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RNAenrich: a web server for non-coding RNA enrichment.

Song Zhang1,2, Kuerbannisha Amahong2, Yintao Zhang2

  • 1Center for Clinical Pharmacy, Cancer Center, Department of Pharmacy, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang 310014, China.

Bioinformatics (Oxford, England)
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A new tool, RNAenrich, offers comprehensive non-coding RNA (ncRNA) enrichment analysis for various RNA types. It utilizes experimentally validated RNA-target interactions for more accurate functional insights, improving upon existing methods.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • Advances in RNA sequencing necessitate robust functional analysis tools for non-coding RNAs (ncRNAs).
  • Existing tools often focus on single ncRNA types or rely on low-confidence predicted targets.
  • Understanding ncRNA function requires considering their interactions with targets like messenger RNAs (mRNAs) and proteins.

Purpose of the Study:

  • To develop a comprehensive and accurate online tool, RNAenrich, for ncRNA enrichment analysis.
  • To support the analysis of diverse ncRNA types, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), small nucleolar RNAs (snoRNAs), and PIWI-interacting RNAs (piRNAs).
  • To integrate experimentally validated RNA-target interaction data for enhanced functional enrichment.

Main Methods:

  • Development of the RNAenrich online tool.
  • Inclusion of a built-in database with millions of experimentally validated RNA-target interactions.
  • Implementation of analysis for various ncRNA types in humans and mice.
  • Generation of interacting networks between ncRNAs and their targets.

Main Results:

  • RNAenrich enables enrichment analysis for multiple ncRNA types (miRNA, lncRNA, circRNA, snoRNA, piRNA, mRNA) in humans and mice.
  • The tool incorporates a large database of experimentally validated RNA-target interactions, improving accuracy.
  • RNAenrich provides a comprehensive interacting network for mechanistic studies of ncRNA function.
  • Case study on COVID-19-related miRNAs demonstrated RNAenrich's improved comprehensiveness and accuracy.

Conclusions:

  • RNAenrich provides a valuable resource for comprehensive and accurate ncRNA functional enrichment analysis.
  • The tool's integration of validated interaction data enhances the reliability of functional predictions.
  • RNAenrich facilitates deeper mechanistic understanding of ncRNA functions through network visualization.