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Explainable Transformer Models for Functional Genomics in Prokaryotes

View ORCID ProfileJim Clauwaert, View ORCID ProfileGerben Menschaert, View ORCID ProfileWillem Waegeman
doi: https://doi.org/10.1101/2020.03.16.993501
Jim Clauwaert
Ghent University
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  • For correspondence: jim.clauwaert@ugent.be
Gerben Menschaert
Ghent University
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Willem Waegeman
Ghent University
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Abstract

The effectiveness of deep learning methods can be largely attributed to the automated extraction of relevant features from raw data. In the field of functional genomics, this generally comprises the automatic selection of relevant nucleotide motifs from DNA sequences. To benefit from automated learning methods, new strategies are required that unveil the decision-making process of trained models. In this paper, we present several methods that can be used to gather insights on biological processes that drive any genome annotation task. This work builds upon a transformer-based neural network framework designed for prokaryotic genome annotation purposes. We find that the majority of sub-units (attention heads) of the model are specialized towards identifying DNA binding sites. Working with a neural network trained to detect transcription start sites in E. coli, we successfully characterize both locations and consensus sequences of transcription factor binding sites, including both well-known and potentially novel elements involved in the initiation of the transcription process.

Competing Interest Statement

The authors have declared no competing interest.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted September 30, 2020.
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Explainable Transformer Models for Functional Genomics in Prokaryotes
Jim Clauwaert, Gerben Menschaert, Willem Waegeman
bioRxiv 2020.03.16.993501; doi: https://doi.org/10.1101/2020.03.16.993501
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Explainable Transformer Models for Functional Genomics in Prokaryotes
Jim Clauwaert, Gerben Menschaert, Willem Waegeman
bioRxiv 2020.03.16.993501; doi: https://doi.org/10.1101/2020.03.16.993501

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