AUTHOR=Cao Zhou , Li Guanglin 

TITLE=MStoCIRC: A powerful tool for downstream analysis of MS/MS data to predict translatable circRNAs

JOURNAL=Frontiers in Molecular Biosciences

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2022.791797

DOI=10.3389/fmolb.2022.791797

ISSN=2296-889X

ABSTRACT=CircRNAs are formed by non-canonical splicing method and appear circular in nature. CircRNAs are widely distributed in organisms and have the features of time- and tissue-specific expression. CircRNAs have attracted increasing interests from scientists because of their non-negligible effects on the growth and development of organisms. Translation capability of circRNAs is a novel and valuable direction in the function research of circRNAs. To explore the translation potential of circRNAs, some progress has been made in both of experimental identification and computational prediction. For computational prediction, both CircCode and circPro are ribosome profiling-based software for predicting translatable circRNAs, and the online databases riboCIRC and TransCirc analyze as multiple 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 data analysis of raw tandem mass spectrometry data; (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 human and Arabidopsis thaliana.