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TIS Transformer: remapping the human proteome using deep learning
Open accessPublished version of a preprint
Abstract
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 are 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.
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Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Linformer: Self-Attention with Linear Complexity
Wang, S., Li, B. Z., Khabsa, M., Fang, H., & Ma, H. (2020) · arXiv · doi:10.48550/arxiv.2006.04768 · Cited by 867
Rethinking Attention with Performers
Choromanski, K., Likhosherstov, V., Dohan, D., Song, X., Gane, A., Sarlos, T., Hawkins, P., Davis, J., Mohiuddin, A., Kaiser, L., et al. (2020) · arXiv · doi:10.48550/arxiv.2009.14794 · Cited by 695 · cited 3×
“To allow long-range attention over full transcripts, we used a recent innovation in calculating full attention introduced by Choromanski et al. (20).”
BackgroundMethodNyströmformer: A Nyström-based Algorithm for Approximating Self-Attention
Xiong, Y., Zeng, Z., Chakraborty, R., Tan, M., Fung, G., Li, Y., & Singh, V. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i16.17664 · Cited by 376
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Metadata
- Authors
- Clauwaert, J., McVey, Z., Gupta, R., & Menschaert, G.
- Affiliations
- Ghent University (BE)
- Novo Nordisk (United Kingdom) (GB)
- Corresponding author
- Clauwaert, Jim
- Menschaert, Gerben
- Venue
- NAR Genomics and Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-01-10
- Volume
- 5
- Issue
- 1
- Pages
- lqad021-lqad021
- ISSN
- 2631-9268 (ISSN-L)
- PMC
- PMC9985340
- OpenAlex
- W4323076266
- Cited by
- 26
- References
- 4
- Citations by year
- 2026: 8 · 2025: 6 · 2024: 3 · 2023: 9
- FWCI
- 2.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- 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
- Novo Nordisk Research Centre Oxford Ltd
- Ghent University
- Novo Nordisk employees
- 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
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