Accurate annotation of human protein-coding small open reading frames


Functional protein-coding small open reading frames (smORFs) are emerging as an important class of genes. However, the number of translated smORFs in the human genome is unclear because proteogenomic methods are not sensitive enough, and, as we show, Ribo-seq strategies require additional measures to ensure comprehensive and accurate smORF annotation. Here, we integrate de novo transcriptome assembly and Ribo-seq into an improved workflow that overcomes obstacles with previous methods, to more confidently annotate thousands of smORFs. Evolutionary conservation analyses suggest that hundreds of smORF-encoded microproteins are likely functional. Additionally, many smORFs are regulated during fundamental biological processes, such as cell stress. Peptides derived from smORFs are also detectable on human leukocyte antigen complexes, revealing smORFs as a source of antigens. Thus, by including additional validation into our smORF annotation workflow, we accurately identify thousands of unannotated translated smORFs that will provide a rich pool of unexplored, functional human genes.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Outline of top-down smORF annotation workflow.
Fig. 2: Comparison of translation prediction for smORFs versus annotated ORFs.
Fig. 3: smORF regulation during ER stress.
Fig. 4: Characteristics of protein-coding smORFs.
Fig. 5: Protein-coding smORFs identified on unannotated transcripts.
Fig. 6: Microproteins detected in HLA-I complexes.

Data availability

All sequencing datasets generated in this study are available through GEO (GSE125218).

Code availability

A custom java script used for three-frame in silico translation of assembled transcripts is included as Supplementary Data 4.


  1. 1.

    Basrai, M. A., Hieter, P. & Boeke, J. D. Small open reading frames: beautiful needles in the haystack. Genome Res. 7, 768–771 (1997).

  2. 2.

    Ochman, H. Distinguishing the ORFs from the ELFs: short bacterial genes and the annotation of genomes. Trends Genet. 18, 335–337 (2002).

  3. 3.

    Lawrence, J. When ELFs are ORFs, but don’t act like them. Trends Genet. 19, 131–132 (2003).

  4. 4.

    Dujon, B. et al. Complete DNA sequence of yeast chromosome XI. Nature 369, 371–378 (1994).

  5. 5.

    Goffeau, A. et al. Life with 6000 genes. Science 274, 563–567 (1996).

  6. 6.

    Saghatelian, A. & Couso, J. P. Discovery and characterization of smORF-encoded bioactive polypeptides. Nat. Chem. Biol. 11, 909–916 (2015).

  7. 7.

    Couso, J. P. & Patraquim, P. Classification and function of small open reading frames. Nat. Rev. Mol. Cell Biol. 18, 575–589 (2017).

  8. 8.

    Galindo, M. I., Pueyo, J. I., Fouix, S., Bishop, S. A. & Couso, J. P. Peptides encoded by short ORFs control development and define a new eukaryotic gene family. PLoS Biol. 5, e106 (2007).

  9. 9.

    Kondo, T. et al. Small peptide regulators of actin-based cell morphogenesis encoded by a polycistronic mRNA. Nat. Cell Biol. 9, 660–665 (2007).

  10. 10.

    Arnoult, N. et al. Regulation of DNA repair pathway choice in S and G2 phases by the NHEJ inhibitor CYREN. Nature 549, 548–552 (2017).

  11. 11.

    Rathore, A. et al. MIEF1 microprotein regulates mitochondrial translation. Biochemistry 57, 5564–5575 (2018).

  12. 12.

    Stein, C. S. et al. Mitoregulin: a lncRNA-encoded microprotein that supports mitochondrial supercomplexes and respiratory efficiency. Cell Rep. 23, 3710–3720.e8 (2018).

  13. 13.

    D’Lima, N. G. et al. A human microprotein that interacts with the mRNA decapping complex. Nat. Chem. Biol. 13, 174–180 (2017).

  14. 14.

    Zhang, Q. et al. The microprotein Minion controls cell fusion and muscle formation. Nat. Commun. 8, 15664 (2017).

  15. 15.

    Ma, J. et al. Improved identification and analysis of small open reading frame encoded polypeptides. Anal. Chem. 88, 3967–3975 (2016).

  16. 16.

    Slavoff, S. A. et al. Peptidomic discovery of short open reading frame-encoded peptides in human cells. Nat. Chem. Biol. 9, 59–64 (2013).

  17. 17.

    Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. S. & Weissman, J. S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).

  18. 18.

    Aspden, J. L. et al. Extensive translation of small open reading frames revealed by Poly-Ribo-Seq. eLife 3, e03528 (2014).

  19. 19.

    Bazzini, A. A. et al. Identification of small ORFs in vertebrates using ribosome footprinting and evolutionary conservation. EMBO J. 33, 981–993 (2014).

  20. 20.

    Hao, Y. et al. SmProt: a database of small proteins encoded by annotated coding and non-coding RNA loci. Brief. Bioinformatics 19, 636–643 (2018).

  21. 21.

