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Genome-Wide circRNA Profiling from RNA-seq Data.

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Summary

This guide explains how to analyze RNA sequencing (RNA-seq) data to discover circular RNA (circRNA) expression patterns. It helps researchers profile circRNAs, aiding in understanding their roles in health and disease.

Keywords:
Circular RNAsExpression analysisRibo-depleted total RNA-seqcircRNA

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Circular RNAs (circRNAs) are increasingly recognized for their roles in cellular functions, development, and diseases.
  • Thousands of circRNAs have been identified across various species.
  • Analyzing RNA sequencing (RNA-seq) data offers a powerful approach to uncover circRNA expression trends.

Purpose of the Study:

  • To provide a practical primer for profiling circular RNAs (circRNAs) from RNA-seq datasets.
  • To guide wet lab scientists with limited bioinformatics experience through circRNA data analysis.
  • To facilitate the discovery of novel circRNA expression patterns and their potential implications.

Main Methods:

  • Accessing and interpreting existing circRNA annotations.
  • Converting circRNA annotations into specific junction sequences for read alignment.
  • Quantifying circRNA expression levels using aligned RNA-seq reads.

Main Results:

  • A clear workflow for identifying and quantifying circRNAs from RNA-seq data.
  • Methodology for converting annotation files into usable junction sequences.
  • Guidance on interpreting alignment data to assess circRNA expression trends.

Conclusions:

  • RNA-seq data analysis is a viable method for circRNA profiling.
  • This primer empowers researchers to explore circRNA expression without extensive bioinformatics expertise.
  • Understanding circRNA expression contributes to insights into cellular homeostasis, development, and disease.