Blanchette, M. et al. Aligning multiple genomic sequences with the threaded blockset aligner. Genome Res. 14, 708–715 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Harris, R. S. Improved Pairwise Alignment of Genomic DNA. PhD thesis, Pennsylvania State Univ. (2007).

  13. Armstrong, J. et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature 587, 246–251 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Goenka, S. D., Turakhia, Y., Paten, B. & Horowitz, M. SegAlign: a scalable GPU-based whole genome aligner. In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis. https://doi.org/10.1109/sc41405.2020.00043 (IEEE, 2020).

  15. Paten, B. et al. Cactus graphs for genome comparisons. J. Comput. Biol. 18, 461–489 (2011).

    Google Scholar 

  16. Li, H., Feng, X. & Chu, C. The design and construction of reference pangenome graphs with minigraph. Genome Biol. 21, 265 (2020).

    PubMed  PubMed Central  Google Scholar 

  17. Lee, C., Grasso, C. & Sharlow, M. F. Multiple sequence alignment using partial order graphs. Bioinformatics 18, 452–464 (2002).

    CAS  PubMed  Google Scholar 

  18. Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Vivian, J. et al. Toil enables reproducible, open source, big biomedical data analyses. Nat. Biotechnol. 35, 314–316 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Paten, B. et al. Cactus: algorithms for genome multiple sequence alignment. Genome Res. 21, 1512–1528 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Hickey, G., Paten, B., Earl, D., Zerbino, D. & Haussler, D. HAL: a hierarchical format for storing and analyzing multiple genome alignments. Bioinformatics 29, 1341–1342 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Fiddes, I. T. et al. Comparative Annotation Toolkit (CAT)-simultaneous clade and personal genome annotation. Genome Res. 28, 1029–1038 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Doerr, D. GFAffix. https://github.com/marschall-lab/GFAffix (2022).

  24. Bzikadze, A. V. & Pevzner, P. A. TandemAligner: a new parameter-free framework for fast sequence alignment. Preprint at bioRxiv https://doi.org/10.1101/2022.09.15.507041 (2022).

  25. Liao, W.-W. et al. A draft human pangenome reference. Nature https://doi.org/10.1038/s41586-023-05896-x (2023).

  26. Nurk, S. et al. The complete sequence of a human genome. Science 376, 44–53 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Rautiainen, M. & Marschall, T. GraphAligner: rapid and versatile sequence-to-graph alignment. Genome Biol. 21, 253 (2020).

    PubMed  PubMed Central  Google Scholar 

  28. Poplin, R. et al. A universal SNP and small-indel variant caller using deep neural networks. Nat. Biotechnol. 36, 983–987 (2018).

    CAS  PubMed  Google Scholar 

  29. Wagner, J. et al. Curated variation benchmarks for challenging medically relevant autosomal genes. Nat. Biotechnol. 40, 672–680 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Ebler, J. et al. Pangenome-based genome inference allows efficient and accurate genotyping across a wide spectrum of variant classes. Nat. Genet. 54, 518–525 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 1000 Genomes Project Consortiumet al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Google Scholar 

  32. Ebert, P. et al. Haplotype-resolved diverse human genomes and integrated analysis of structural variation. Science 372, eabf7117 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Chakraborty, M., Emerson, J. J., Macdonald, S. J. & Long, A. D. Structural variants exhibit widespread allelic heterogeneity and shape variation in complex traits. Nat. Commun. 10, 4872 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Huang, W. et al. Natural variation in genome architecture among 205 Drosophila melanogaster Genetic Reference Panel lines. Genome Res. 24, 1193–1208 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. Preprint at arXiv https://doi.org/10.48550/arXiv.1207.3907 (2012).

  36. Miller, D. E. et al. Identification and characterization of breakpoints and mutations on Drosophila melanogaster balancer chromosomes. G3 (Bethesda) 10, 4271–4285 (2020).

