Detecting differentially expressed circular RNAs from multiple quantification methods using a generalized linear mixed model.

Autor:	Buratin A; Department of Molecular Medicine, University of Padova, Padova, Italy.; Department of Biology, University of Padova, Padova, Italy., Romualdi C; Department of Biology, University of Padova, Padova, Italy., Bortoluzzi S; Department of Molecular Medicine, University of Padova, Padova, Italy.; Interdepartmental Research Center for Innovative Biotechnologies (CRIBI), University of Padova, Padova, Italy., Gaffo E; Department of Molecular Medicine, University of Padova, Padova, Italy.
Jazyk:	angličtina
Zdroj:	Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2022 May 20; Vol. 20, pp. 2495-2502. Date of Electronic Publication: 2022 May 20 (Print Publication: 2022).
DOI:	10.1016/j.csbj.2022.05.026
Abstrakt:	Finding differentially expressed circular RNAs (circRNAs) is instrumental to understanding the molecular basis of phenotypic variation between conditions linked to circRNA-involving mechanisms. To date, several methods have been developed to identify circRNAs, and combining multiple tools is becoming an established approach to improve the detection rate and robustness of results in circRNA studies. However, when using a consensus strategy, it is unclear how circRNA expression estimates should be considered and integrated into downstream analysis, such as differential expression assessment. This work presents a novel solution to test circRNA differential expression using quantifications of multiple algorithms simultaneously. Our approach analyzes multiple tools' circRNA abundance count data within a single framework by leveraging generalized linear mixed models (GLMM), which account for the sample correlation structure within and between the quantification tools. We compared the GLMM approach with three widely used differential expression models, showing its higher sensitivity in detecting and efficiently ranking significant differentially expressed circRNAs. Our strategy is the first to consider combined estimates of multiple circRNA quantification methods, and we propose it as a powerful model to improve circRNA differential expression analysis. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2022 The Author(s).)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu