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 if Miniconda is installed using which conda. If nothing is returned, install Miniconda. Miniconda requires 400 MB of disk space to install.
	<ol>
		<li>There are a few methods to install Miniconda, and they differ by platform. Refer to the PLATFORM-SETUP markdown file on the Meffert Lab GitHub repository [https://github.com/Meffert-Lab/SCRAP/blob/main/PLATFORM-SETUP.md] where there are further instructions for installing on Windows, MacOS, and Ubuntu. For Linux users, Linux has its own default package manager (apt). In the case specific to this study, use the command&#160;brew install Miniconda to install Miniconda using an existing package manager, brew. 
		NOTE: &#39;Homebrew&#39;, termed &#39;brew&#39; is an open-source software package management system that simplifies the installation of software on Apple&#39;s operating system, macOS.</li>
		<li>If conda is being installed for the first time, run conda init for the particular shell that is in use. In the example here, that shell in use is zsh. Then, close and re-open the shell. If conda was successfully installed, the base environment activated within the terminal session will be seen.</li>
	</ol>
	</li>
	<li>Download the SCRAP source and install its dependencies.
	<ol>
		<li>The preferred method for obtaining SCRAP source is using Git. Access this by running git clone&#160;https://github.com/Meffert-Lab/SCRAP to obtain the latest copy of the source code.</li>
		<li>Install mamba, an improved package solver for conda, and install all the dependencies for SCRAP from SCRAP_environment.yml to its own conda environment using the following commands: 
		conda install -n base conda-forge::mamba 
		mamba env create -f SCRAP/SCRAP_environment.yml -n SCRAP</li>
	</ol>
	</li>
	<li>Next, run the reference installation for SCRAP. The arguments used in the reference installation will be specific to the organism whose sncRNA-mRNA interactions are being analyzed. 
	bash SCRAP/bin/Reference_Installation.sh -r full/path/to/SCRAP/ -m hsa -g hg38 -s human
	<ol>
		<li>Provide the directory of the SCRAP source folder for reference installation. Installation steps will then be performed using the files within the fasta and annotation folders. List the full path without any shorthand. End with a slash.</li>
		<li>Refer to the tables in README.md for the correct miRbase species abbreviations. The up-to-date reference genomes can be found at https://genome.ucsc.edu/ or https://www.ncbi.nlm.nih.gov/data-hub/genome/. In this example,&#160;hg38 will be used for the mouse GRCm38 genome.</li>
		<li>The currently included species for annotation are human, mouse, and worm. View the corresponding species.annotation.bed files in the annotation directory in the SCRAP source folder. If the use of a different species for analysis is desired, provide an annotation.bed file that follows the same naming scheme species.annotation.bed.</li>
	</ol>
	</li>
</ol>
2. Running SCRAP
<ol>
	<li>Now that the dependencies and SCRAP are installed, - run the script SCRAP.sh 
	bash SCRAP/bin/SCRAP.sh -d full/path/to/CLASH_Human/ -a full/path/to/CLASH_Human/CLASH_Human_Adapters.txt -p no -f yes -r full/path/to/SCRAP/ -m hsa -g hg38
	<ol>
		<li>List the entire path to the sample directories without any shorthand. Format the sample directories with the folder name matching the sample name exactly, as shown in Figure 1.</li>
		<li>Note that the path listed is the path to the directory that contains all the sample folders, not the path to any individual sample folder or a sample file (refer to the command line in step 2.1).</li>
		<li>Next, list the entire path to the adapter file. Ensure that the sample names in the adapter file match the previously mentioned folder names and file names (refer to the command line in step 2.1).</li>
		<li>Indicate whether the samples are paired-end and whether or not filtering for pre-miRNAs and/or tRNAs will be performed. Add a filter for rRNA cleaning if desired (refer to the command line in step 2.1). 
		NOTE: The users may or may not decide to use these filters depending on the sample types and experimental goals. Depending upon the experimental design, pre-miRNAs, tRNAs, and rRNAs can consume available sequencing depth for real sncRNA:target RNA chimeras and users can employ filters to exclude them. However, users may want to avoid such filtering in certain circumstances (e.g., mapping sncRNA targets to the mitochondrial genome, which contains mitochondrial rRNAs).</li>
		<li>Next, list the entire path to the reference directory, the miRbase abbreviation, and the reference genome abbreviation (refer to the command line in step 2.1). 
		&#8203;NOTE: The script may take a few hours to complete, depending on the dataset size and the CPU of the computer being used.</li>
	</ol>
	</li>
</ol>
3. Peak calling and annotation
<ol>