    CAS  PubMed  Google Scholar 

  37. Sherman, R. M. et al. Assembly of a pan-genome from deep sequencing of 910 humans of African descent. Nat. Genet. 51, 30–35 (2019).

    CAS  PubMed  Google Scholar 

  38. Human Pangenome Reference Consortium. HPRC Pangenome Resources. https://github.com/human-pangenomics/hpp_pangenome_resources (2022).

  39. Guarracino, A. et al. Recombination between heterologous human acrocentric chromosomes. Nature https://doi.org/10.1038/s41586-023-05976-y (2023).

  40. Zhou, Y. et al. Graph pangenome captures missing heritability and empowers tomato breeding. Nature 606, 527–534 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Leonard, A. S. et al. Structural variant-based pangenome construction has low sensitivity to variability of haplotype-resolved bovine assemblies. Nat. Commun. 13, 3012 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Li, H. Identifying centromeric satellites with dna-brnn. Bioinformatics 35, 4408–4410 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Numanagic, I. et al. Fast characterization of segmental duplications in genome assemblies. Bioinformatics 34, i706–i714 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Gao, Y. et al. abPOA: an SIMD-based C library for fast partial order alignment using adaptive band. Bioinformatics 37, 2209–2211 (2021).

    CAS  PubMed  Google Scholar 

  45. Earl, D. et al. Alignathon: a competitive assessment of whole-genome alignment methods. Genome Res. 24, 2077–2089 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Garrison, E. & Guarracino, A. Unbiased pangenome graphs. Bioinformatics 39, btac743 (2023).

    CAS  PubMed  Google Scholar 

  47. Eizenga, J. M. et al. Efficient dynamic variation graphs. Bioinformatics 36, 5139–5144 (2020).

    CAS  PubMed Central  Google Scholar 

  48. Sirén, J., Garrison, E., Novak, A. M., Paten, B. & Durbin, R. Haplotype-aware graph indexes. Bioinformatics 36, 400–407 (2020).

    PubMed  Google Scholar 

  49. Mose, L. E., Wilkerson, M. D., Hayes, D. N., Perou, C. M. & Parker, J. S. ABRA: improved coding indel detection via assembly-based realignment. Bioinformatics 30, 2813–2815 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Zook, J. M. et al. Extensive sequencing of seven human genomes to characterize benchmark reference materials. Sci. Data 3, 160025 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Krusche, P. et al. Best practices for benchmarking germline small-variant calls in human genomes. Nat. Biotechnol. 37, 555–560 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Cleary, J. G. et al. Comparing variant call files for performance benchmarking of next-generation sequencing variant calling pipelines. Preprint at bioRxiv https://doi.org/10.1101/023754 (2015).

  53. Li, H. et al. A synthetic-diploid benchmark for accurate variant-calling evaluation. Nat. Methods 15, 595–597 (2018).

    PubMed  PubMed Central  Google Scholar 

  54. broadinstitute/picard. https://github.com/broadinstitute/picard

  55. Kuhn, R. M., Haussler, D. & Kent, W. J. The UCSC Genome Browser and associated tools. Brief. Bioinform. 14, 144–161 (2012).

    PubMed  PubMed Central  Google Scholar 

  56. English, A. C., Menon, V. K., Gibbs, R. A., Metcalf, G. A. & Sedlazeck, F. J. Truvari: refined structural variant comparison preserves allelic diversity. Genome Biol. 23, 271 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Smit, A. F. A., Hubley, R. & Green, P. RepeatMasker Open-4.0. http://www.repeatmasker.org (2013–2015).

  58. Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank A. D. Long for many suggestions and insights regarding the D. melanogaster data and the whole vg team for their work to create and maintain vg, upon which much of this work depends. B.P., A.N., J.M.E. and J.M. were partly supported by National Institutes of Health (NIH) grants R01HG010485, U24HG010262, U24HG011853, OT3HL142481, U01HG010961 (with H.L.) and OT2OD033761. H.L. was partly supported by NIH grant R01HG010040 and T.M. by U01HG010973. Computational infrastructure and support for running PanGenie were provided by the Centre for Information and Media Technology at Heinrich Heine University Düsseldorf.

