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Updated: Sep 20, 2025

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
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Detecting differentially expressed circular RNAs from multiple quantification methods using a generalized linear

Alessia Buratin1,2, Chiara Romualdi2, Stefania Bortoluzzi1,3

  • 1Department of Molecular Medicine, University of Padova, Padova, Italy.

Computational and Structural Biotechnology Journal
|June 6, 2022
PubMed
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This summary is machine-generated.

This study introduces a new method for analyzing circular RNA (circRNA) expression by combining data from multiple quantification tools. This generalized linear mixed model (GLMM) approach improves the detection and ranking of differentially expressed circRNAs.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Circular RNAs (circRNAs) are crucial in understanding phenotypic variations.
  • Multiple computational tools exist for circRNA identification, with consensus strategies improving reliability.
  • Integrating expression estimates from various circRNA quantification tools for downstream analysis remains a challenge.

Purpose of the Study:

  • To develop a novel method for assessing differential expression of circRNAs using quantifications from multiple algorithms simultaneously.
  • To address the challenge of integrating circRNA expression estimates from diverse tools into a unified analytical framework.

Main Methods:

  • Utilized generalized linear mixed models (GLMM) to analyze circRNA abundance count data from multiple tools.
Keywords:
AUC, Area under the ROC curveCircular RNAsDECs, Differentially Expressed circRNAsDEMs, Differential Expression ModelsDifferential expressionFDR, False Discovery RateGLMM, Generalized Linear Mixed ModelGeneralized linear mixed modelsRNA-seqRNAseq, RNA sequencingTPR, True Positive RatecircRNAscircRNAs, circular RNAs

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  • The GLMM framework accounts for sample correlation structures within and between quantification tools.
  • Compared the proposed GLMM approach against three established differential expression models.

    • Main Results:

    • The GLMM approach demonstrated higher sensitivity in detecting differentially expressed circRNAs.
    • The method efficiently ranked significant differentially expressed circRNAs.
    • This strategy represents the first approach to integrate combined estimates from multiple circRNA quantification methods.

    Conclusions:

    • The proposed GLMM-based strategy offers a powerful solution for improving circRNA differential expression analysis.
    • This novel method enhances the accuracy and robustness of identifying differentially expressed circRNAs by leveraging multiple quantification tools.
    • The findings provide a valuable tool for researchers investigating circRNA functions in various biological contexts.