circRNAprofiler: An R-based computational framework for the downstream analysis of circular RNAs

Table of Contents

• Introduction
• Install the package
• Load the package
• Running circRNAprofiler
  • Module 1 - Set up project folder
    • initCircRNAprofiler()
    • checkProjectFolder()
  • Module 2 - Import predicted circRNAs
    • formatGTF()
    • getBackSplicedJunctions()
  • Module 3 - Merge commonly identified circRNAs
    • mergeBSJunctions()
  • Module 4 - Filter circRNAs
    • filterCirc()
  • Module 5 - Find differentially expressed circRNAs
    • getDeseqRes()
    • getEdgerRes()
  • Module 6 - Map BSJ coordinates between species and genome assemblies
    • liftBSJcoords()
  • Module 7 - Annotate circRNAs internal structure and flanking introns
    • annotateBSJs()
  • Module 8 - Generate random BSJs
    • getRandomBSJunctions()
  • Retrieve target sequences
  • Module 9 - Retrieve internal circRNA sequences
    • getCircSeqs()
  • Module 10 - Retrieve BSJ sequences
    • getSeqsAcrossBSJs()
  • Module 11 - Retrieve sequences flanking the BSJs
    • getSeqsFromGRs()
  • Screen target sequences
  • Module 12 - Screen target sequences for RBP/de Novo motifs
    • getMotifs()
    • mergeMotifs()
  • Module 13 - Screen circRNA sequences for miRNA binding sites
    • getMiRsites()
    • rearrageMiRres()
  • Annotate target sequences
  • Module 14 - Annotate GWAS SNPs
    • annotateSNPsGWAS()
  • Module 15 - Annotate repetitive elements
    • annotateRepeats()
• Support
• Citation
• Acknowledgement
• Note
• References

Introduction

circRNAprofiler is an R-based framework that only requires an R installation and offers 15 modules for a comprehensive in silico analysis of circRNAs. This computational framework allows to combine and analyze circRNAs previously detected by multiple publicly available annotation-based circRNA detection tools. It covers different aspects of circRNAs analysis from differential expression analysis, evolutionary conservation, biogenesis to functional analysis. The pipeline used by circRNAprofiler is highly automated and customizable. Furthermore, circRNAprofiler includes additional functions for data visualization which facilitate the interpretation of the results.

Figure 1: Schematic representation of the circRNA analysis workflow implemented by circRNAprofiler. The grey boxes represent the 15 modules with the main R-functions reported in italics. The different type of sequences that can be selected are depicted in the dashed box. BSJ, Back-Spliced Junction.

This vignettes provides a guide of how to use the R package circRNAProfiler.

As practical example the RNA-sequencing data from human left ventricle tissues previously analyzed by our group for the presence of circRNAs (Khan et al. 2016), was here re-analyzed. Multiple detection tools (CircMarker(cm), MapSplice2 (ms) and NCLscan (ns)) were used this time fo