Author information

Author notes

  1. These authors contributed equally: Glenn Hickey, Jean Monlong.

Authors and Affiliations

  1. UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA

    Glenn Hickey, Jean Monlong, Adam M. Novak, Jordan M. Eizenga, Mobin Asri, Xian H. Chang, Mark Diekhans, Marina Haukness, David Haussler, Julian K. Lucas, Charles Markello, Karen H. Miga, Hugh E. Olsen, Trevor Pesout, Jouni Sirén & Benedict Paten

  2. Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany

    Jana Ebler & Tobias Marschall

  3. Center for Digital Medicine, Heinrich Heine University Düsseldorf, Düsseldorf, Germany

    Jana Ebler, Daniel Doerr, Peter Ebert, Jana Ebler, Hugo Magalhães, Pierre Marijon, Tobias Marschall & Tobias Marschall

  4. Center for Computational and Genomic Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Yan Gao

  5. Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA

    Haoyu Cheng, Justin Chu, Xiaowen Feng & Heng Li

  6. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Haoyu Cheng, Xiaowen Feng, Carlos Garcia Giron & Heng Li

  7. Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, USA

    Haley J. Abel

  8. McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA

    Lucinda L. Antonacci-Fulton, Sarah Cody, Robert S. Fulton, Allison A. Regier, Chad Tomlinson & Ting Wang

  9. Google LLC, Mountain View, CA, USA

    Gunjan Baid, Anastasiya Belyaeva, Andrew Carroll, Pi-Chuan Chang, Daniel E. Cook, Alexey Kolesnikov, Maria Nattestad & Kishwar Shafin

  10. Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA

    Carl A. Baker, Evan E. Eichler, William T. Harvey, Kendra Hoekzema, Jennifer Kordosky, Alexandra P. Lewis, Katherine M. Munson, David Porubsky & Mitchell R. Vollger

  11. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK

    Konstantinos Billis, Susan Fairley, Paul Flicek, Adam Frankish, Leanne Haggerty, Thibaut Hourlier, Jan O. Korbel, Fergal J. Martin & Francesca Floriana Tricomi

  12. Department of Human Genetics, McGill University, Montreal, QC, Canada

    Guillaume Bourque

  13. Canadian Center for Computational Genomics, McGill University, Montreal, QC, Canada

    Guillaume Bourque

  14. Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan

    Guillaume Bourque

  15. Institute of Genetics and Biophysics, National Research Council, Naples, Italy

    Silvia Buonaiuto & Vincenza Colonna

  16. Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA

    Mark J. P. Chaisson & Tsung-Yu Lu

  17. Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA

    Vincenza Colonna, Christian Fischer, Erik Garrison, Andrea Guarracino, Pjotr Prins & Flavia Villani

  18. Arizona State University, Barrett and O’Connor Washington Center, Washington, DC, USA

    Robert M. Cook-Deegan

  19. Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, Santa Cruz, CA, USA

    Omar E. Cornejo & Samuel Sacco

  20. Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany

    Daniel Doerr, Peter Ebert, Jana Ebler, Hugo Magalhães, Pierre Marijon & Tobias Marschall

  21. Core Unit Bioinformatics, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany

    Peter Ebert

  22. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    Evan E. Eichler, David Haussler & Erich D. Jarvis

  23. Vertebrate Genome Laboratory, The Rockefeller University, New York, NY, USA

    Olivier Fedrigo, Giulio Formenti, Erich D. Jarvis & Jacquelyn Mountcastle

  24. National Institutes of Health (NIH)–National Human Genome Research Institute, Bethesda, MD, USA

    Adam L. Felsenfeld, Baergen I. Schultz, Michael W. Smith & Heidi J. Sofia

  25. Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA

    Robert S. Fulton & Ting Wang

  26. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Copenhagen, Denmark

    Shilpa Garg

  27. Institute for Society and Genetics, College of Letters and Science, University of California, Los Angeles, Los Angeles, CA, USA

