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2018/1 Our review "Computational strategies for exploring circular RNAs" was published on Trends in Genetics


http://www.cell.com/trends/genetics/fulltext/S0168-9525(17)30236-6

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.

    该论文全面阐述了环形RNA研究和数据挖掘中诸多方法,探讨了相关方法在非编码RNA数据挖掘中的适用条件与优劣评估,并指出未来环形RNA数据挖掘的发展趋势与挑战。

    环形RNA是近年来获得广泛关注的一类结构呈闭合环形的RNA分子,并入选Clarivate Analytics 2017年度热点前沿领域。环形RNA的基因来源、内部组成、细胞定位、生成机制与生物功能均较为多样,通过高通量测序数据的挖掘对其深入研究成为该领域的必经途径。依据参考基因组的使用策略,现有的识别算法可划分为基于分段比对(split-alignment based)和基于伪参考序列构建(pseudo-reference based)两大类。由于所借助比对算法类型的不同,各识别算法又分别针对剪切型(splice-aware)和全能型(versatile)比对算法进行优化。此外,在向后剪接读段(back-spliced junction read)的检测和配对末端比对信息的筛选上,这些识别算法采用的策略也不尽相同。以上关键步骤极大影响了识别算法在不同转录组测序数据上的表现,目前现有的十余种环形RNA识别算法在敏感度、可靠性和适用范围上均有显著差别。

    在环形RNA数据的挖掘算法上,我们有多篇研究成果发表在Nature CommunicationsGenome BiologyBriefings in Bioinformatics等国际知名学术期刊上,其中环形RNA的识别算法(CIRI)两年多来的引用次数已超过120次。论文第一作者高远在攻读博士期间获得中科院院长优秀奖(2017)和中国科学院优秀博士论文(2017),现在美国University of Pennsylvania的Perelman School of Medicine进行博士后研究工作。上述研究获得了国家自然科学基金委重大研究计划项目、优秀青年基金项目和中国科学院的经费资助。 


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