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https://doi.org/10.21256/zhaw-24882| Publication type: | Article in scientific journal |
| Type of review: | Peer review (publication) |
| Title: | Learning the regulatory code of gene expression |
| Authors: | Zrimec, Jan Buric, Filip Kokina, Mariia Garcia, Victor Zelezniak, Aleksej |
| et. al: | No |
| DOI: | 10.3389/fmolb.2021.673363 10.21256/zhaw-24882 |
| Published in: | Frontiers in Molecular Biosciences |
| Volume(Issue): | 8 |
| Issue: | 673363 |
| Issue Date: | Jun-2021 |
| Publisher / Ed. Institution: | Frontiers Research Foundation |
| ISSN: | 2296-889X |
| Language: | English |
| Subjects: | Chromatin accessibility; Cis-regulatory grammar; Deep neural network; Gene expression prediction; Gene regulatory structure; mRNA & protein abundance; Machine learning; Regulatory genomics |
| Subject (DDC): | 006: Special computer methods 572: Biochemistry |
| Abstract: | Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/24882 |
| Fulltext version: | Published version |
| License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
| Departement: | Life Sciences and Facility Management |
| Organisational Unit: | Institute of Computational Life Sciences (ICLS) |
| Published as part of the ZHAW project: | Digitale Werkzeuge zur Codonoptimierung |
| Appears in collections: | Publikationen Life Sciences und Facility Management |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| 2021_Zrimec-etal_Regulatory-code-gene-expression.pdf | 2.25 MB | Adobe PDF |  View/Open |
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Zrimec, J., Buric, F., Kokina, M., Garcia, V., & Zelezniak, A. (2021). Learning the regulatory code of gene expression. Frontiers in Molecular Biosciences, 8(673363). https://doi.org/10.3389/fmolb.2021.673363
Zrimec, J. et al. (2021) ‘Learning the regulatory code of gene expression’, Frontiers in Molecular Biosciences, 8(673363). Available at: https://doi.org/10.3389/fmolb.2021.673363.
J. Zrimec, F. Buric, M. Kokina, V. Garcia, and A. Zelezniak, “Learning the regulatory code of gene expression,” Frontiers in Molecular Biosciences, vol. 8, no. 673363, Jun. 2021, doi: 10.3389/fmolb.2021.673363.
ZRIMEC, Jan, Filip BURIC, Mariia KOKINA, Victor GARCIA und Aleksej ZELEZNIAK, 2021. Learning the regulatory code of gene expression. Frontiers in Molecular Biosciences. Juni 2021. Bd. 8, Nr. 673363. DOI 10.3389/fmolb.2021.673363
Zrimec, Jan, Filip Buric, Mariia Kokina, Victor Garcia, and Aleksej Zelezniak. 2021. “Learning the Regulatory Code of Gene Expression.” Frontiers in Molecular Biosciences 8 (673363). https://doi.org/10.3389/fmolb.2021.673363.
Zrimec, Jan, et al. “Learning the Regulatory Code of Gene Expression.” Frontiers in Molecular Biosciences, vol. 8, no. 673363, June 2021, https://doi.org/10.3389/fmolb.2021.673363.
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