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In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
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MSCFS: inferring circRNA functional similarity based on multiple data sources.

Liang Shu1, Cheng Zhou1, Xinxu Yuan2

  • 1School of Computer Science and Engineering, Central South University, Lushangnan Road, Changsha, China.

BMC Bioinformatics
|July 17, 2021
PubMed
Summary

A new method, MSCFS, effectively calculates circular RNA (circRNA) functional similarity by integrating diverse data. This approach aids in predicting circRNA functions and understanding their roles in biological processes.

Keywords:
CircRNA functional similarityMultiple data sourcesMultiple representations

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Circular RNAs (circRNAs) are increasingly recognized for their critical roles in biological processes and human health.
  • Accurate inference of circRNA functions and functional similarity is crucial but lacks effective computational approaches.
  • Existing methods struggle to comprehensively capture the multifaceted nature of circRNA functionality.

Purpose of the Study:

  • To develop an effective computational approach for calculating circRNA functional similarity.
  • To integrate multiple data sources for a more robust estimation of circRNA functional relationships.
  • To enable better prediction of circRNA functions and associations.

Main Methods:

  • Proposed a novel method, MSCFS (Multi-Source CircRNA Functional Similarity), to compute circRNA functional similarity.
  • Integrated data from circRNA-disease associations, circRNA-gene-Gene Ontology (GO) associations, and circRNA sequence information.
  • Employed representation learning techniques to build three distinct circRNA functional similarity networks, which were then merged.

Main Results:

  • MSCFS demonstrated a positive correlation with circRNA-miRNA association similarity and circRNA co-expression similarity, validating its performance.
  • A circRNA functional similarity network was constructed using MSCFS.
  • Case analyses confirmed the method's utility in inferring novel circRNA functions and other potential associations.

Conclusions:

  • MSCFS successfully integrates diverse data sources relevant to circRNA functions.
  • The method provides a valuable tool for exploring circRNA functional similarity.
  • Correlation and case analyses support MSCFS as a reliable approach for circRNA functional prediction.