    Olexiouk, V., Van Criekinge, W. & Menschaert, G. An update on a repository of small ORFs identified by ribosome profiling. Nucleic Acids Res. 46, D497–D502 (2018).

  22. 22.

    Ji, Z., Song, R., Regev, A. & Struhl, K. Many lncRNAs, 5′UTRs, and pseudogenes are translated and some are likely to express functional proteins. eLife 4, e08890 (2015).

  23. 23.

    Hsu, P. Y. et al. Super-resolution ribosome profiling reveals unannotated translation events in Arabidopsis. Proc. Natl Acad. Sci. USA 113, E7126–E7135 (2016).

  24. 24.

    Calviello, L. et al. Detecting actively translated open reading frames in ribosome profiling data. Nat. Methods 13, 165–170 (2016).

  25. 25.

    Raj, A. et al. Thousands of novel translated open reading frames in humans inferred by ribosome footprint profiling. eLife 5, e13328 (2016).

  26. 26.

    Diament, A. & Tuller, T. Estimation of ribosome profiling performance and reproducibility at various levels of resolution. Biol. Direct 11, 24 (2016).

  27. 27.

    Robasky, K., Lewis, N. E. & Church, G. M. The role of replicates for error mitigation in next-generation sequencing. Nat. Rev. Genet. 15, 56–62 (2014).

  28. 28.

    Ma, J., Saghatelian, A. & Shokhirev, M. N. The influence of transcript assembly on the proteogenomics discovery of microproteins. PLoS ONE 13, e0194518 (2018).

  29. 29.

    Oslowski, C. M. & Urano, F. Measuring ER stress and the unfolded protein response using mammalian tissue culture system. Methods Enzymol. 490, 71–92 (2011).

  30. 30.

    Liu, C.-L. et al. Genome-wide analysis of tunicamycin-induced endoplasmic reticulum stress response and the protective effect of endoplasmic reticulum inhibitors in neonatal rat cardiomyocytes. Mol. Cell. Biochem. 413, 57–67 (2016).

  31. 31.

    Xu, J. & Zhang, J. Are human translated pseudogenes functional? Mol. Biol. Evol. 33, 755–760 (2016).

  32. 32.

    Gjymishka, A., Su, N. & Kilberg, M. S. Transcriptional induction of the human asparagine synthetase gene during the unfolded protein response does not require the ATF6 and IRE1/XBP1 arms of the pathway. Biochem. J. 417, 695–703 (2009).

  33. 33.

    Andreev, D. E. et al. Translation of 5′ leaders is pervasive in genes resistant to eIF2 repression. eLife 4, e03971 (2015).

  34. 34.

    Sidrauski, C., McGeachy, A. M., Ingolia, N. T. & Walter, P. The small molecule ISRIB reverses the effects of eIF2α phosphorylation on translation and stress granule assembly. eLife 4, e05033 (2015).

  35. 35.

    Xiao, Z., Zou, Q., Liu, Y. & Yang, X. Genome-wide assessment of differential translations with ribosome profiling data. Nat. Commun. 7, 11194 (2016).

  36. 36.

    Guan, B. J. et al. Translational control during endoplasmic reticulum stress beyond phosphorylation of the translation initiation factor eIF2α. J. Biol. Chem. 289, 12593–12611 (2014).

  37. 37.

    Zhao, C., Datta, S., Mandal, P., Xu, S. & Hamilton, T. Stress-sensitive regulation of IFRD1 mRNA decay is mediated by an upstream open reading frame. J. Biol. Chem. 285, 8552–8562 (2010).

  38. 38.

    Sundaram, A., Plumb, R., Appathurai, S. & Mariappan, M. The Sec61 translocon limits IRE1α signaling during the unfolded protein response. eLife 6, e27187 (2017).

  39. 39.

    ENCODE Project Consortium An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  40. 40.

    Chew, G. L., Pauli, A. & Schier, A. F. Conservation of uORF repressiveness and sequence features in mouse, human and zebrafish. Nat. Commun. 7, 11663 (2016).

  41. 41.

    Delcourt, V. et al. The protein coded by a short open reading frame, not by the annotated coding sequence, is the main gene product of the dual-coding gene MIEF1. Mol. Cell. Proteomics 17, 2402–2411 (2018).

  42. 42.

    Brocchieri, L. & Karlin, S. Protein length in eukaryotic and prokaryotic proteomes. Nucleic Acids Res. 33, 3390–3400 (2005).

  43. 43.

    Lin, M. F., Jungreis, I. & Kellis, M. PhyloCSF: a comparative genomics method to distinguish protein coding and non-coding regions. Bioinformatics 27, i275–i282 (2011).

  44. 44.

    Ingolia, N. T., Brar, G. A., Rouskin, S., McGeachy, A. M. & Weissman, J. S. Genome-wide annotation and quantitation of translation by ribosome profiling. Curr. Protoc. Mol. Biol. 103, 4.18.1–4.18.19 (2013).

