CoChat
Sign Up
PreprintCited by 26RNA and protein synthesis mechanismsGenomics and Phylogenetic StudiesMachine Learning in Bioinformatics

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

TIS Transformer: Remapping the Human Proteome Using Deep Learning

Open access
Clauwaert, Jim, Gupta, Ramneek, McVey, Zahra, Menschaert, Gerben·2021·NAR Genomics and Bioinformatics
DOI 10.1101/2021.11.18.468957PMC PMC9985340

Preprint — published as 10.1093/nargab/lqad021 · Paper page

Open this paper in CoChat — free accountOpen PDF

Abstract

A bstract: The correct mapping of the proteome is an important step towards advancing our understanding of biological systems and cellular mechanisms. Methods that provide better mappings can fuel important processes such as drug discovery and disease understanding. Currently, true determination of translation initiation sites is primarily achieved by in vivo experiments. Here we propose TIS Transformer, a deep learning model for the determination of translation start sites solely utilizing the information embedded in the transcript nucleotide sequence. The method is built upon deep learning techniques first designed for natural language processing. We prove this approach to be best suited for learning the semantics of translation, outperforming previous approaches by a large margin. We demonstrate that limitations in the model performance is primarily due to the presence of low quality annotations against which the model is evaluated against. Advantages of the method are its ability to detect key features of the translation process and multiple coding sequences on a transcript. These include micropeptides encoded by short Open Reading Frames, either alongside a canonical coding sequence or within long non-coding RNAs. To demonstrate the use of our methods, we applied TIS Transformer to remap the full human proteome.

Overview

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 · 10.1101/2021.11.18.468957

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

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.

