用于 circRNA 上 RBP 结合位点预测的深度学习方法的最新进展

IF 2.4 3区 生物学 Q3 BIOCHEMICAL RESEARCH METHODS
Zhengfeng Wang, Xiujuan Lei, Yuchen Zhang, Fang-Xiang Wu, Yi Pan
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引用次数: 0

摘要

环状 RNA(circRNA)与 RNA 结合蛋白(RBP)之间的相互作用在各种疾病的发生和发展中发挥着重要的生物学作用。CLIP-seq等高通量生物实验方法能有效分析二者之间的相互作用,但生物实验效率低、成本高,且每次只能捕获特定细胞环境中特定RBP与circRNA的结合位点。这些生物实验仍然依赖于下游数据分析来了解许多生物结构和生理过程背后的机制。然而,实验数据维度和生产速度的快速增长给传统分析方法带来了挑战。近年来,深度学习在基因组和转录组方面取得了长足的进步,一些针对 circRNA 上 RBP 结合位点的深度学习预测算法也应运而生。本文简要介绍了circRNA-RBP相互作用相关的生物学背景知识;介绍了该领域相关的深度学习技术,包括circRNA上RBP结合位点预测的问题提出、数据来源、序列编码、深度学习模型和整体流程;深入分析了当前的深度学习方法。最后,讨论了当前研究中存在的一些问题以及未来的研究方向。希望能帮助没有深度学习基础知识或基本生物学背景的研究人员快速理解 circRNA 上的 RBP 结合位点预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recent Progress of Deep Learning Methods for RBP Binding Sites Prediction on circRNA
The interaction between circular RNA (circRNA) and RNA binding protein (RBP) plays an important biological role in the occurrence and development of various diseases. Highthroughput biological experimental methods such as CLIP-seq can effectively analyze the interaction between the two, but biological experiments are inefficient and expensive, and they can only capture binding sites of a specific RBP on circRNA in a selected cell environment at a time. These biological experiments still rely on downstream data analysis to understand the mechanisms behind many biological structures and physiological processes. However, the rapid growth of experimental data dimensions and production speed pose challenges to traditional analysis methods. In recent years, deep learning has made great progress in the genome and transcriptome, and some deep learning prediction algorithms for RBP binding sites on circRNA have also emerged. In this paper, we briefly introduce some biological background knowledge related to circRNA-RBP interaction; present relevant deep learning techniques in this field, including the problem formulation, data source, sequence encoding, deep learning model and overall process of RBP binding sites prediction on circRNA; deeply analyze the current deep learning methods. Finally, some problems existing in the current research and the direction of future research are discussed. It is hoped to help researchers without basic knowledge of deep learning or basic biological background quickly understand the RBP binding sites prediction on circRNA.
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来源期刊
Current Bioinformatics
Current Bioinformatics 生物-生化研究方法
CiteScore
6.60
自引率
2.50%
发文量
77
审稿时长
>12 weeks
期刊介绍: Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth/mini-reviews, research papers and guest edited thematic issues written by leaders in the field, covering a wide range of the integration of biology with computer and information science. The journal focuses on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.
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