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bioRxiv · 10.1101/2022.12.22.521582

LegNet: resetting the bar in deep learning for accurate prediction of promoter activity and variant effects from massive parallel reporter assays

Penzar, D.·Nogina, D.·Meshcheryakov, G.·Lando, A.·Rafi, A. M.·de Boer, C.·Zinkevich, A.·Kulakovskiy, I. V.
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Abstract

MotivationThe increasing volume of data from high-throughput experiments including parallel reporter assays facilitates the development of complex deep learning approaches for DNA regulatory grammar. ResultsHere we introduce LegNet, an EfficientNetV2-inspired convolutional network for modeling short gene regulatory regions. By approaching the sequence-to-expression regression problem as a soft classification task, LegNet secured first place for the autosome.org team in the DREAM 2022 challenge of predicting gene expression from gigantic parallel reporter assays. Using published data, here we demonstrate that LegNet outperforms existing models and accurately predicts gene expression per se as well as the effects of single-nucleotide variants. Furthermore, we show how LegNet can be used in a diffusion network manner for the rational design of promoter sequences yielding the desired expression level. Availability and Implementationhttps://github.com/autosome-ru/LegNet. The GitHub repository includes the Python code under the MIT license to reproduce the results presented in the study and a Jupyter Notebook tutorial. Supplementary InformationOnline-only supplementary data are available at Bioinformatics online. Contactdmitrypenzar1996@gmail.com, ivan.kulakovskiy@gmail.com

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Penzar, D., Nogina, D., Meshcheryakov, G., Lando, A., Rafi, A. M., de Boer, C., Zinkevich, A., Kulakovskiy, I. V.. 2022-12-23. LegNet: resetting the bar in deep learning for accurate prediction of promoter activity and variant effects from massive parallel reporter assays. https://doi.org/10.1101/2022.12.22.521582

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