Citations

Cites

Flagged first, then by citations

Show all 1 references

Research works printed but not linked: 48

  1. Aken BL et al. 2016. The Ensembl Gene Annotation System. Database. 2016: · DOI · Paper page
  2. 2013, Translational Repression of the McKusickKaufman Syndrome Transcript by Unique Upstream Open Reading Frames Encoding Mitochondrial Proteins with Alternative Polyadenylation Sites, Biochimica et Biophysica Acta (BBA) - General Subjects, 1830, 2728, 10.1016/j.bbagen.2012.12.010 · DOI
  3. 10.1038/nrg3520 · DOI
  4. 10.1016/j.ab.2014.06.022 · DOI
  5. Cheng J , Dong L , and Lapata M. 2016. Long Short-Term Memory-Networks for Machine Reading. arXiv:1601.06733 [cs]. · DOI
  6. Choromanski K et al. 2021. Rethinking Attention with Performers. arXiv:2009.14794 [cs, stat]. · arXiv
  7. 10.1038/369371a0 · DOI
  8. 10.1016/j.molcel.2015.11.013 · DOI
  9. 2006, The Abundance of Short Proteins in the Mammalian Proteome, PLOS Genetics, 2, e52, 10.1371/journal.pgen.0020052 · DOI
  10. 10.1016/j.heliyon.2020.e04825 · DOI
  11. Jorgensen RA and Dorantes-Acosta AE. 2012. Conserved Peptide Upstream Open Reading Frames Are Associated with Regulatory Genes in Angiosperms. Frontiers in Plant Science. 0: · DOI
  12. 10.1016/j.compbiomed.2015.09.010 · DOI
  13. 2019, DeepGSR: An Optimized Deep-Learning Structure for the Recognition of Genomic Signals and Regions, Bioinformatics, 35, 1125, 10.1093/bioinformatics/bty752 · DOI
  14. 2018, MOXI Is a Mitochondrial Micropeptide That Enhances Fatty Acid β-Oxidation, Cell Reports, 23, 3701, 10.1016/j.celrep.2018.05.058 · DOI
  15. 10.1038/nature21034 · DOI
  16. 10.1038/s41587-022-01369-0 · DOI
  17. Mudge JM , Ruiz-Orera J , Prensner JR , Brunet MA , Gonzalez JM , et al. 2021. A Community-Driven Roadmap to Advance Research on Translated Open Reading Frames Detected by Ribo-Seq. bioRxiv. 2021.06.10.447896. · DOI
  18. Parikh A , Täckström O , Das D , and Uszkoreit J 2016. A Decomposable Attention Model for Natural Language Inference. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Austin, Texas: Association for Computational Linguistics, pp. 2249–2255. · DOI
  19. 10.1002/bies.201400103 · DOI
  20. 2021, Most Non-Canonical Proteins Uniquely Populate the Proteome or Immunopeptidome, Cell Reports, 34, 108815, 10.1016/j.celrep.2021.108815 · DOI
  21. 10.1093/bioinformatics/btm177 · DOI
  22. 2018, Mitoregulin: A lncRNA-Encoded Microprotein That Supports Mitochondrial Supercomplexes and Respiratory Efficiency, Cell Reports, 23, 3710, 10.1016/j.celrep.2018.06.002 · DOI
  23. Sundararajan M , Taly A , and Yan Q. 2017. Axiomatic Attribution for Deep Networks. arXiv:1703.01365 [cs]. · arXiv
  24. 10.2527/jas2016.94supplement4184x · DOI · Paper page
  25. 2017, Attention Is All You Need, Advances in Neural Information Processing Systems, 30, 5998
  26. 2021, The Role of Micropeptides in Biology, Cellular and Molecular Life Sciences, 78, 3285, 10.1007/s00018-020-03740-3 · DOI
  27. Wang S , Li BZ , Khabsa M , Fang H , and Ma H. 2020. Linformer: Self-Attention with Linear Complexity. arXiv:2006.04768 [cs, stat]. · arXiv
  28. 10.1016/j.dsp.2021.103202 · DOI
  29. 10.1016/S0968-0004(03)00051-3 · DOI
  30. Xiong Y , Zeng Z , Chakraborty R , Tan M , Fung G , Li Y , and Singh V. 2021. Nystr\”omformer: A Nystr\”om-Based Algorithm for Approximating Self-Attention. arXiv:2102.03902 [cs]. · DOI · Paper page
  31. 10.1093/nar/gkv1157 · DOI
  32. 2020, Emerging Role of Long Noncoding RNA-Encoded Micropeptides in Cancer, Cancer Cell International, 20, 506, 10.1186/s12935-020-01589-x · DOI
  33. 10.1074/jbc.R116.733899 · DOI
  34. 2020, Advances in Neural Information Processing Systems, 33, 17283
  35. 10.1093/bioinformatics/btx247 · DOI
  36. 10.1093/bioinformatics/16.9.799 · DOI
  37. 10.1504/IJDMB.2018.094781 · DOI
  38. 10.1016/j.ab.2014.06.022 · DOI
  39. K. Choromanski , V. Likhosherstov , D. Dohan , X. Song , A. Gane , T. Sarlos , P. Hawkins , J. Davis , A. Mo-hiuddin , L. Kaiser , D. Belanger , L. Colwell , and A. Weller . Rethinking Attention with Performers. arXiv:2009.14794 [cs, stat], Mar. 2021. · arXiv
  40. 10.1016/j.heliyon.2020.e04825 · DOI
  41. 10.1016/j.compbiomed.2015.09.010 · DOI
  42. 10.1093/bioinformatics/bty752 · DOI
  43. 10.1038/s41587-022-01369-0 · DOI
  44. 10.1093/bioinformatics/btm177 · DOI
  45. 10.1016/j.dsp.2021.103202 · DOI
  46. 10.1093/bioinformatics/btx247 · DOI
  47. 10.1093/bioinformatics/16.9.799 · DOI
  48. 10.1504/IJDMB.2018.094781 · DOI

Metadata

Authors
Clauwaert, J., Gupta, R., McVey, Z., & Menschaert, G.
Affiliations
  • Ghent University (BE)
  • Novo Nordisk (United Kingdom) (GB)
Corresponding author
  • Clauwaert, Jim
  • Menschaert, Gerben
ORCID
Venue
NAR Genomics and Bioinformatics
Publisher
openRxiv
Published
2021-11-19
Volume
5
Issue
1
Pages
lqad021-lqad021
ISSN
2631-9268 (ISSN-L)
DOI
10.1101/2021.11.18.468957
PMC
PMC9985340
OpenAlex
W4323076266
Cited by
26
References
1
Citations by year
2026: 8 · 2025: 6 · 2024: 3 · 2023: 9
FWCI
2.32
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • NAR Genomics and Bioinformatics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Ghent University Academic Bibliography (Ghent University) (publishedVersion)
  • Ghent University Academic Bibliography (Ghent University) (submittedVersion, cc-by)
Funders
Universiteit Gent
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
translation initiation sites, transformer models, transcript sequences, micropeptides, open reading frames, long non-coding RNA, alternative translation initiation, annotation quality

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