Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Laboratory of Computational genomics, Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China.
Trends Genet. 2018 May;34(5):389-400. doi: 10.1016/j.tig.2017.12.016. Epub 2018 Jan 12.
Recent studies have demonstrated that circular RNAs (circRNAs) are ubiquitous and have diverse functions and mechanisms of biogenesis. In these studies, computational profiling of circRNAs has been prevalently used as an indispensable method to provide high-throughput approaches to detect and analyze circRNAs. However, without an overall understanding of the underlying strategies, these computational methods may not be appropriately selected or used for a specific research purpose, and some misconceptions may result in biases in the analyses. In this review we attempt to illustrate the key steps and summarize tradeoff of different strategies, covering all popular algorithms for circRNA detection and various downstream analyses. We also clarify some common misconceptions and put emphasis on the fields of application for these computational methods.
最近的研究表明，环状 RNA（circRNA）是普遍存在的，具有多种功能和生物发生机制。在这些研究中，环状 RNA 的计算分析已被广泛用作一种不可或缺的方法，提供高通量的方法来检测和分析环状 RNA。然而，如果没有对潜在策略的全面了解，这些计算方法可能无法为特定的研究目的选择或使用，一些误解可能导致分析中的偏差。在这篇综述中，我们试图说明关键步骤，并总结不同策略的权衡，涵盖了用于 circRNA 检测的所有流行算法和各种下游分析。我们还澄清了一些常见的误解，并强调了这些计算方法的应用领域。
