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Buratin, A.

Publications and source records attributed to Buratin, A..

2 recordsLinked to original sources
bioRxiv2021

CRAFT: a bioinformatics software for custom prediction of circular RNA functions

Circular RNAs (circRNAs), transcripts generated by backsplicing, are particularly stable and pleiotropic molecules, whose dysregulation drives human diseases and cancer by modulating gene expression and signaling pathways. CircRNAs can regulate cellular processes by different mechanisms, including interaction with microRNAs (miRNAs) and RNA-binding proteins (RBP), and encoding specific peptides. The prediction of circRNA functions is instrumental to interpret their impact in diseases, and to prioritize circRNAs for functional investigation. Currently, circRNA functional predictions are provided by web databases that do not allow custom analyses, while self-standing circRNA prediction tools are mostly limited to predict only one type of function, mainly focusing on the miRNA sponge activity of circRNAs. To solve these issues, we developed CRAFT (CircRNA Function prediction Tool), a freely available computational pipeline that predicts circRNA sequence and molecular interactions with miRNAs and RBP, along with their coding potential. Analysis of a set of circRNAs with known functions has been used to appraise CRAFT predictions and to optimize its setting. CRAFT provides a comprehensive graphical visualization of the results, links to several knowledge databases, and extensive functional enrichment analysis. Moreover, it originally combines the predictions for different circRNAs. CRAFT is a useful tool to help the user explore the potential regulatory networks involving the circRNAs of interest and generate hypotheses about the cooperation of circRNAs into the modulation of biological processes. Key pointsO_LICRAFT is a self standing tool for comprehensive circRNA function prediction. C_LIO_LICRAFT functions include circRNA sequence reconstruction, microRNA and RNA-binding protein response elements and coding potential prediction. C_LIO_LIPredictions for multiple circRNAs are connected to infer possible cooperation networks and illustrate the potential impact of circRNAs on biological and disease processes. C_LI

bioinformatics↗
bioRxiv2021

Sensitive, reliable, and robust circRNA detection from RNA-seq with CirComPara2

Circular RNAs (circRNAs) are a large class of covalently closed RNA molecules that originate by a process called back-splicing. CircRNAs are emerging as functional RNAs involved in the regulation of biological processes as well as in disease and cancer mechanisms. Current computational methods for circRNA identification from RNA-seq experiments are characterised by low discovery rates and performance dependent on the analysed data set. We developed a new automated computational pipeline, CirComPara2 (https://github.com/egaffo/CirComPara2), that consistently achieves high recall rates without losing precision by combining multiple circRNA detection methods. In our benchmark analysis, CirComPara2 outperformed state-of-the-art circRNA discovery tools and proved to be a reliable and robust method for comprehensive transcriptomics characterisation.

bioinformatics↗