Circular RNAs (circRNAs) are regulatory RNAs that play a crucial role in various biological activities and have been identified as potential biomarkers for neurological disorders and cancer. CircRNAs have emerged as significant regulators of gene expression through different mechanisms, including regulation of transcription and splicing, modulation of translation, and post-translational modifications. Additionally, some circRNAs operate as microRNA (miRNA) sponges in the cytoplasm, boosting post-transcriptional expression of target genes by inhibiting miRNA activity. Although existing pipelines can reconstruct circRNAs, identify miRNAs sponged by them, retrieve cascade-regulated mRNAs, and represent the regulatory interactions as complex circRNA-miRNA-mRNA networks, none of the state-of-the-art approaches can discriminate the biological level at which the mRNAs involved in the interactions are regulated, avoiding considering potential target mRNAs not regulated at the post-transcriptional level. EasyCircR is a novel R package that combines circRNA detection and reconstruction with post-transcriptional gene expression analysis (exon-intron split analysis) and miRNA response element prediction. The package enables estimation and visualization of circRNA-miRNA-mRNA interactions through an intuitive Shiny application, leveraging the post-transcriptional regulatory nature of circRNA-miRNA relationship and excluding unrealistic regulatory interactions at the biological level. EasyCircR source code, Docker container and user guide are available at: https://github.com/InfOmics/EasyCircR

EasyCircR: Detection and reconstruction of circular RNAs post-transcriptional regulatory interaction networks / Aparo, Antonino; Avesani, Simone; Parmigiani, Luca; Napoli, Sara; Bertoni, Francesco; Bonnici, Vincenzo; Cascione, Luciano; Giugno, Rosalba. - In: COMPUTERS IN BIOLOGY AND MEDICINE. - ISSN 0010-4825. - 188:(2025). [10.1016/j.compbiomed.2025.109846]

EasyCircR: Detection and reconstruction of circular RNAs post-transcriptional regulatory interaction networks

Bonnici, Vincenzo;
2025-01-01

Abstract

Circular RNAs (circRNAs) are regulatory RNAs that play a crucial role in various biological activities and have been identified as potential biomarkers for neurological disorders and cancer. CircRNAs have emerged as significant regulators of gene expression through different mechanisms, including regulation of transcription and splicing, modulation of translation, and post-translational modifications. Additionally, some circRNAs operate as microRNA (miRNA) sponges in the cytoplasm, boosting post-transcriptional expression of target genes by inhibiting miRNA activity. Although existing pipelines can reconstruct circRNAs, identify miRNAs sponged by them, retrieve cascade-regulated mRNAs, and represent the regulatory interactions as complex circRNA-miRNA-mRNA networks, none of the state-of-the-art approaches can discriminate the biological level at which the mRNAs involved in the interactions are regulated, avoiding considering potential target mRNAs not regulated at the post-transcriptional level. EasyCircR is a novel R package that combines circRNA detection and reconstruction with post-transcriptional gene expression analysis (exon-intron split analysis) and miRNA response element prediction. The package enables estimation and visualization of circRNA-miRNA-mRNA interactions through an intuitive Shiny application, leveraging the post-transcriptional regulatory nature of circRNA-miRNA relationship and excluding unrealistic regulatory interactions at the biological level. EasyCircR source code, Docker container and user guide are available at: https://github.com/InfOmics/EasyCircR
2025
EasyCircR: Detection and reconstruction of circular RNAs post-transcriptional regulatory interaction networks / Aparo, Antonino; Avesani, Simone; Parmigiani, Luca; Napoli, Sara; Bertoni, Francesco; Bonnici, Vincenzo; Cascione, Luciano; Giugno, Rosalba. - In: COMPUTERS IN BIOLOGY AND MEDICINE. - ISSN 0010-4825. - 188:(2025). [10.1016/j.compbiomed.2025.109846]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/3021275
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