Utilizing RNA-seq data in monotone iterative generalized linear model to elevate prior knowledge quality of the circRNA-miRNA-mRNA regulatory axis.

Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE

Original Publication: [London] : BioMed Central, 2000-

RNA, Circular*/genetics ; RNA, Circular*/metabolism ; RNA, Messenger*/genetics ; RNA, Messenger*/metabolism ; MicroRNAs*/genetics ; MicroRNAs*/metabolism ; RNA-Seq*/methods; Algorithms ; Linear Models ; Gene Regulatory Networks ; Humans ; Computational Biology/methods ; Sequence Analysis, RNA/methods

Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no Competing interests.
Background: Current experimental data on RNA interactions remain limited, particularly for non-coding RNAs, many of which have only recently been discovered and operate within complex regulatory networks. Researchers often rely on in-silico interaction detection algorithms, such as TargetScan, which are based on biochemical sequence alignment. However, these algorithms have limited performance. RNA-seq expression data can provide valuable insights into regulatory networks, especially for understudied interactions such as circRNA-miRNA-mRNA. By integrating RNA-seq data with prior interaction networks obtained experimentally or through in-silico predictions, researchers can discover novel interactions, validate existing ones, and improve interaction prediction accuracy.
Results: This paper introduces Pi-GMIFS, an extension of the generalized monotone incremental forward stagewise (GMIFS) regression algorithm that incorporates prior knowledge. The algorithm first estimates prior response values through a prior-only regression, interpolates between these prior values and the original data, and then applies the GMIFS method. Our experimental results on circRNA-miRNA-mRNA regulatory interaction networks demonstrate that Pi-GMIFS consistently enhances precision and recall in RNA interaction prediction by leveraging implicit information from bulk RNA-seq expression data, outperforming the initial prior knowledge.
Conclusion: Pi-GMIFS is a robust algorithm for inferring acyclic interaction networks when the variable ordering is known. Its effectiveness was confirmed through extensive experimental validation. We proved that RNA-seq data of a representative size help infer previously unknown interactions available in TarBase v9 and improve the quality of circRNA disease annotation.
(© 2025. The Author(s).)

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SGS23/184/OHK3/3T/13 Grant Agency of the Czech Technical University in Prague; e-INFRA CZ project (ID:90254) Ministry of Education, Youth and Sports of the Czech Republic

Keywords: Bayesian network; Circular RNA; Functional annotation; Penalized regression; Structure inference

0 (RNA, Circular)
0 (RNA, Messenger)
0 (MicroRNAs)

Date Created: 20250527 Date Completed: 20250528 Latest Revision: 20250531

20250531

PMC12117772

10.1186/s12859-025-06161-w

40426030