MicroRNAs (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.

中文翻译：

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使用 transformer 和 base-pair 模式推进 microRNA 靶位点预测


MicroRNA （miRNA） 是参与各种细胞过程的短非编码 RNA，在基因调控中起着至关重要的作用。识别 miRNA 靶标仍然是一个核心挑战，对于阐明复杂的基因调控网络至关重要。传统的计算方法主要集中在通过种子区域（称为经典位点）内的完美 Watson-Crick 碱基配对来识别 miRNA 靶标。然而，新出现的证据表明，完美的种子匹配并不是 miRNA 介导的调节的先决条件，这强调了识别不完美或非经典位点的重要性。为了应对这一挑战，我们提出了 Mimosa，这是一种新的计算方法，它采用 Transformer 框架来增强 miRNA 靶标的预测。Mimosa 通过整合上下文、位置和碱基配对信息来捕获深入的属性，从而提高其预测能力，从而脱颖而出。其识别非经典碱基配对模式的独特能力使 Mimosa 成为出色的模型，减少了对预选候选靶标的依赖。Mimosa 在基因水平预测方面取得了卓越的性能，并且通过广泛的基准测试，在各种非人类物种的位点水平预测中也显示出令人印象深刻的表现。为了促进 miRNA 靶向的研究工作，我们开发了一个易于使用的 Web 服务器，用于全面的端到端预测，该服务器可在 http://monash.bioweb.cloud.edu.au/Mimosa 上公开获得。