	<li>Once SCRAP is finished running, check that the output includes, among other files, a SAMPLE.aligned.unique.bam file. This is a binary file containing alignments of target RNAs onto the user-provided reference genome.</li>
	<li>Now perform peak calling by running Peak_Calling.sh. 
	bash SCRAP/bin/Peak_Calling.sh -d CLASH_Human/ -a CLASH_Human/CLASH_Human_Adapters.txt -c 3 -l 2 -f no -r SCRAP/ -m hsa -g hg38 
	NOTE: Peak calling is a feature of SCRAP, which is designed to allow researchers to readily evaluate the most robust and reproducible small noncoding RNA:target RNA interactions within their chimeric RNA libraries. This feature, for example, can aid researchers in identifying interactions that they may want to select for further investigation. Step 3.2.2 below describes how the user sets the criteria which they want to be used to define the stringency with which a peak is called - this includes the number of unique interactions, or sequencing reads, which must have occurred for the peak to be called, as well as the number of libraries in which this particular interaction must have occurred.
	<ol>
		<li>Again, list the full paths to the directory containing the sample folders, and the adapter file (refer to the command line in step 3.2).</li>
		<li>Next, set the minimum number of sequencing reads required for a peak to be called (refer to the command line in step 3.2).</li>
		<li>Set the minimum number of distinct sequencing libraries that must contain a peak for it to be called (refer to the command line in step 3.2). 
		NOTE: The choice of values for both 3.2.2 and 3.2.3 will depend upon the nature of the samples sequenced and the number of samples or sample types. Here, at least 3 chimeric sequencing reads in a sample are required to call a peak, and the peak must be supported by at least 2 samples. An investigator evaluating a dataset in which there are many sequencing library replicates for a given condition, for example, might decide to require the presence of the reads in a greater number of sample sequencing libraries.</li>
		<li>Indicate whether sncRNAs of the same family must contribute to the same peak. For example, since miRNAs of the same family share seed sequences, these miRNAs can bind shared and overlapping sets of gene targets; a user might want to identify the full impact of a family on these targets by assessing their collective peaks(refer to the command line in step 3.2).</li>
		<li>Next, indicate the full path to the reference directory, the miRBase abbreviation, and the reference genome abbreviation (refer to the command line in step 3.2).</li>
	</ol>
	</li>
	<li>Once peak calling is complete, run peak annotation. 
	&#8203;bash SCRAP/bin/Peak_Annotation.sh -p CLASH_Human/peaks.bed -r SCRAP/ -s human
	<ol>
		<li>List the full path to the resulting peaks.bed (or peaks.family.bed) file from peak calling, the full path to the reference directory, and the desired species for annotation.</li>
	</ol>
	</li>
</ol>
4. Visualizing the data
NOTE: All steps for analysis using SCRAP are now completed. For visualizing the data, several approaches are recommended:
<ol>
	<li>Merge all the .bam (binary SAM file) files that will be desired to visualize together (samtools merge).</li>
	<li>Sort the resulting merged .bam file (samtools sort). File contents are sorted line by line so that samtools may index.</li>
	<li>Index the sorted .bam file (samtools index). A BAI (binary samtools format index) file is generated to permit visualization in the integrative genomics viewer (IGV).</li>
	<li>Finally, open the resulting sorted .bam and indexed .bai file in IGV. 
	NOTE: SncRNA:Target RNA interactions of interest may be prioritized for follow-up in a number of investigation-specific ways. One generic initial approach is to assess the interactions for which peaks are supported by the most chimeric sequencing reads. Interactions of interest may also be visualized using the DuplexFold Web Server from the RNAstructure package by inputting the sequence for both the sncRNA and the target RNA from the detected interaction11. For each peak, the chromosome (first column) and genomic coordinates (start: 1st column end: 2nd column) can be found within the peaks.bed.species.annotation.txt file generated in peak annotation. For miRNAs in particular, while reproducible and functional interactions can lack extensive seed-matched binding (e.g., interactions may use 3&#39; compensatory binding), the presence of seed-matched sites in a cognate binding motif of the target RNA can nonetheless be assessed as a validating feature of functionally important detected interactions4,12. Ancillary data processing could include comparisons of differential read coverage between peaks in distinct biological conditions and potentially assessment of clustering of regulated genes into pathways using a pathway analysis tool.</li>
</ol>

