cirCodAn: A GHMM-based tool for accurate prediction of coding regions in circRNA

Studies focusing on characterizing circRNAs with the potential to translate into peptides are quickly advancing. It is helping to elucidate the roles played by circRNAs in several biological processes, especially in the emergence and development of diseases. While various tools are accessible for pr...

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Bibliographic Details
Published in:	Advances in protein chemistry and structural biology Vol. 139; pp. 289 - 334
Main Authors:	Barbosa, Denilson Fagundes, Oliveira, Liliane Santana, Nachtigall, Pedro Gabriel, Valentini Junior, Rodolpho, de Souza, Nayane, Paschoal, Alexandre Rossi, Kashiwabara, André Yoshiaki
Format:	Journal Article
Language:	English
Published:	Netherlands 2024
Subjects:	Generalized hidden Markov model Probabilistic models Transcriptomics CircRNA Coding region prediction
Online Access:	Get full text
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Summary:	Studies focusing on characterizing circRNAs with the potential to translate into peptides are quickly advancing. It is helping to elucidate the roles played by circRNAs in several biological processes, especially in the emergence and development of diseases. While various tools are accessible for predicting coding regions within linear sequences, none have demonstrated accurate open reading frame detection in circular sequences, such as circRNAs. Here, we present cirCodAn, a novel tool designed to predict coding regions in circRNAs. We evaluated the performance of cirCodAn using datasets of circRNAs with strong translation evidence and showed that cirCodAn outperformed the other tools available to perform a similar task. Our findings demonstrate the applicability of cirCodAn to identify coding regions in circRNAs, which reveals the potential of use of cirCodAn in future research focusing on elucidating the biological roles of circRNAs and their encoded proteins. cirCodAn is freely available at https://github.com/denilsonfbar/cirCodAn.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1876-1631
DOI:	10.1016/bs.apcsb.2023.11.012