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Approaching complete genomes, transcriptomes and epi-omes with accurate long-read sequencing

Nature Methods volume 20, pages 12–16 (2023)Cite this article

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The year 2022 will be remembered as the turning point for accurate long-read sequencing, which now establishes the gold standard for speed and accuracy at competitive costs. We discuss the key bioinformatics techniques needed to power long reads across application areas and close with our vision for long-read sequencing over the coming years.

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Fig. 1: Long-read sequencing methods and applications.
Fig. 2: Improvement of sequencing technologies.

References

  1. Nurk, S. et al. Science 376, 44–53 (2022).

    Article  CAS  Google Scholar 

  2. Aganezov, S. et al. Science 376, eabl3533 (2022).

    Article  CAS  Google Scholar 

  3. Gorzynski, J. E. et al. N. Engl. J. Med. 386, 700–702 (2022).

    Article  Google Scholar 

  4. Hufford, M. B. et al. Science 373, 655–662 (2021).

    Article  CAS  Google Scholar 

  5. Glinos, D. A. et al. Nature 608, 353–359 (2022).

    Article  CAS  Google Scholar 

  6. Naish, M. et al. Science 374, eabi7489 (2021).

    Article  Google Scholar 

  7. Gershman, A. et al. Science 376, eabj5089 (2022).

    Article  CAS  Google Scholar 

  8. Goodwin, S., McPherson, J. D. & McCombie, W. R. Nat. Rev. Genet. 17, 333–351 (2016).

    Article  CAS  Google Scholar 

  9. Wenger, A. M. et al. Nat. Biotechnol. 37, 1155–1162 (2019).

    Article  CAS  Google Scholar 

  10. Silvestre-Ryan, J. & Holmes, I. Genome Biol. 22, 38 (2021).

    Article  Google Scholar 

  11. Ekim, B., Berger, B. & Chikhi, R. Cell Syst. 12, 958–968.e6 (2021).

    Article  CAS  Google Scholar 

  12. Baid, G. et al. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01435-7 (2022).

    Article  Google Scholar 

  13. Furlan, M. et al. RNA Biol. 18 (Suppl. 1), 31–40 (2021).

  14. Kovaka, S., Fan, Y., Ni, B., Timp, W. & Schatz, M. C. Nat. Biotechnol. 39, 431–441 (2021).

    Article  CAS  Google Scholar 

  15. Payne, A. et al. Nat. Biotechnol. 39, 442–450 (2021).

    Article  CAS  Google Scholar 

  16. Gamaarachchi, H. et al. Nat. Biotechnol. 40, 1026–1029 (2022).

    Article  CAS  Google Scholar 

  17. Watson, M. & Warr, A. Nat. Biotechnol. 37, 124–126 (2019).

    Article  CAS  Google Scholar 

  18. Rautiainen, M. et al. Preprint at bioRxiv https://doi.org/10.1101/2022.06.24.497523 (2022).

  19. Ou, S. et al. Preprint at bioRxiv https://doi.org/10.1101/2022.10.09.511471 (2022).

  20. Vollger, M. R., Kerpedjiev, P., Phillippy, A. M. & Eichler, E. E. Bioinformatics https://doi.org/10.1093/bioinformatics/btac018 (2022).

    Article  Google Scholar 

  21. Sedlazeck, F. J. et al. Nat. Methods 15, 461–468 (2018).

    Article  CAS  Google Scholar 

  22. Audano, P. A. et al. Cell 176, 663–675.e19 (2019).

    Article  CAS  Google Scholar 

  23. Alonge, M. et al. Cell 182, 145–161.e23 (2020).

    Article  CAS  Google Scholar 

  24. Sone, J. et al. Nat. Genet. 51, 1215–1221 (2019).

    Article  CAS  Google Scholar 

  25. Della Coletta, R., Qiu, Y., Ou, S., Hufford, M. B. & Hirsch, C. N. Genome Biol. 22, 3 (2021).

    Article  Google Scholar 

  26. Li, H. Bioinformatics 34, 3094–3100 (2018).

    Article  CAS  Google Scholar 

  27. Marco-Sola, S., Moure, J. C., Moreto, M. & Espinosa, A. Bioinformatics 37, 456–463 (2021).

    Article  CAS  Google Scholar 

  28. Kirsche, M. et al. Preprint at bioRxiv https://doi.org/10.1101/2021.05.27.445886 (2021).

  29. Wyman, D. & Mortazavi, A. Bioinformatics 35, 340–342 (2019).

    Article  CAS  Google Scholar 

  30. Kovaka, S. et al. Genome Biol. 20, 278 (2019).

    Article  CAS  Google Scholar 

  31. Chen, Y. et al. Preprint at bioRxiv https://doi.org/10.1101/2021.04.21.440736 (2021).

  32. Drexler, H. L. et al. Nat. Protoc. 16, 1343–1375 (2021).

    Article  CAS  Google Scholar 

  33. Lebrigand, K., Magnone, V., Barbry, P. & Waldmann, R. Nat. Commun. 11, 4025 (2020).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We would like to thank all past and current members of the Schatz lab, as well as our long-read collaborators, especially Timour Baslan, Andrew Carroll, Jason Chin, Megan Dennis, Evan Eichler, Tom Gingeras, Mark Gerstein, Sara Goodwin, Ian Henderson, Candice Hirsch, Matthew Hufford, Alison Klein, Ben Langmead, Zach Lippman, Erich Jarvis, W. Richard McCombie, Rajiv McCoy, Karen Miga, Rachel O’Neill, Mihaela Pertea, Adam Phillippy, Fritz Sedlazeck, Steven Salzberg, Winston Timp, Eli Van Allen, Justin Zook, and many others. Finally, we would also like to thank the researchers at PacBio and Oxford Nanopore for their developments and collaborations. This work was supported in part by the US National Science Foundation (IOS-2216612, IOS-1758800), the US National Institutes of Health (U24HG010263, U41HG006620, U01CA253481), and the Human Frontier Science Program (RGP0025/2021).

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Authors and Affiliations

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

    Sam Kovaka, Shujun Ou, Katharine M. Jenike & Michael C. Schatz

  2. Department of Molecular Genetics, Ohio State University, Columbus, OH, USA

    Shujun Ou

  3. Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA

    Katharine M. Jenike & Michael C. Schatz

  4. Department of Biology, Johns Hopkins University, Baltimore, MD, USA

    Michael C. Schatz

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  1. Sam Kovaka
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  2. Shujun Ou
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Correspondence to Michael C. Schatz.

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Kovaka, S., Ou, S., Jenike, K.M. et al. Approaching complete genomes, transcriptomes and epi-omes with accurate long-read sequencing. Nat Methods 20, 12–16 (2023). https://doi.org/10.1038/s41592-022-01716-8

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