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Open AccessArticle

Docker4Circ: A Framework for the Reproducible Characterization of circRNAs from RNA-Seq Data

Department of Computer Science, University of Turin, 10149 Turin, Italy
Department of Clinical and Biological Sciences, University of Turin, Orbassano, 10043 Turin, Italy
Center for Genomic Science, Italian Institute of Technology, 20139 Milan, Italy
Division of Cellular and Molecular Pathology, Department of Pathology, University of Cambridge, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
Department of Molecular Biotechnology and Health Sciences, University of Turin, 10126 Turin, Italy
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Int. J. Mol. Sci. 2020, 21(1), 293;
Received: 11 December 2019 / Accepted: 28 December 2019 / Published: 31 December 2019
(This article belongs to the Special Issue Non-Coding RNA Biogenesis and Function)
Recent improvements in cost-effectiveness of high-throughput technologies has allowed RNA sequencing of total transcriptomes suitable for evaluating the expression and regulation of circRNAs, a relatively novel class of transcript isoforms with suggested roles in transcriptional and post-transcriptional gene expression regulation, as well as their possible use as biomarkers, due to their deregulation in various human diseases. A limited number of integrated workflows exists for prediction, characterization, and differential expression analysis of circRNAs, none of them complying with computational reproducibility requirements. We developed Docker4Circ for the complete analysis of circRNAs from RNA-Seq data. Docker4Circ runs a comprehensive analysis of circRNAs in human and model organisms, including: circRNAs prediction; classification and annotation using six public databases; back-splice sequence reconstruction; internal alternative splicing of circularizing exons; alignment-free circRNAs quantification from RNA-Seq reads; and differential expression analysis. Docker4Circ makes circRNAs analysis easier and more accessible thanks to: (i) its R interface; (ii) encapsulation of computational tasks into docker images; (iii) user-friendly Java GUI Interface availability; and (iv) no need of advanced bash scripting skills for correct use. Furthermore, Docker4Circ ensures a reproducible analysis since all its tasks are embedded into a docker image following the guidelines provided by Reproducible Bioinformatics Project. View Full-Text
Keywords: circRNA; reproducible analysis; pipeline; docker images circRNA; reproducible analysis; pipeline; docker images
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Ferrero, G.; Licheri, N.; Coscujuela Tarrero, L.; De Intinis, C.; Miano, V.; Calogero, R.A.; Cordero, F.; De Bortoli, M.; Beccuti, M. Docker4Circ: A Framework for the Reproducible Characterization of circRNAs from RNA-Seq Data. Int. J. Mol. Sci. 2020, 21, 293.

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