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>

The authors have nothing to disclose.

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.

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

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

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.

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 ...

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

Universidad de Cartagena UDEC

Research

JoVE Journal

Biology

551 Views.  Johns Hopkins University School of Medicine. 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.

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

Competitive Genomic Screens of Barcoded Yeast Libraries

By virtue of advances in next generation sequencing technologies, we have access to new genome sequences almost daily. The tempo of these advances is accelerating, promising greater depth and breadth. In light of these extraordinary advances, the need for fast, parallel methods to define gene function becomes ever more important. Collections of genome-wide deletion mutants in yeasts and E. coli have served as workhorses for functional characterization of gene function, but this approach is not scalable, current gene-deletion approaches require each of the thousands of genes that comprise a genome to be deleted and verified. Only after this work is complete can we pursue high-throughput phenotyping. Over the past decade, our laboratory has refined a portfolio of competitive, miniaturized, high-throughput genome-wide assays that can be performed in parallel. This parallelization is possible because of the inclusion of DNA 'tags', or 'barcodes,' into each mutant, with the barcode serving as a proxy for the mutation and one can measure the barcode abundance to assess mutant fitness. In this study, we seek to fill the gap between DNA sequence and barcoded mutant collections. To accomplish this we introduce a combined transposon disruption-barcoding approach that opens up parallel barcode assays to newly sequenced, but poorly characterized microbes. To illustrate this approach we present a new Candida albicans barcoded disruption collection and describe how both microarray-based and next generation sequencing-based platforms can be used to collect 10,000 - 1,000,000 gene-gene and drug-gene interactions in a single experiment.

We have developed comprehensive, unbiased genome-wide screens to understand gene-drug and gene-environment interactions. Methods for screening these mutant collections are presented.

By virtue of advances in next generation sequencing technologies, we have access to new genome sequences almost daily.  The tempo of these advances is ...

Single Read and Paired End mRNA-Seq Illumina Libraries from 10 Nanograms Total RNA

Whole transcriptome sequencing by mRNA-Seq is now used extensively to perform global gene expression, mutation, allele-specific expression and other genome-wide analyses. mRNA-Seq even opens the gate for gene expression analysis of non-sequenced genomes. mRNA-Seq offers high sensitivity, a large dynamic range and allows measurement of transcript copy numbers in a sample. Illumina&#x2019;s genome analyzer performs sequencing of a large number (&gt; 107) of relatively short sequence reads (&lt; 150 bp).The &quot;paired end&quot; approach, wherein a single long read is sequenced at both its ends, allows for tracking alternate splice junctions, insertions and deletions, and is useful for de novo transcriptome assembly.
One of the major challenges faced by researchers is a limited amount of starting material. For example, in experiments where cells are harvested by laser micro-dissection, available starting total RNA may measure in nanograms. Preparation of mRNA-Seq libraries from such samples have been described1, 2 but involves significant PCR amplification that may introduce bias. Other RNA-Seq library construction procedures with minimal PCR amplification have been published3, 4 but require microgram amounts of starting total RNA.
Here we describe a protocol for the Illumina Genome Analyzer II platform for mRNA-Seq sequencing for library preparation that avoids significant PCR amplification and requires only 10 nanograms of total RNA. While this protocol has been described previously and validated for single-end sequencing5, where it was shown to produce directional libraries without introducing significant amplification bias, here we validate it further for use as a paired end protocol. We selectively amplify polyadenylated messenger RNAs from starting total RNA using the T7 based Eberwine linear amplification method, coined &quot;T7LA&quot; (T7 linear amplification). The amplified poly-A mRNAs are fragmented, reverse transcribed and adapter ligated to produce the final sequencing library. For both single read and paired end runs, sequences are mapped to the human transcriptome6 and normalized so that data from multiple runs can be compared. We report the gene expression measurement in units of transcripts per million (TPM), which is a superior measure to RPKM when comparing samples7.

Here we describe a method for preparation of both single read and paired end Illumina mRNA-Seq sequencing libraries for gene expression analysis based on T7 linear RNA amplification. This protocol requires only 10 nanograms of starting total RNA and generates highly consistent libraries representing whole transcripts.

