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Related Experiment Video

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Identification of Circular RNAs using RNA Sequencing
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Specific identification and quantification of circular RNAs from sequencing data.

Jun Cheng1, Franziska Metge1, Christoph Dieterich1

  • 1Max Planck Institute for Biology of Ageing, Joseph-Stelzmann-Strasse 9B, 50931 Cologne, Germany.

Bioinformatics (Oxford, England)
|November 12, 2015
PubMed
Summary

We developed DCC and CircTest, new computational tools for precise detection and quantification of circular RNAs (circRNAs) from sequencing data. These tools offer improved accuracy for studying circRNA functions and expression patterns.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Circular RNAs (circRNAs) are a class of RNA molecules with largely unknown functions.
  • Despite recent advances, computational tools for detecting and quantifying circRNAs remain limited.
  • High-throughput sequencing has renewed interest in circRNAs, necessitating improved analytical methods.

Purpose of the Study:

  • To develop and validate novel computational tools for the accurate detection and quantification of circRNAs.
  • To enable the estimation of circRNA versus host gene expression.
  • To facilitate the testing of host gene independence of circRNA expression across experimental conditions.

Main Methods:

  • Development of the DCC software for systematic detection of back-splice junctions from next-generation sequencing data using STAR aligner output.
  • Application of filters and integration of replicate data by DCC to generate a precise list of circRNA candidates.
  • Estimation of circRNA and host gene expression by DCC using junction and non-junction read counts.
  • Utilizing the R package CircTest to test for host gene-independence of circRNA expression.

Main Results:

  • DCC demonstrates significantly higher precision compared to existing state-of-the-art methods at similar sensitivity levels.
  • The software was validated on mouse brain and publicly available sequencing datasets.
  • DCC and CircTest enable robust estimation and differential analysis of circRNA expression.
  • The approach was successfully applied to identify age-dependent circRNAs in Drosophila.

Conclusions:

  • DCC and CircTest provide a powerful and precise computational framework for circRNA research.
  • These tools address the current limitations in circRNA detection and quantification.
  • The developed software facilitates deeper investigation into the functional roles of circRNAs.