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In This Article

  • Summary
  • Abstract
  • Introduction
  • Protocol
  • Representative Results
  • Discussion
  • Acknowledgements
  • Materials
  • References
  • Reprints and Permissions

Summary

Here, we present a protocol demonstrating the installation and use of a bioinformatics pipeline to analyze chimeric RNA sequencing data used in the study of in vivo RNA:RNA interactions.

Abstract

An understanding of the in vivo gene regulatory interactions of small noncoding RNAs (sncRNAs), such as microRNAs (miRNAs), with their target RNAs has been advanced in recent years by biochemical approaches which use cross-linking followed by ligation to capture sncRNA:target RNA interactions through the formation of chimeric RNAs and subsequent sequencing libraries. While datasets from chimeric RNA sequencing provide genome-wide and substantially less ambiguous input than miRNA prediction software, distilling this data into meaningful and actionable information requires additional analyses and may dissuade investigators lacking a computational background. This report provides a tutorial to support entry-level computational biologists in installing and applying a recent open-source software tool: Small Chimeric RNA Analysis Pipeline (SCRAP). Platform requirements, updates, and an explanation of pipeline steps and manipulation of key user-input variables is provided. Reducing a barrier for biologists to gain insights from chimeric RNA sequencing approaches has the potential to springboard discovery-based investigations of regulatory sncRNA:target RNA interactions in multiple biological contexts.

Introduction

Small noncoding RNAs are highly studied for their post-transcriptional roles in coordinating expression from suites of genes in diverse processes such as differentiation and development, signal processing, and disease1,2,3. The ability to accurately determine the target transcripts of gene-regulatory small noncoding RNAs (sncRNAs), including microRNAs (miRNAs), is of importance to studies of RNA biology at both basic and translational levels. Bioinformatic algorithms that exploit anticipated complementarity between the miRNA seed sequence and its potential targets have been f....

Protocol

NOTE: The protocol will begin with downloading and installing software required to analyze chimeric RNA sequencing libraries using SCRAP.

1. Installation

  1. Before installing SCRAP, install the dependencies Git and Miniconda on the machine to be used for the analyses. Git is likely already installed. On the Mac OSX platform, for example, verify this using which git to see that the "git" utility is present and installed in this directory. Check.......

Representative Results

Results for sncRNA:target RNA detected by a modified version of SCRAP (SCRAP release 2.0, which implements modifications for rRNA filtering) on previously published sequencing datasets prepared using CLEAR-CLIP9 is shown in Figure 2 and Table 1. Users can appreciate the decrease in the relative fraction miRNA interactions with intron regions which occurs following the isolation of high-confidence interactions by peak calling in SCRAP. Additional data .......

Discussion

This protocol on the use of SCRAP pipeline for analysis of sncRNA:target RNA interactions is designed to assist investigators who are entering into computational analysis. Completion of the tutorial is expected to guide investigators with entry-level or greater computational experience through the steps required for installation and use of this pipeline and its application to analyze data gained from chimeric RNA sequencing libraries. Steps critical to the completion of this protocol include correct reference installatio.......

Acknowledgements

We thank members of the Meffert laboratory for helpful discussions, including BH Powell and WT Mills IV, for critical feedback on describing the installation and implementation of the pipeline. This work was supported by a Braude Foundation award, the Maryland Stem Cell Research Fund Launch Program, the Blaustein Endowment for Pain Research and Education award, and NINDS RO1NS103974 and NIMH RO1MH129292 to M.K.M.

....

Materials

NameCompanyCatalog NumberComments
GenomesUCSC Genome browserN/Ahttps://genome.ucsc.edu/ or https://www.ncbi.nlm.nih.gov/data-hub/genome/
LinuxLinuxUbuntu 20.04 or 22.04 LTS recommended
MacAppleMac OSX (>11)
Platform setupGitHubN/Ahttps://github.com/Meffert-Lab/SCRAP/blob/main/PLATFORM-SETUP.md]
SCRAP pipelineGitHubN/Ahttps://github.com/Meffert-Lab/SCRAP
Unix shellUnix operating systembash >=5.0
Unix shellUnix operating systemzsh (5.9 recommended)
WindowsWindowsWSL Ubuntu 20.04 or 22.04 LTS

References

  1. Morris, K. V., Mattick, J. S. The rise of regulatory RNA. Nature Reviews Genetics. 15 (6), 423-437 (2014).
  2. Li, X., Jin, D. S., Eadara, S., Caterina, M. J., Meffert, M. K. Regulation by noncoding RNAs of local transl....

Explore More Articles

Computational AnalysisChimeric Small Noncoding RNATarget RNA SequencingHigh throughput SequencingCross linkingLigationMiRNAPost transcriptional RegulationSCRAPComputational PipelineOpen source Software

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