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.

NOTE: The protocol will begin with downloading and installing software required to analyze chimeric RNA sequencing libraries using SCRAP.
1. Installation
<ol>
	<li>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 &#34;git&#34; utility is present and installed in this directory. Check...

In Vivo RNA: RNA Interactions Using the SCRAP Bioinformatics Pipeline

<ol>
	<li>Morris, K. V., Mattick, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+rise+of+regulatory+RNA.">The rise of regulatory RNA.</a> Nature Reviews Genetics. 15 (6), 423-437 (2014).</li><li>Li, X., Jin, D. S., Eadara, S., Caterina, M. J., Meffert, M. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regulation+by+noncoding+RNAs+of+local+translation,+injury+responses,+and+pain+in+the+peripheral+nervous+system.">Regulation by noncoding RNAs of local translation, injury responses, and pain in the peripheral nervous system.</a> Neurobiology of Pain (Cambridge, Mass.). 13, 100119 (2023).</li><li>Shi, J., Zhou, T., Chen, Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exploring+the+expanding+universe+of+small+RNAs.">Exploring the expanding universe of small RNAs.</a> Nature Cell Biology. 24 (4), 415-423 (2022).</li><li>Broughton, J. P., Lovci, M. T., Huang, J. L., Yeo, G. W., Pasquinelli, A. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pairing+beyond+the+seed+supports+microRNA+targeting+specificity.">Pairing beyond the seed supports microRNA targeting specificity.</a> Molecular Cell. 64 (2), 320-333 (2016).</li><li>Grosswendt, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Unambiguous+identification+of+miRNA:target+site+interactions+by+different+types+of+ligation+reactions.">Unambiguous identification of miRNA:target site interactions by different types of ligation reactions.</a> Molecular Cell. 54 (6), 1042-1054 (2014).</li><li>Mills, W. T., Eadara, S., Jaffe, A. E., Meffert, M. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SCRAP:+a+bioinformatic+pipeline+for+the+analysis+of+small+chimeric+RNA-seq+data.">SCRAP: a bioinformatic pipeline for the analysis of small chimeric RNA-seq data.</a> RNA. 29 (1), 1-17 (2023).</li><li>Helwak, A., Kudla, G., Dudnakova, T., Tollervey, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mapping+the+human+miRNA+interactome+by+CLASH+reveals+frequent+noncanonical+binding.">Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding.</a> Cell. 153 (3), 654-665 (2013).</li><li>Hoefert, J. E., Bjerke, G. A., Wang, D., Yi, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+microRNA-200+family+coordinately+regulates+cell+adhesion+and+proliferation+in+hair+morphogenesis.">The microRNA-200 family coordinately regulates cell adhesion and proliferation in hair morphogenesis.</a> Journal of Cell Biology. 217 (6), 2185-2204 (2018).</li><li>Moore, M. J., Zhang, C., Gantman, E. C., Mele, A., Darnell, J. C., Darnell, R. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mapping+Argonaute+and+conventional+RNA-binding+protein+interactions+with+RNA+at+single-nucleotide+resolution+using+HITS-CLIP+and+CIMS+analysis.">Mapping Argonaute and conventional RNA-binding protein interactions with RNA at single-nucleotide resolution using HITS-CLIP and CIMS analysis.</a> Nature Protocols. 9 (2), 263-293 (2014).</li><li>Bjerke, G. A., Yi, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrated+analysis+of+directly+captured+microRNA+targets+reveals+the+impact+of+microRNAs+on+mammalian+transcriptome.">Integrated analysis of directly captured microRNA targets reveals the impact of microRNAs on mammalian transcriptome.</a> RNA. 26 (3), 306-323 (2020).</li><li>Reuter, J. S., Mathews, D. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNAstructure:+software+for+RNA+secondary+structure+prediction+and+analysis.">RNAstructure: software for RNA secondary structure prediction and analysis.</a> BMC Bioinformatics. 11 (1), 129 (2010).</li><li>Moore, M. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miRNA-target+chimeras+reveal+miRNA+3&#8242;-end+pairing+as+a+major+determinant+of+Argonaute+target+specificity.">miRNA-target chimeras reveal miRNA 3&#8242;-end pairing as a major determinant of Argonaute target specificity.</a> Nature Communications. 6 (1), 8864 (2015).</li><li>Travis, A. J., Moody, J., Helwak, A., Tollervey, D., Kudla, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hyb:+a+bioinformatics+pipeline+for+the+analysis+of+CLASH+(crosslinking,+ligation+and+sequencing+of+hybrids)+data.">Hyb: a bioinformatics pipeline for the analysis of CLASH (crosslinking, ligation and sequencing of hybrids) data.</a> Methods (San Diego, Calif.). 65 (3), 263-273 (2014).</li></ol>

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...