    Nanibaa’ A. Garrison

  28. Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Nanibaa’ A. Garrison

  29. Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Nanibaa’ A. Garrison

  30. Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA

    Richard E. Green

  31. Dovetail Genomics, Scotts Valley, CA, USA

    Richard E. Green

  32. Quantitative Life Sciences, McGill University, Montreal, QC, Canada

    Cristian Groza

  33. Genomics Research Centre, Human Technopole, Milan, Italy

    Andrea Guarracino

  34. Department of Genetics, Yale University School of Medicine, New Haven, CT, USA

    Ira M. Hall, Wen-Wei Liao & Shuangjia Lu

  35. Center for Genomic Health, Yale University School of Medicine, New Haven, CT, USA

    Ira M. Hall & Wen-Wei Liao

  36. Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany

    Simon Heumos

  37. Biomedical Data Science, Department of Computer Science, University of Tübingen, Tübingen, Germany

    Simon Heumos

  38. Tree of Life, Wellcome Sanger Institute, Hinxton, Cambridge, UK

    Kerstin Howe & Jonathan M. D. Wood

  39. Northeastern University, Boston, MA, USA

    Miten Jain

  40. Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY, USA

    Erich D. Jarvis

  41. Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Hanlee P. Ji & HoJoon Lee

  42. Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Eimear E. Kenny

  43. Program in Bioethics and Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA

    Barbara A. Koenig

  44. European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany

    Jan O. Korbel

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

    Sergey Koren, Ann McCartney, Sergey Nurk, Adam M. Phillippy, Mikko Rautiainen, Arang Rhie & Brian Walenz

  46. Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA

    Wen-Wei Liao

  47. Computer Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain

    Santiago Marco-Sola

  48. Departament d’Arquitectura de Computadors i Sistemes Operatius, Universitat Autònoma de Barcelona, Barcelona, Spain

    Santiago Marco-Sola

  49. Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA

    Jennifer McDaniel, Nathan D. Olson, Justin Wagner & Justin M. Zook

  50. Coriell Institute for Medical Research, Camden, NJ, USA

    Matthew W. Mitchell

  51. Department of Computer Science, University of Pisa, Pisa, Italy

    Moses Njagi Mwaniki

  52. Department of Public Health Sciences, University of California, Davis, Davis, CA, USA

    Alice B. Popejoy

  53. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Daniela Puiu & Aleksey V. Zimin

  54. Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany

    Ashley D. Sanders

  55. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA

    Valerie A. Schneider & Françoise Thibaud-Nissen

  56. Center for Health Data Science, University of Copenhagen, Copenhagen, Denmark

    Jonas A. Sibbesen

  57. Al Jalila Genomics Center of Excellence, Al Jalila Children’s Specialty Hospital, Dubai, UAE

    Ahmad N. Abou Tayoun

  58. Center for Genomic Discovery, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE

    Ahmad N. Abou Tayoun

  59. Division of Medical Genetics, University of Washington School of Medicine, Seattle, WA, USA

    Mitchell R. Vollger

  60. Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA

    Aleksey V. Zimin

Authors
  1. Glenn Hickey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jean Monlong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jana Ebler
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adam M. Novak
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jordan M. Eizenga
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yan Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tobias Marschall
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Heng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Benedict Paten
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

