Preprint Article Version 1 This version is not peer-reviewed

Docker4Circ: A Framework for a Reproducible Characterization of CircRNAs from RNA-Seq Data

Version 1 : Received: 15 July 2019 / Approved: 19 July 2019 / Online: 19 July 2019 (05:05:35 CEST)

How to cite: 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 a Reproducible Characterization of CircRNAs from RNA-Seq Data. Preprints 2019, 2019070219 (doi: 10.20944/preprints201907.0219.v1). 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 a Reproducible Characterization of CircRNAs from RNA-Seq Data. Preprints 2019, 2019070219 (doi: 10.20944/preprints201907.0219.v1).

Abstract

Recently the increased cost-effectiveness of high-throughput technologies has made available a large number of RNA sequencing datasets to identify circular RNAs (circRNAs). However, despite many computational tools were developed to predict circRNAs, a limited number of workflows exists to predict and to characterize circRNAs. Moreover, to the best of our knowledge, these available workflows do not ensure computational reproducibility and require advanced bash scripting skills to be correctly installed and used. To cope with these critical aspects we present Docker4Circ, a new computational framework designed for a comprehensive analysis of circRNAs composed of: circRNAs prediction, classification and annotation using public databases, the back-splicing sequence reconstruction; the internal alternative splicing of circularizing exons; the alignment-free circRNAs quantification from RNA-Seq reads, and, finally, their differential expression analysis. Docker4Circ was specifically designed for making easier and more accessible circRNAs analysis thanks to the following features: (i) its R interface; (ii) the encapsulation of its computational tasks into a docker image; (iii) an available user-friendly Java GUI Interface. Furthermore, Docker4Circ ensures a reproducible analysis because all its tasks were embedded into a docker image following the guidelines provided by Reproducible Bioinformatics Project (RBP, http://reproducible-bioinformatics.org/). The effectiveness of Docker4Circ was demonstrated on a real case study whose goal is to characterize the circRNAs predicted in colorectal cancer cell lines and quantified in public RNA-Seq experiments performed on primary tumor tissues. In conclusion, we propose Docker4Circ as a framework for reproducible and comprehensive analyses of circRNAs to efficiently exploit their biological role.

Supplementary and Associated Material

Subject Areas

circRNA; reproducible analysis; pipeline, Docker images

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