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Standardized annotation of translated open reading frames

Nature Biotechnology volume 40, pages 994–999 (2022)Cite this article

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To the Editor — Ribosome profiling (Ribo-seq) has extended our understanding of the translational ‘vocabulary’ of the human genome, uncovering thousands of open reading frames (ORFs) within long noncoding RNAs (lncRNAs) and presumed untranslated regions (UTRs) of protein-coding genes. However, reference gene annotation projects have been circumspect in their incorporation of these ORFs because of uncertainties about their experimental reproducibility and physiological roles. Yet, it is clear that certain ‘Ribo-seq ORFs’ make stable proteins, others mediate gene regulation, and many have medical implications. Ultimately, the absence of standardized ORF annotation has created a circular problem: while Ribo-seq ORFs remain unrecognized by reference annotation databases, this lack of recognition will thwart studies examining their roles. Here, we outline a community-led effort involving Ensembl/GENCODE, the HUGO Gene Nomenclature Committee (HGNC), UniProtKB, HUPO/HPP and PeptideAtlas to produce a standardized catalog of 7,264 human Ribo-seq ORFs; a path to bring protein-level evidence for Ribo-seq ORFs into reference annotation databases; and a roadmap to facilitate research in the global community.

Ribo-seq1 provides an RNA-sequencing-based readout of mRNA translation by isolating ribosome-bound RNA fragments of ~30 nucleotides in length. Sequencing of these fragments offers genome-wide footprints of ribosome–RNA interactions, detecting translated ORFs with sub-codon resolution2,3,4,5,6,7,8. Although Ribo-seq circumnavigates the experimental difficulties of working with protein molecules (for example, using mass spectrometry (MS) analytical tools) and readily finds translations missed by in silico evolutionary methods, it does not demonstrate the actual existence of proteins, and most translations do not show signs of constraint as coding sequences (CDS). A wide range of ‘functional’ scenarios are therefore plausible for Ribo-seq ORFs (Table 1).

Table 1 Approaches to interpreting Ribo-seq ORFs
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Fig. 1: Characterization of a consensus set of Ribo-seq ORFs for annotation by GENCODE.

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Acknowledgements

A.F., J.M.M., F.C. and P.F. are supported by the Wellcome Trust (grant number 108749/Z/15/Z), the National Human Genome Research Institute (NHGRI) of the US National Institutes of Health (NIH) under award number 2U41HG007234 and the European Molecular Biology Laboratory (EMBL). For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Ensembl is a registered trademark of EMBL. M.G. and Y.T.Y. are supported by NHGRI, NIH, under award number 2U41HG007234. I.J. and M.K. are supported by National Human Genome Research Institute of the NIH under award numbers 2U41HG007234 and R01 HG004037. UniProt is supported by the NHGRI, NIH, under award number (U24HG007822), EMBL core funds and the Swiss Federal Government through the State Secretariat for Education, Research and Innovation (SERI). J.R.P. is supported by the Harvard K-12 in Central Nervous System tumors (5K12 CA 90354-18), the Alex’s Lemonade Stand Foundation Young Investigator Award (no. 21-23983) and the Musella Foundation for Brain Tumor Research. T.F.M. is supported by the NIH under award number F32GM123685. M.M.A. acknowledges funding from the Spanish Government grant PGC2018-094091-B-I00 (MCI/AEI/FEDER, EU) and AGAUR grant 2017SGR01020. J.C. is supported by the NIH Pathway to Independence Award (R00 GM134154) and the Cancer Prevent and Research Institute of Texas (RR200095). J.S.W. is supported by the Howard Hughes Medical Institute. A.A.B. is supported by the Stowers Institute for Medical Research and the NIH (R01 GM136849). A.-R.C. is supported by funds provided by the Searle Scholars program, the Sloan Research Fellowship in Computational and Evolutionary Molecular Biology and the National Institute of General Medical Sciences, NIH, award number DP2GM137422. P.V.B. wishes to acknowledge the support from the Investigator in Science Award (grant number 210692/Z/18/Z) by SFI-HRB-Wellcome Trust Biomedical Research Partnership and from Russian Science Foundation (grant number 20-14-00121). N.H. is the recipient of a European Research Council advanced grant under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. AdG788970). N.H. is supported by a grant from the Leducq Foundation (11 CVD-01). The work of the HGNC is funded by the Wellcome Trust (208349/Z/17/Z) and the NHGRI, NIH (under award number U24HG003345). X.R. and M.A.B. are funded by the Canadian Institutes of Health Research, grant PJT-175322. X.R. is funded as a Canada Research Chair in functional proteomics and discovery of novel proteins. N.T.I. is supported by the NIH under award number R01 GM130996. R.L.M. and E.W.D. are supported by NIH grants R01GM087221, R24GM127667, U19AG023122, 1S10OD026936-01 and US National Science Foundation grant DBI-1933311. J.L.A. is supported by the Medical Research Council (MR/N000471/1). M.A.B. is supported by a Junior 1 fellowship from the Fonds de Recherche du Québec–Santé.