Whole transcriptome sequencing by mRNA-Seq is now used extensively to perform global gene expression, mutation, allele-specific expression and other ...

In vitro Transcription and Capping of Gaussia Luciferase mRNA Followed by HeLa Cell Transfection

In vitro transcription is the synthesis of RNA transcripts by RNA polymerase from a linear DNA template containing the corresponding promoter sequence (T7, T3, SP6) and the gene to be transcribed (Figure 1A). A typical transcription reaction consists of the template DNA, RNA polymerase, ribonucleotide triphosphates, RNase inhibitor and buffer containing Mg2+ ions.
Large amounts of high quality RNA are often required for a variety of applications. Use of in vitro transcription has been reported for RNA structure and function studies such as splicing1, RNAi experiments in mammalian cells2, antisense RNA amplification by the "Eberwine method"3, microarray analysis4 and for RNA vaccine studies5. The technique can also be used for producing radiolabeled and dye labeled probes6. Warren, et al. recently reported reprogramming of human cells by transfection with in vitro transcribed capped RNA7. The T7 High Yield RNA Synthesis Kit from New England Biolabs has been designed to synthesize up to 180 &mu;g RNA per 20 &mu;l reaction. RNA of length up to 10kb has been successfully transcribed using this kit. Linearized plasmid DNA, PCR products and synthetic DNA oligonucleotides can be used as templates for transcription as long as they have the T7 promoter sequence upstream of the gene to be transcribed.
Addition of a 5' end cap structure to the RNA is an important process in eukaryotes. It is essential for RNA stability8, efficient translation9, nuclear transport10 and splicing11. The process involves addition of a 7-methylguanosine cap at the 5' triphosphate end of the RNA. RNA capping can be carried out post-transcriptionally using capping enzymes or co-transcriptionally using cap analogs. In the enzymatic method, the mRNA is capped using the Vaccinia virus capping enzyme12,13. The enzyme adds on a 7-methylguanosine cap at the 5' end of the RNA using GTP and S-adenosyl methionine as donors (cap 0 structure). Both methods yield functionally active capped RNA suitable for transfection or other applications14 such as generating viral genomic RNA for reverse-genetic systems15 and crystallographic studies of cap binding proteins such as eIF4E16.
In the method described below, the T7 High Yield RNA Synthesis Kit from NEB is used to synthesize capped and uncapped RNA transcripts of Gaussia luciferase (GLuc) and Cypridina luciferase (CLuc). A portion of the uncapped GLuc RNA is capped using the Vaccinia Capping System (NEB). A linearized plasmid containing the GLuc or CLuc gene and T7 promoter is used as the template DNA. The transcribed RNA is transfected into HeLa cells and cell culture supernatants are assayed for luciferase activity. Capped CLuc RNA is used as the internal control to normalize GLuc expression.

In vitro Transcription and Capping of Gaussia Luciferase mRNA Followed by HeLa Cell Transfection

This method describes high yield in vitro synthesis of both capped and uncapped mRNA from a linearized plasmid containing the Gaussia luciferase (GLuc) gene. The RNA is purified and a fraction of the uncapped RNA is enzymatically capped using the Vaccinia virus capping enzyme. In the final step, the mRNA is transfected into HeLa cells and cell culture supernatants are assayed for luciferase activity.

In vitro transcription is the synthesis of RNA transcripts by RNA polymerase from a linear DNA template containing the corresponding promoter sequence ...

