Cao Zhou, Li Guanglin
Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest China, College of Life Sciences, Shaanxi Normal University, Xi'an, China.
Front Mol Biosci. 2022 Aug 22;9:791797. doi: 10.3389/fmolb.2022.791797. eCollection 2022.
CircRNAs are formed by a non-canonical splicing method and appear circular in nature. CircRNAs are widely distributed in organisms and have the features of time- and tissue-specific expressions. CircRNAs have attracted increasing interest from scientists because of their non-negligible effects on the growth and development of organisms. The translation capability of circRNAs is a novel and valuable direction in the functional research of circRNAs. To explore the translation potential of circRNAs, some progress has been made in both experimental identification and computational prediction. For computational prediction, both CircCode and CircPro are ribosome profiling-based software applications for predicting translatable circRNAs, and the online databases riboCIRC and TransCirc analyze as many pieces of evidence as possible and list the predicted translatable circRNAs of high confidence. Simultaneously, mass spectrometry in proteomics is often recognized as an efficient method to support the identification of protein and peptide sequences from diverse complex templates. However, few applications fully utilize mass spectrometry to predict translatable circRNAs. Therefore, this research aims to build up a scientific analysis pipeline with two salient features: 1) it starts with the data analysis of raw tandem mass spectrometry data; and 2) it also incorporates other translation evidence such as IRES. The pipeline has been packaged into an analysis tool called mass spectrometry to translatable circRNAs (MStoCIRC). MStoCIRC is mainly implemented by Python3 language programming and could be downloaded from GitHub (https://github.com/QUMU00/mstocirc-master). The tool contains a main program and several small, independent function modules, making it more multifunctional. MStoCIRC can process data efficiently and has obtained hundreds of translatable circRNAs in humans and .
环状RNA（circRNAs）通过一种非经典剪接方式形成，本质上呈环状。circRNAs广泛分布于生物体中，具有时间和组织特异性表达的特征。由于其对生物体生长发育具有不可忽视的影响，circRNAs已引起科学家越来越多的关注。circRNAs的翻译能力是circRNAs功能研究中一个新颖且有价值的方向。为了探索circRNAs的翻译潜力，在实验鉴定和计算预测方面都取得了一些进展。对于计算预测，CircCode和CircPro都是基于核糖体图谱分析的软件应用程序，用于预测可翻译的circRNAs，在线数据库riboCIRC和TransCirc会尽可能多地分析证据，并列出预测的高可信度可翻译circRNAs。同时，蛋白质组学中的质谱分析通常被认为是一种从各种复杂模板中支持鉴定蛋白质和肽序列的有效方法。然而，很少有应用充分利用质谱来预测可翻译的circRNAs。因此，本研究旨在构建一个具有两个显著特点的科学分析流程：1）从原始串联质谱数据的数据分析开始；2）它还纳入了其他翻译证据，如内部核糖体进入位点（IRES）。该流程已被打包成一个名为质谱分析可翻译circRNAs（MStoCIRC）的分析工具。MStoCIRC主要通过Python3语言编程实现，可以从GitHub（https://github.com/QUMU00/mstocirc-master）下载。该工具包含一个主程序和几个小型独立功能模块，使其功能更丰富。MStoCIRC可以高效处理数据，并已在人类中获得了数百个可翻译的circRNAs。