Author information

Author notes

  1. These authors contributed equally: Jonathan M. Mudge, Jorge Ruiz-Orera, John R. Prensner, Sebastiaan van Heesch

Authors and Affiliations

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

    Jonathan M. Mudge, Ferriol Calvet, Jose Manuel Gonzalez, Michele Magrane, Elspeth Bruford, Maria Jesus Martin, Paul Flicek & Adam Frankish

  2. Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany

    Jorge Ruiz-Orera, Jana Felicitas Schulz & Norbert Hubner

  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    John R. Prensner, Irwin Jungreis & Manolis Kellis

  4. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    John R. Prensner

  5. Division of Pediatric Hematology/Oncology, Boston Children’s Hospital, Boston, MA, USA

    John R. Prensner

  6. Department of Pediatrics, Medical Genetics Service, Université de Sherbrooke, Sherbrooke, Quebec, Canada

    Marie A. Brunet

  7. MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA

    Irwin Jungreis & Manolis Kellis

  8. Clayton Foundation Laboratories for Peptide Biology, Salk Institute for Biological Studies, La Jolla, CA, USA

    Thomas F. Martinez & Alan Saghatelian

  9. Department of Pharmaceutical Sciences, University of California, Irvine, CA, USA

    Thomas F. Martinez

  10. Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA

    Yucheng T. Yang & Mark Gerstein

  11. Department of Molecular Biophysics & Biochemistry, Yale University, New Haven, CT, USA

    Yucheng T. Yang

  12. Evolutionary Genomics Group, Research Programme on Biomedical Informatics, Hospital del Mar Research Institute (IMIM) and Universitat Pompeu Fabra (UPF), Barcelona, Spain

    M. Mar Albà

  13. Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain

    M. Mar Albà

  14. School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, UK

    Julie L. Aspden

  15. LeedsOmics, University of Leeds, Leeds, UK

    Julie L. Aspden

  16. School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland

    Pavel V. Baranov

  17. Stowers Institute for Medical Research, Kansas City, MO, USA

    Ariel A. Bazzini

  18. Department of Molecular and Integrative Physiology, University of Kansas Medical Center, Kansas City, KS, USA

    Ariel A. Bazzini

  19. Department of Haematology, University of Cambridge School of Clinical Medicine, Cambridge, UK

    Elspeth Bruford

  20. Functional Genomics Centre, Human Technopole, Milan, Italy

    Lorenzo Calviello

  21. Computational Biology Centre, Human Technopole, Milan, Italy

    Lorenzo Calviello

  22. Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    Anne-Ruxandra Carvunis

  23. Pittsburgh Center for Evolutionary Biology and Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    Anne-Ruxandra Carvunis

  24. Department of Pharmacology and Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Jin Chen

  25. Centro Andaluz de Biologia del Desarrollo, CSIC-UPO, Seville, Spain

    Juan Pablo Couso

  26. Institute for Systems Biology, Seattle, WA, USA

    Eric W. Deutsch & Robert L. Moritz

  27. Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA

    Mark Gerstein

  28. Department of Computer Science, Yale University, New Haven, CT, USA

    Mark Gerstein

  29. Department of Statistics & Data Science, Yale University, New Haven, CT, USA

    Mark Gerstein

  30. Charité–Universitätsmedizin, Berlin, Germany

    Norbert Hubner

  31. DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany

    Norbert Hubner

  32. Department of Molecular and Cell Biology and California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA, USA

    Nicholas T. Ingolia

  33. Biobix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Ghent, Belgium

    Gerben Menschaert

  34. Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany

    Uwe Ohler

  35. Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany

    Uwe Ohler

  36. Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany

    Uwe Ohler

  37. Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, Québec, Canada

    Xavier Roucou

  38. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Jonathan S. Weissman

  39. Whitehead Institute for Biomedical Research, Cambridge, MA, USA

    Jonathan S. Weissman

  40. Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA

    Jonathan S. Weissman

  41. Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands

    Sebastiaan van Heesch

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Contributions

J.M.M., J.R.-O., J.R.P. and S.v.H. conceptualized the work and supervised the international collaboration. J.R.-O., J.M.G., M.M., M.J.M., F.C., E.B., E.W.D., R.L.M. and J.M.M. performed data curation. All authors contributed to standardization of the data analysis approach. All authors contributed to discussions on Phase I and II of this effort and continue to provide scientific oversight. A.F., P.F., M.J.M., G.M, Y.T.Y., J.R.P., T.F.M., M.M.A., J.C., J.S.W., A.A.B., A.-R.C., P.V.B., N.H., X.R., M.A.B., N.T.I., E.B., E.W.D. and R.L.M. provided funding. J.M.M., J.R.-O., J.R.P. and S.v.H. wrote the original manuscript draft. All authors reviewed the manuscript and provided edits. All authors approved the final manuscript.

Corresponding authors

Correspondence to Jonathan M. Mudge, Jorge Ruiz-Orera, John R. Prensner or Sebastiaan van Heesch.

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Competing interests

P.V.B. is a co-founder of RiboMaps Ltd., which provides Ribo-seq analysis as a commercial service, including identification of translated ORFs. A.R.C. is a member of the scientific advisory board for Flagship Labs 69, Inc. P.F. is a member of the scientific advisory boards of Fabric Genomics, Inc., and Eagle Genomics, Ltd. The other authors declare no competing interests.

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Nature Biotechnology thanks Sudhakaran Prabakaran, Mathias Uhlén and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Methods, Supplementary Figs. 1–4 and Supplementary Table descriptions

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Mudge, J.M., Ruiz-Orera, J., Prensner, J.R. et al. Standardized annotation of translated open reading frames. Nat Biotechnol 40, 994–999 (2022). https://doi.org/10.1038/s41587-022-01369-0

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