  45. 45.

    MacLean, J. A. 2nd & Wilkinson, M. F. The Rhox genes. Reproduction 140, 195–213 (2010).

  46. 46.

    Bassani-Sternberg, M., Pletscher-Frankild, S., Jensen, L. J. & Mann, M. Mass spectrometry of human leukocyte antigen class I peptidomes reveals strong effects of protein abundance and turnover on antigen presentation. Mol. Cell. Proteomics 14, 658–673 (2015).

  47. 47.

    Erhard, F. et al. Improved Ribo-seq enables identification of cryptic translation events. Nat. Methods 15, 363–366 (2018).

  48. 48.

    Calviello, L. & Ohler, U. Beyond read-counts: ribo-seq data analysis to understand the functions of the transcriptome. Trends Genet. 33, 728–744 (2017).

  49. 49.

    Cenik, C. et al. Integrative analysis of RNA, translation, and protein levels reveals distinct regulatory variation across humans. Genome Res. 25, 1610–1621 (2015).

  50. 50.

    Gerashchenko, M. V. & Gladyshev, V. N. Ribonuclease selection for ribosome profiling. Nucleic Acids Res. 45, e6 (2017).

  51. 51.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

  52. 52.

    Wang, H., McManus, J. & Kingsford, C. Isoform-level ribosome occupancy estimation guided by transcript abundance with Ribomap. Bioinformatics 32, 1880–1882 (2016).

  53. 53.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

  54. 54.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

  55. 55.

    Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E. L. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305, 567–580 (2001).

  56. 56.

    Marchler-Bauer, A. et al. CDD/SPARCLE: functional classification of proteins via subfamily domain architectures. Nucleic Acids Res. 45, D200–D203 (2017).

  57. 57.

    Xu, T. et al. ProLuCID: an improved SEQUEST-like algorithm with enhanced sensitivity and specificity. J. Proteom. 129, 16–24 (2015).

  58. 58.

    Cociorva, D., Tabb, D. L. & Yates, J. R. Validation of tandem mass spectrometry database search results using DTASelect. Curr. Protoc. Bioinformatics 16, 13.4.1–13.4.14 (2006).

  59. 59.

    Chi, H. et al. Comprehensive identification of peptides in tandem mass spectra using an efficient open search engine. Nat. Biotechnol. 36, 1059–1061 (2018).

  60. 60.

    Kessler, J. H. et al. Competition-based cellular peptide binding assay for HLA class I. Curr. Protoc. Immunol. 61, 18.12.1–18.12.15 (2004).

Download references


We thank the Saghatelian laboratory for helpful comments and suggestions throughout the study, and N. Ingolia for advice on RNase I digestion conditions. We also thank M. Ku, N. Hah and the Salk Institute Next Generation Sequencing Core for preparation of RNA-seq libraries and high-throughput sequencing of Ribo-seq and RNA-seq libraries. This research was supported by NIH/NIGMS (R01 GM102491, A.S.), Leona M. and Harry B. Helmsley Charitable Trust grant (A.S.), Dr Frederick Paulsen Chair/Ferring Pharmaceuticals (A.S.), NIH/NIGMS postdoctoral fellowship (F32 GM123685, T.F.M.), George E. Hewitt Foundation for medical research (Q.C.) and a Pioneer Fellowship (D.T.). This work was also supported by the Razavi Newman Integrative Genomics and Bioinformatics Core and the Next Generation Sequencing Core Facilities of the Salk Institute with funding from the NIH-NCICCSG (P30 014195) and the Chapman Foundation.

Author information

T.F.M. and A.S. conceived the project, designed the experiments and wrote the manuscript. T.F.M. performed cell culture and prepared RPFs and total RNA. T.F.M. and C.D. prepared Ribo-seq libraries. T.F.M. analyzed Ribo-seq and RNA-seq data, developed the smORF annotation workflow and wrote custom scripts to generate Ribo-seq plots. M.N.S. performed de novo transcriptome assembly and generated ORF databases. Q.C. performed HLA-I experiments. T.F.M. and D.T. analyzed HLA-I proteomics data. All authors discussed the results and edited the manuscript. A.S. supervised the study.

Correspondence to Thomas F. Martinez or Alan Saghatelian.

Ethics declarations

Competing interests

The authors declare no competing interests.

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. 1–14.

Reporting Summary

Supplementary Data 1

List of protein-coding smORFs identified in this study and their properties.

Supplementary Data 2

List of significantly regulated ER stress smORFs and annotated genes.

Supplementary Data 3

List of smORFs containing conserved protein domains and predicted transmembrane helices, as well as smORFs encoding peptides identified in HLA-I proteomics datasets.

Supplementary Data 4

Custom java script used to generate a three-frame ORF database from transcriptome assembly in gtf format.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Martinez, T.F., Chu, Q., Donaldson, C. et al. Accurate annotation of human protein-coding small open reading frames. Nat Chem Biol (2019).

Download citation