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bioRxiv · 10.1101/2024.05.05.592612

A deep learning-based model integrating base-pairing patterns improves microRNA target site prediction

Bi, Y.·Li, F.·Wang, C.·Pan, T.·Davidovich, C.·Webb, G. I.·Song, J.
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Abstract

Micro RNAs (miRNAs) are short non-coding RNAs involved in various cellular processes, playing a crucial role in gene regulation. Identifying miRNA targets remains a central challenge and is pivotal for elucidating the complex gene regulatory networks. Traditional computational approaches have predominantly focused on identifying miRNA targets through perfect Watson-Crick base pairings within the seed region, referred to as canonical sites. However, emerging evidence suggests that perfect seed matches are not a prerequisite for miRNA-mediated regulation, underscoring the importance of also recognizing imperfect, or non-canonical, sites. To address this challenge, we propose Mimosa, a new computational approach that employs the Transformer framework to enhance the prediction of miRNA targets. Mimosa distinguishes itself by integrating contextual, positional, and base-pairing information to capture in-depth attributes, thereby improving its predictive capabilities. Its unique ability to identify non-canonical base-pairing patterns makes Mimosa a standout model, reducing the reliance on pre-selecting candidate targets. Mimosa achieves superior performance in gene-level predictions and also shows impressive performance in site-level predictions across various non-human species through extensive benchmarking tests. To facilitate research efforts in miRNA targeting, we have developed an easy-to-use web server for comprehensive end-to-end predictions, which is publicly available at http://monash.bioweb.cloud.edu.au/Mimosa/.

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Bi, Y., Li, F., Wang, C., Pan, T., Davidovich, C., Webb, G. I., Song, J.. 2024-05-07. A deep learning-based model integrating base-pairing patterns improves microRNA target site prediction. https://doi.org/10.1101/2024.05.05.592612

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