Liu Li, Han Kaiyuan, Sun Huimin, Han Lu, Gao Dong, Xi Qilemuge, Zhang Lirong, Lin Hao
Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China.
Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Brief Bioinform. 2023 Mar 19;24(2). doi: 10.1093/bib/bbad072.
Precisely calling chromatin loops has profound implications for further analysis of gene regulation and disease mechanisms. Technological advances in chromatin conformation capture (3C) assays make it possible to identify chromatin loops in the genome. However, a variety of experimental protocols have resulted in different levels of biases, which require distinct methods to call true loops from the background. Although many bioinformatics tools have been developed to address this problem, there is still a lack of special introduction to loop-calling algorithms. This review provides an overview of the loop-calling tools for various 3C-based techniques. We first discuss the background biases produced by different experimental techniques and the denoising algorithms. Then, the completeness and priority of each tool are categorized and summarized according to the data source of application. The summary of these works can help researchers select the most appropriate method to call loops and further perform downstream analysis. In addition, this survey is also useful for bioinformatics scientists aiming to develop new loop-calling algorithms.
精确识别染色质环对进一步分析基因调控和疾病机制具有深远意义。染色质构象捕获（3C）分析技术的进步使得在基因组中识别染色质环成为可能。然而，各种实验方案导致了不同程度的偏差，这需要不同的方法从背景中识别真正的环。尽管已经开发了许多生物信息学工具来解决这个问题，但仍然缺乏对环识别算法的专门介绍。本综述概述了基于各种3C技术的环识别工具。我们首先讨论不同实验技术产生的背景偏差和去噪算法。然后，根据应用的数据源对每个工具的完整性和优先级进行分类和总结。这些工作的总结可以帮助研究人员选择最合适的方法来识别环并进一步进行下游分析。此外，这项调查对旨在开发新的环识别算法的生物信息学科学家也很有用。