Substrate Generation for Endonucleases of CRISPR/Cas Systems

The interaction of viruses and their prokaryotic hosts shaped the evolution of bacterial and archaeal life. Prokaryotes developed several strategies to evade viral attacks that include restriction modification, abortive infection and CRISPR/Cas systems. These adaptive immune systems found in many Bacteria and most Archaea consist of clustered regularly interspaced short palindromic repeat (CRISPR) sequences and a number of CRISPR associated (Cas) genes (Fig. 1) 1-3. Different sets of Cas proteins and repeats define at least three major divergent types of CRISPR/Cas systems 4. The universal proteins Cas1 and Cas2 are proposed to be involved in the uptake of viral DNA that will generate a new spacer element between two repeats at the 5' terminus of an extending CRISPR cluster 5. The entire cluster is transcribed into a precursor-crRNA containing all spacer and repeat sequences and is subsequently processed by an enzyme of the diverse Cas6 family into smaller crRNAs 6-8. These crRNAs consist of the spacer sequence flanked by a 5' terminal (8 nucleotides) and a 3' terminal tag derived from the repeat sequence 9. A repeated infection of the virus can now be blocked as the new crRNA will be directed by a Cas protein complex (Cascade) to the viral DNA and identify it as such via base complementarity10. Finally, for CRISPR/Cas type 1 systems, the nuclease Cas3 will destroy the detected invader DNA 11,12 .
These processes define CRISPR/Cas as an adaptive immune system of prokaryotes and opened a fascinating research field for the study of the involved Cas proteins. The function of many Cas proteins is still elusive and the causes for the apparent diversity of the CRISPR/Cas systems remain to be illuminated. Potential activities of most Cas proteins were predicted via detailed computational analyses. A major fraction of Cas proteins are either shown or proposed to function as endonucleases 4.
Here, we present methods to generate crRNAs and precursor-cRNAs for the study of Cas endoribonucleases. Different endonuclease assays require either short repeat sequences that can directly be synthesized as RNA oligonucleotides or longer crRNA and pre-crRNA sequences that are generated via in vitro T7 RNA polymerase run-off transcription. This methodology allows the incorporation of radioactive nucleotides for the generation of internally labeled endonuclease substrates and the creation of synthetic or mutant crRNAs. Cas6 endonuclease activity is utilized to mature pre-crRNAs into crRNAs with 5'-hydroxyl and a 2',3'-cyclic phosphate termini.

CRISPR/Cas systems mediate adaptive immunity in Bacteria and Archaea. Many Cas proteins are proposed to act as endoribonucleases acting on crRNA precursors of varying length. Here we illustrate three different approaches to generate pre-crRNA substrates for the biochemical analysis of Cas endonuclease activity.

The  interaction of viruses and their prokaryotic hosts shaped the evolution of  bacterial and archaeal life. Prokaryotes developed several strategies to ...

Depletion of Ribosomal RNA for Mosquito Gut Metagenomic RNA-seq

The mosquito gut accommodates dynamic microbial communities across different stages of the insect's life cycle. Characterization of the genetic capacity and functionality of the gut community will provide insight into the effects of gut microbiota on mosquito life traits. Metagenomic RNA-Seq has become an important tool to analyze transcriptomes from various microbes present in a microbial community. Messenger RNA usually comprises only 1-3% of total RNA, while rRNA constitutes approximately 90%. It is challenging to enrich messenger RNA from a metagenomic microbial RNA sample because most prokaryotic mRNA species lack stable poly(A) tails. This prevents oligo d(T) mediated mRNA isolation. Here, we describe a protocol that employs sample derived rRNA capture probes to remove rRNA from a metagenomic total RNA sample. To begin, both mosquito and microbial small and large subunit rRNA fragments are amplified from a metagenomic community DNA sample. Then, the community specific biotinylated antisense ribosomal RNA probes are synthesized in vitro using T7 RNA polymerase. The biotinylated rRNA probes are hybridized to the total RNA. The hybrids are captured by streptavidin-coated beads and removed from the total RNA. This subtraction-based protocol efficiently removes both mosquito and microbial rRNA from the total RNA sample. The mRNA enriched sample is further processed for RNA amplification and RNA-Seq.

A ribosomal RNA (rRNA) depletion protocol was developed to enrich messenger RNA (mRNA) for RNA-seq of the mosquito gut metatranscriptome. Sample specific rRNA probes, which were used to remove rRNA via subtraction, were created from the mosquito and its gut microbes. Performance of the protocol can result in the removal of approximately 90-99% of rRNA.

The mosquito gut accommodates dynamic microbial communities across different stages of the insect's life cycle. Characterization of the genetic capacity ...