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 frequently used for the prediction of miRNA:target RNA interactions. While these bioinformatic algorithms have been successful, they also can harbor both false positive and false negative results, as has been reviewed elsewhere4,5,6. Recently, several biochemical approaches have been designed and implemented that allow unambiguous and semiquantitative determination of in vivo sncRNA:target RNA interactions by in vivo crosslinking and ensuing incorporation of a ligation step to physically attach the sncRNA to its target to form a single chimeric RNA4,5,7,8,9,10. Subsequent preparation of sequencing libraries from the chimeric RNAs allows assessment of the sncRNA:target RNA interactions by computational processing of the sequencing data. This video provides a tutorial for installing and using a computational pipeline termed small chimeric RNA analysis pipeline (SCRAP), which is designed to allow robust and reproducible analysis of sncRNA:target RNA interactions from chimeric RNA sequencing libraries6.
A goal of this tutorial is to assist investigators in avoiding excessive reliance on purely predictive bioinformatic algorithms by lowering barriers to the analysis of data generated through biochemical approaches providing chimeric molecular readouts of sncRNA:target RNA interactions. This tutorial provides practical steps and tips to guide entry-level computational scientists through the use of a pipeline, SCRAP, developed for analyzing chimeric RNA sequencing data, which can be generated by several existing biochemical protocols, including crosslinking, ligation, and sequencing of hybrids (CLASH) and covalent ligation of endogenous Argonaute-bound RNAs- crosslinking and immunoprecipitation (CLEAR-CLIP)7,9.
The use of SCRAP offers several advantages for the analysis of chimeric RNA sequencing data, compared to other computational pipelines6. One salient advantage is its extensive annotation and the incorporation of call-outs to well-supported and routinely updated bioinformatic scripts within the pipeline, in comparison to alternative pipelines that often rely on custom and/or unsupported scripts for steps in the pipeline. This feature lends stability to SCRAP, making it more worthwhile for researchers to familiarize themselves with the pipeline and to incorporate its use into their workflow. SCRAP has also been demonstrated to outperform alternative pipelines in calling peaks of sncRNA:target RNA interactions and to have cross-platform functionality, as detailed in a prior publication6.
By the end of this tutorial, users will be able to (i) know platform requirements for SCRAP and install SCRAP pipelines, (ii) install reference genomes and set up command line parameters for SCRAP, and (iii) understand peak calling criteria and perform peak calling and peak annotation.
This video will describe in practical detail how researchers studying RNA biology may install and optimally use the computational pipeline, SCRAP, to analyze sncRNA interactions with target RNAs, such as messenger RNAs, in chimeric RNA-sequencing data obtained through one of the discussed biochemical approaches to sequencing library preparation.
SCRAP is a command line utility. Generally, following the guide below, the user will need to (i) download and install SCRAP (https://github.com/Meffert-Lab/SCRAP), (ii) Install reference genomes and run SCRAP, and (iii) perform peak calling and annotation.
Further details of the computational steps in this procedure can be found at&#160;https://github.com/Meffert-Lab/SCRAP. This article will provide the setup and background information to allow investigators with entry-level computational skills to install, optimize, and use SCRAP on chimeric RNA sequencing library datasets.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Genomes</td>
			<td>UCSC Genome browser</td>
			<td>N/A</td>
			<td>https://genome.ucsc.edu/ or https://www.ncbi.nlm.nih.gov/data-hub/genome/</td>
		</tr>
		<tr>
			<td>Linux</td>
			<td>Linux</td>
			<td>Ubuntu 20.04 or 22.04 LTS recommended</td>
			<td></td>
		</tr>
		<tr>
			<td>Mac</td>
			<td>Apple</td>
			<td>Mac OSX (&#62;11)</td>
			<td></td>
		</tr>
		<tr>
			<td>Platform setup</td>
			<td>GitHub</td>
			<td>N/A</td>
			<td>https://github.com/Meffert-Lab/SCRAP/blob/main/PLATFORM-SETUP.md]</td>
		</tr>
		<tr>
			<td>SCRAP pipeline</td>
			<td>GitHub</td>
			<td>N/A</td>
			<td>https://github.com/Meffert-Lab/SCRAP</td>
		</tr>
		<tr>
			<td>Unix shell</td>
			<td>Unix operating system</td>
			<td>bash &#62;=5.0</td>
			<td></td>
		</tr>
		<tr>
			<td>Unix shell</td>
			<td>Unix operating system</td>
			<td>zsh (5.9 recommended)</td>
			<td></td>
		</tr>
		<tr>
			<td>Windows</td>
			<td>Windows</td>
			<td>WSL Ubuntu 20.04 or 22.04 LTS</td>
			<td></td>
		</tr>
	</tbody>
</table>

computational analysis tutorial for chimeric small noncoding rna: target rna sequencing libraries

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.

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data 

Computational Analysis Tutorial for Chimeric Small Noncoding RNA: Target RNA Sequencing Libraries

This protocol covers the installation and practical steps for the use of a computational pipeline designed to analyze high throughput sequencing data from chimeric noncoding RNA-target RNA-interactions. The molecular strategy to form chimeric RNAs is a relatively recent development that has advantages when compared to prior biochromatic prediction tools in unambiguously understanding the genome-wide noncoding RNA-target RNA-interaction landscape. Sequencing data generated from the high throughput sequencing of the chimeric RNAs can present an analysis challenge for new adopters of this strategy.We have established an open source cross-platform computational pipeline that is highly annotated to allow entry-level users to analyze data from their chimeric noncoding RNA-target RNA sequencing experiments. In future experiments, the Mefford lab plans to use computational analysis by scrap on dataset generated from our ongoing investigations into post transcriptional regulation in the mammalian nervous system, as well as to maintain an update scrap on our GitHub.

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 ...

Watch this Scientific Journal Video about A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data at JoVE.com

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.

An understanding of the in vivo gene regulatory interactions of small noncoding RNAs (sncRNAs), such as microRNAs (miRNAs), with their target RNAs has ...

computational-analysis-tutorial-for-chimeric-small-noncoding-rna

Research

JoVE Journal

Biology

402 visualizações.  Johns Hopkins University School of Medicine. Este protocolo abrange a instalação e as etapas práticas para o uso de um pipeline computacional projetado para analisar dados de sequenciamento de alto rendimento de interações de RNA-alvo não codificantes quiméricos. A estratégia molecular para formar RNAs quiméricos é um desenvolvimento relativamente recente que tem vantagens quando comparado com ferramentas de predição biocromática anteriores na compreensão inequívoca do cenário de interação RNA-alvo não-codificante do ...

Vídeo: Autor em destaque: Um pipeline computacional para analisar interações quiméricas de RNA-alvo de RNA não codificante em dados de sequenciamento de alto rendimento 