Name: Installing and Running SCRAP for Small Noncoding RNA-mRNA Interaction Analysis
Uploaded: 2023-12-01T14:40:00
Description: Watch this Scientific Journal Video about Installing and Running SCRAP for Small Noncoding RNA-mRNA Interaction Analysis at JoVE.com

installing and running scrap for small noncoding rna-mrna interaction analysis

In Vivo RNA: RNA Interactions Using the SCRAP Bioinformatics Pipeline

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.

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.

Watch this Scientific Journal Video about Installing and Running SCRAP for Small Noncoding RNA-mRNA Interaction Analysis at JoVE.com

Installing and Running SCRAP for Small Noncoding RNA-mRNA Interaction Analysis  

Before initiating SCRAP installation on the Mac OSX platform, execute the command which git in the terminal to confirm the existence of Git. To verify Miniconda installation, type which conda in the terminal. To install Miniconda, refer to the Platform Setup markdown file on the Meffert Lab GitHub Repository for instructions to install on Windows, Mac OS, and Ubuntu For Mac systems, using the Home Brew Package Manager, use the command brew install miniconda to install Miniconda.To install Conda, run conda init, specifying the shell in use, then close and reopen the shell. A successful installation displays the base environment activated within the terminal session. Next, run the indicated git clone to obtain the latest copy of the SCRAP source code.Install Mamba, an improved package solver for Conda. Using the following command, install all the dependencies for SCRAP from SCRAP_environment. yml to its own Conda environment.Next, use the indicated bash script, specific to the organism whose SNC RNA mRNA interactions are analyzed for SCRAP. Provide the directory of the SCRAP source folder and perform the installation steps using the files within the FASTA and annotation folders. Ensure the directory path is complete and ends with a slash.Refer to the tables in ReadMe. md for the correct miRBase species abbreviations. For updated reference genome, use the UCSC Genome Browser or the NCBI Genome Data Hub.Inspect the corresponding species.annotation. bed files in the annotation directory in the SCRAP source folder. If there's a need to analyze a different species, provide an indicated file.After installing dependencies and SCRAP, use the indicated script to initiate SCRAP analysis with the specified command structure. List the full path to the sample directories without any shorthand and format the sample directories with the folder name matching the sample name. Emphasize that the path listed leads to the directory containing all the sample folders and not to an individual sample folder or file.Next, list the entire path to the adapter file. Verify that the sample names in the adapter file match the previously mentioned folder names and file names. Specify the sample type, either paired end or single end, and state the choice of RNA filters for pre-mRNAs, tRNAs, and optionally RNA cleaning.List the entire path to the reference directory. miRBase abbreviation and the reference genome abbreviation.

Research

JoVE Journal

Biology

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