Isolation of Small Noncoding RNAs from Human Serum

The analysis of RNA and its expression is a common feature in many laboratories. Of significance is the emergence of small RNAs like microRNAs, which are found in mammalian cells. These small RNAs are potent gene regulators controlling vital pathways such as growth, development and death and much interest has been directed at their expression in bodily fluids. This is due to their dysregulation in human diseases such as cancer and their potential application as serum biomarkers. However, the analysis of miRNA expression in serum may be problematic. In most cases the amount of serum is limiting and serum contains low amounts of total RNA, of which small RNAs only constitute 0.4-0.5%1. Thus the isolation of sufficient amounts of quality RNA from serum is a major challenge to researchers today. In this technical paper, we demonstrate a method which uses only 400 &#181;l of human serum to obtain sufficient RNA for either DNA arrays or qPCR analysis. The advantages of this method are its simplicity and ability to yield high quality RNA. It requires no specialized columns for purification of small RNAs and utilizes general reagents and hardware found in common laboratories. Our method utilizes a Phase Lock Gel to eliminate phenol contamination while at the same time yielding high quality RNA. We also introduce an additional step to further remove all contaminants during the isolation step. This protocol is very effective in isolating yields of total RNA of up to 100 ng/&#181;l from serum but can also be adapted for other biological tissues.

This protocol describes a method for extracting small RNAs from human serum. We have used this method to isolate microRNAs from cancer serum for use in DNA arrays and also singleplex quantitative PCR. The protocol utilizes phenol and guanidinium thiocyanate reagents with modifications to yield high quality RNA.

The analysis of RNA and its expression is a common feature in many laboratories. Of significance is the emergence of small RNAs like microRNAs, which are ...

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the long-lasting gold standards is to sequence 16S ribosomal RNA (rRNA) gene amplicons generated by domain-level PCR reactions amplifying from genomic DNA. Traditionally, this was performed by cloning and Sanger (capillary electrophoresis) sequencing of PCR amplicons. The advent of next-generation sequencing has tremendously simplified and increased the sequencing depth for 16S rRNA gene sequencing. The introduction of benchtop sequencers now allows small labs to perform their 16S rRNA sequencing in-house in a matter of days. Here, an approach for 16S rRNA gene amplicon sequencing using a benchtop next-generation sequencer is detailed. The environmental DNA is first amplified by PCR using primers that contain sequencing adapters and barcodes. They are then coupled to spherical particles via emulsion PCR. The particles are loaded on a disposable chip and the chip is inserted in the sequencing machine after which the sequencing is performed. The sequences are retrieved in fastq format, filtered and the barcodes are used to establish the sample membership of the reads. The filtered and binned reads are then further analyzed using publically available tools. An example analysis where the reads were classified with a taxonomy-finding algorithm within the software package Mothur is given. The method outlined here is simple, inexpensive and straightforward and should help smaller labs to take advantage from the ongoing genomic revolution.

Characterizing microbial community has been a longstanding goal in environmental microbiology. Next-generation sequencing methods now allow for the characterization of microbial communities at an unprecedented depth with minimal cost and labor. We detail here our approach to sequence bacterial 16S ribosomal RNA genes using a benchtop sequencer.

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the ...

Enhanced Northern Blot Detection of Small RNA Species in Drosophila Melanogaster

The last decades have witnessed the explosion of scientific interest around gene expression control mechanisms at the RNA level. This branch of molecular biology has been greatly fueled by the discovery of noncoding RNAs as major players in post-transcriptional regulation. Such a revolutionary perspective has been accompanied and triggered by the development of powerful technologies for profiling short RNAs expression, both at the high-throughput level (genome-wide identification) or as single-candidate analysis (steady state accumulation of specific species). Although several state-of-art strategies are currently available for dosing or visualizing such fleeing molecules, Northern Blot assay remains the eligible approach in molecular biology for immediate and accurate evaluation of RNA expression. It represents a first step toward the application of more sophisticated, costly technologies and, in many cases, remains a preferential method to easily gain insights into RNA biology. Here we overview an efficient protocol (Enhanced Northern Blot) for detecting weakly expressed microRNAs (or other small regulatory RNA species) from Drosophila melanogaster whole embryos, manually dissected larval/adult tissues or in vitro cultured cells. A very limited amount of RNA is required and the use of material from flow cytometry-isolated cells can be also envisaged.

Enhanced Northern Blot Detection of Small RNA Species in Drosophila Melanogaster

The aim of this publication is to visualize and discuss the operative steps of an Enhanced Northern Blot protocol on RNA extracted from Drosophila melanogaster embryos, cells, and tissues. This protocol is particularly useful for the efficient detection of small RNA species.

The last decades have witnessed the explosion of scientific interest around gene expression control mechanisms at the RNA level. This branch of molecular ...

