ArticleFrontiers in molecular biosciences2022
MStoCIRC: A powerful tool for downstream analysis of MS/MS data to predict translatable circRNAs.
Article in Frontiers in molecular biosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Decoding the functional network of circular RNAs encoding proteins in hepatocellular carcinoma: from carcinogenesis to clinical transformation.Journal of advanced research · 2026Review
- Decoding circRNA translation: challenges and advances in computational method development.Frontiers in genetics · 2025Review
- The Internal Ribosome Entry Site in Circular RNAs.Advances in experimental medicine and biology · 2025Review
- Translation of Circular RNAs: Functions of Translated Products and Related Bioinformatics Approaches.Current bioinformatics · 2024Article
- CFCPred: Advancing circRNAs-Encoded Peptides Prediction Through Cluster Purity-Guided Resampling and Fuzzy Voting.IET systems biologyArticle
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
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Registered trials
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