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CRMnet: a deep learning model for predicting gene expression from large regulatory sequence datasets

Ke Ding, Gunjan Dixit, View ORCID ProfileBrian J. Parker, View ORCID ProfileJiayu Wen
doi: https://doi.org/10.1101/2022.12.02.518786
Ke Ding
1Division of Genome Science and Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
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Gunjan Dixit
1Division of Genome Science and Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
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Brian J. Parker
2School of Computing and Biological Data Science Institute, Australian National University, Canberra, ACT, Australia
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  • For correspondence: Brian.Parker@anu.edu.au Jiayu.Wen@anu.edu.au
Jiayu Wen
1Division of Genome Science and Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
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  • For correspondence: Brian.Parker@anu.edu.au Jiayu.Wen@anu.edu.au
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ABSTRACT

Recent large datasets measuring the gene expression of millions of possible gene promoter sequences provide a resource to design and train optimised deep neural network architectures to predict expression from sequences. High predictive performance due to the modelling of dependencies within and between regulatory sequences is an enabler for biological discoveries in gene regulation through model interpretation techniques.

To understand the regulatory code that delineates gene expression, we have designed a novel deep-learning model (CRMnet) to predict gene expression in Saccharomyces cerevisiae. Our model outperforms the current benchmark models and achieves a Pearson correlation coefficient of 0.971. Interpretation of informative genomic regions determined from model saliency maps, and overlapping the saliency maps with known yeast motifs, support that our model can successfully locate the binding sites of transcription factors that actively modulate gene expression. We compare our model’s training times on a large compute cluster with GPUs and Google TPUs to indicate practical training times on similar datasets.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 02, 2022.
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CRMnet: a deep learning model for predicting gene expression from large regulatory sequence datasets
Ke Ding, Gunjan Dixit, Brian J. Parker, Jiayu Wen
bioRxiv 2022.12.02.518786; doi: https://doi.org/10.1101/2022.12.02.518786
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CRMnet: a deep learning model for predicting gene expression from large regulatory sequence datasets
Ke Ding, Gunjan Dixit, Brian J. Parker, Jiayu Wen
bioRxiv 2022.12.02.518786; doi: https://doi.org/10.1101/2022.12.02.518786

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