Highly Efficient Ligation of Small RNA Molecules for MicroRNA Quantitation by High-Throughput Sequencing

MiRNA cloning and high-throughput sequencing, termed miR-Seq, stands alone as a transcriptome-wide approach to quantify miRNAs with single nucleotide resolution. This technique captures miRNAs by attaching 3&#8217; and 5&#8217; oligonucleotide adapters to miRNA molecules and allows de novo miRNA discovery. Coupling with powerful next-generation sequencing platforms, miR-Seq has been instrumental in the study of miRNA biology. However, significant biases introduced by oligonucleotide ligation steps have prevented miR-Seq from being employed as an accurate quantitation tool. Previous studies demonstrate that biases in current miR-Seq methods often lead to inaccurate miRNA quantification with errors up to 1,000-fold for some miRNAs1,2. To resolve these biases imparted by RNA ligation, we have developed a small RNA ligation method that results in ligation efficiencies of over 95% for both 3&#8217; and 5&#8242; ligation steps. Benchmarking this improved library construction method using equimolar or differentially mixed synthetic miRNAs, consistently yields reads numbers with less than two-fold deviation from the expected value. Furthermore, this high-efficiency miR-Seq method permits accurate genome-wide miRNA profiling from in vivo total RNA samples2.

MicroRNAs (miRNAs) are a widely conserved class of regulatory molecules. Here we describe a miRNA cloning method that relies upon two potent ligation steps followed by high-throughput sequencing. Our method permits accurate genome-wide quantitation of miRNAs.

MiRNA cloning and high-throughput sequencing, termed miR-Seq, stands alone as a transcriptome-wide approach to quantify miRNAs with single nucleotide ...

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Hematopoietic stem cells (HSCs) are used clinically for transplantation treatment to rebuild a patient&#39;s hematopoietic system in many diseases such as leukemia and lymphoma. Elucidating the mechanisms controlling HSCs self-renewal and differentiation is important for application of HSCs for research and clinical uses. However, it is not possible to obtain large quantity of HSCs due to their inability to proliferate in vitro. To overcome this hurdle, we used a mouse bone marrow derived cell line, the EML (Erythroid, Myeloid, and Lymphocytic) cell line, as a model system for this study.
RNA-sequencing (RNA-Seq) has been increasingly used to replace microarray for gene expression studies. We report here a detailed method of using RNA-Seq technology to investigate the potential key factors in regulation of EML cell self-renewal and differentiation. The protocol provided in this paper is divided into three parts. The first part explains how to culture EML cells and separate Lin-CD34+ and Lin-CD34- cells. The second part of the protocol offers detailed procedures for total RNA preparation and the subsequent library construction for high-throughput sequencing. The last part describes the method for RNA-Seq data analysis and explains how to use the data to identify differentially expressed transcription factors between Lin-CD34+ and Lin-CD34- cells. The most significantly differentially expressed transcription factors were identified to be the potential key regulators controlling EML cell self-renewal and differentiation. In the discussion section of this paper, we highlight the key steps for successful performance of this experiment.
In summary, this paper offers a method of using RNA-Seq technology to identify potential regulators of self-renewal and differentiation in EML cells. The key factors identified are subjected to downstream functional analysis in vitro and in vivo.

RNA-sequencing and bioinformatics analyses were used to identify significantly and differentially expressed transcription factors in Lin-CD34+ and Lin-CD34- subpopulations of mouse EMLcells. These transcription factors might play important roles in determining the switch between self-renewing Lin-CD34+ and partially differentiated Lin-CD34- cells.

Hematopoietic stem cells (HSCs) are used clinically for transplantation treatment to rebuild a patient&#39;s hematopoietic system in many diseases such as ...

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

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Competitive Genomic Screens of Barcoded Yeast Libraries

Single Read and Paired End mRNA-Seq Illumina Libraries from 10 Nanograms Total RNA

In vitro Transcription and Capping of Gaussia Luciferase mRNA Followed by HeLa Cell Transfection

Substrate Generation for Endonucleases of CRISPR/Cas Systems

Depletion of Ribosomal RNA for Mosquito Gut Metagenomic RNA-seq

Isolation of Small Noncoding RNAs from Human Serum

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Enhanced Northern Blot Detection of Small RNA Species in Drosophila Melanogaster

Highly Efficient Ligation of Small RNA Molecules for MicroRNA Quantitation by High-Throughput Sequencing

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis