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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 i...

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Autores principales: Dal Molin, Anna, Gaffo, Enrico, Difilippo, Valeria, Buratin, Alessia, Tretti Parenzan, Caterina, Bresolin, Silvia, Bortoluzzi, Stefania
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921651/
https://www.ncbi.nlm.nih.gov/pubmed/35106564
http://dx.doi.org/10.1093/bib/bbab601
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author Dal Molin, Anna
Gaffo, Enrico
Difilippo, Valeria
Buratin, Alessia
Tretti Parenzan, Caterina
Bresolin, Silvia
Bortoluzzi, Stefania
author_facet Dal Molin, Anna
Gaffo, Enrico
Difilippo, Valeria
Buratin, Alessia
Tretti Parenzan, Caterina
Bresolin, Silvia
Bortoluzzi, Stefania
author_sort Dal Molin, Anna
collection PubMed
description 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.
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spelling pubmed-89216512022-03-15 CRAFT: a bioinformatics software for custom prediction of circular RNA functions Dal Molin, Anna Gaffo, Enrico Difilippo, Valeria Buratin, Alessia Tretti Parenzan, Caterina Bresolin, Silvia Bortoluzzi, Stefania Brief Bioinform Problem Solving Protocol 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. Oxford University Press 2022-01-31 /pmc/articles/PMC8921651/ /pubmed/35106564 http://dx.doi.org/10.1093/bib/bbab601 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Problem Solving Protocol
Dal Molin, Anna
Gaffo, Enrico
Difilippo, Valeria
Buratin, Alessia
Tretti Parenzan, Caterina
Bresolin, Silvia
Bortoluzzi, Stefania
CRAFT: a bioinformatics software for custom prediction of circular RNA functions
title CRAFT: a bioinformatics software for custom prediction of circular RNA functions
title_full CRAFT: a bioinformatics software for custom prediction of circular RNA functions
title_fullStr CRAFT: a bioinformatics software for custom prediction of circular RNA functions
title_full_unstemmed CRAFT: a bioinformatics software for custom prediction of circular RNA functions
title_short CRAFT: a bioinformatics software for custom prediction of circular RNA functions
title_sort craft: a bioinformatics software for custom prediction of circular rna functions
topic Problem Solving Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921651/
https://www.ncbi.nlm.nih.gov/pubmed/35106564
http://dx.doi.org/10.1093/bib/bbab601
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