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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
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.
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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Zrimec, J., Buric, F., Kokina, M., Garcia, V., & Zelezniak, A. (2021). Learning the regulatory code of gene expression. Frontiers in Molecular Biosciences, 8(673363).
Zrimec, J. et al. (2021) ‘Learning the regulatory code of gene expression’, Frontiers in Molecular Biosciences, 8(673363). Available at:
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).
Zrimec, Jan, et al. “Learning the Regulatory Code of Gene Expression.” Frontiers in Molecular Biosciences, vol. 8, no. 673363, June 2021,

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