Human Pangenome Reference Consortium

  • Haley J. Abel
  • , Lucinda L. Antonacci-Fulton
  • , Mobin Asri
  • , Gunjan Baid
  • , Carl A. Baker
  • , Anastasiya Belyaeva
  • , Konstantinos Billis
  • , Guillaume Bourque
  • , Silvia Buonaiuto
  • , Andrew Carroll
  • , Mark J. P. Chaisson
  • , Pi-Chuan Chang
  • , Xian H. Chang
  • , Haoyu Cheng
  • , Justin Chu
  • , Sarah Cody
  • , Vincenza Colonna
  • , Daniel E. Cook
  • , Robert M. Cook-Deegan
  • , Omar E. Cornejo
  • , Mark Diekhans
  • , Daniel Doerr
  • , Peter Ebert
  • , Jana Ebler
  • , Evan E. Eichler
  • , Jordan M. Eizenga
  • , Susan Fairley
  • , Olivier Fedrigo
  • , Adam L. Felsenfeld
  • , Xiaowen Feng
  • , Christian Fischer
  • , Paul Flicek
  • , Giulio Formenti
  • , Adam Frankish
  • , Robert S. Fulton
  • , Yan Gao
  • , Shilpa Garg
  • , Erik Garrison
  • , Nanibaa’ A. Garrison
  • , Carlos Garcia Giron
  • , Richard E. Green
  • , Cristian Groza
  • , Andrea Guarracino
  • , Leanne Haggerty
  • , Ira M. Hall
  • , William T. Harvey
  • , Marina Haukness
  • , David Haussler
  • , Simon Heumos
  • , Glenn Hickey
  • , Kendra Hoekzema
  • , Thibaut Hourlier
  • , Kerstin Howe
  • , Miten Jain
  • , Erich D. Jarvis
  • , Hanlee P. Ji
  • , Eimear E. Kenny
  • , Barbara A. Koenig
  • , Alexey Kolesnikov
  • , Jan O. Korbel
  • , Jennifer Kordosky
  • , Sergey Koren
  • , HoJoon Lee
  • , Alexandra P. Lewis
  • , Heng Li
  • , Wen-Wei Liao
  • , Shuangjia Lu
  • , Tsung-Yu Lu
  • , Julian K. Lucas
  • , Hugo Magalhães
  • , Santiago Marco-Sola
  • , Pierre Marijon
  • , Charles Markello
  • , Tobias Marschall
  • , Fergal J. Martin
  • , Ann McCartney
  • , Jennifer McDaniel
  • , Karen H. Miga
  • , Matthew W. Mitchell
  • , Jean Monlong
  • , Jacquelyn Mountcastle
  • , Katherine M. Munson
  • , Moses Njagi Mwaniki
  • , Maria Nattestad
  • , Adam M. Novak
  • , Sergey Nurk
  • , Hugh E. Olsen
  • , Nathan D. Olson
  • , Benedict Paten
  • , Trevor Pesout
  • , Adam M. Phillippy
  • , Alice B. Popejoy
  • , David Porubsky
  • , Pjotr Prins
  • , Daniela Puiu
  • , Mikko Rautiainen
  • , Allison A. Regier
  • , Arang Rhie
  • , Samuel Sacco
  • , Ashley D. Sanders
  • , Valerie A. Schneider
  • , Baergen I. Schultz
  • , Kishwar Shafin
  • , Jonas A. Sibbesen
  • , Jouni Sirén
  • , Michael W. Smith
  • , Heidi J. Sofia
  • , Ahmad N. Abou Tayoun
  • , Françoise Thibaud-Nissen
  • , Chad Tomlinson
  • , Francesca Floriana Tricomi
  • , Flavia Villani
  • , Mitchell R. Vollger
  • , Justin Wagner
  • , Brian Walenz
  • , Ting Wang
  • , Jonathan M. D. Wood
  • , Aleksey V. Zimin
  •  & Justin M. Zook

Contributions

G.H., J.M., H.L. and B.P. designed the method. G.H., J.M. and J.E. contributed to the results and analysis. G.H., J.M., A.N., J.E. and B.P. wrote the mansuscript. All authors contributed to the software. B.P. led the project.

Corresponding authors

Correspondence to Glenn Hickey or Benedict Paten.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Biotechnology thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–20 and Supplementary Tables 1–6.

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hickey, G., Monlong, J., Ebler, J. et al. Pangenome graph construction from genome alignments with Minigraph-Cactus. Nat Biotechnol 42, 663–673 (2024). https://doi.org/10.1038/s41587-023-01793-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41587-023-01793-w

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research