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Updated: Aug 27, 2025

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
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circGPA: circRNA functional annotation based on probability-generating functions.

Petr Ryšavý1, Jiří Kléma2, Michaela Dostálová Merkerová3

  • 1Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic. petr.rysavy@fel.cvut.cz.

BMC Bioinformatics
|September 27, 2022
PubMed
Summary

We developed a fast algorithm to predict circular RNA (circRNA) functions using their interactions with microRNAs (miRNAs). This method efficiently annotates circRNAs, aiding disease research and biomarker discovery.

Keywords:
Annotation termCircular RNAInteraction network

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Circular RNAs (circRNAs) are crucial regulators of gene expression and implicated in various diseases.
  • Their stability makes circRNAs promising diagnostic biomarkers.
  • Discovering circRNA functions experimentally is costly and time-consuming.

Purpose of the Study:

  • To develop an efficient computational method for predicting circRNA functions.
  • To overcome the limitations of experimental methods for circRNA annotation.
  • To enable large-scale annotation and reannotation of circRNA databases.

Main Methods:

  • Constructing circRNA-miRNA interaction networks.
  • Information propagation from known functional nodes to target circRNAs.
  • Utilizing probability-generating functions for a deterministic p-value calculation in association tests.
  • Comparing the proposed method against Monte Carlo sampling.

Main Results:

  • The new algorithm is significantly more efficient than Monte Carlo sampling, achieving speeds two orders of magnitude faster.
  • The method enables feasible summary annotation of large circRNA datasets.
  • A comprehensive annotation of a current circRNA database was successfully generated.

Conclusions:

  • The proposed algorithm offers a highly effective and efficient approach for circRNA functional prediction and annotation.
  • This computational tool accelerates the discovery of circRNA functions, supporting disease research.
  • The methodology is generalizable to other RNA types.