All illustrated figures were created with BioRender.com. CAI acknowledges support provided through an Ovarian Cancer Research Alliance Early Career Investigator Award, a Forbeck Foundation Accelerator Grant, and the Minnestoa Ovarian Cancer Alliance National Early Detection Research Award.

Beginner-Friendly Single-Language Pipeline for CUT&RUN Data Analysis

<ol>
	<li>Hainer, S. J., Fazzio, T. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-resolution+chromatin+profiling+using+CUT&amp;RUN.">High-resolution chromatin profiling using CUT&amp;RUN.</a> Curr Protoc Mol Biol. 126 (1), e85 (2019).</li><li>Zhang, Y., 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=Model-based+analysis+of+ChiP-Seq+(MACS).">Model-based analysis of ChiP-Seq (MACS).</a> Genome Biology. 9 (9), R137 (2008).</li><li>Xu, S., Grullon, S., Ge, K., Peng, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Stem cell transcriptional networks: Methods and Protocols. , (2014).</li><li>Meers, M. P., Tenenbaum, D., Henikoff, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Peak+calling+by+sparse+enrichment+analysis+for+cut&amp;run+chromatin+profiling.">Peak calling by sparse enrichment analysis for cut&amp;run chromatin profiling.</a> Epigenetics Chromatin. 12 (1), 42 (2019).</li><li>Ashburner, M., 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=Gene+ontology:+Tool+for+the+unification+of+biology.+The+gene+ontology+consortium.">Gene ontology: Tool for the unification of biology. The gene ontology consortium.</a> Nat Genet. 25 (1), 25-29 (2000).</li><li>Harris, M. A., 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=The+gene+ontology+(GO)+database+and+informatics+resource.">The gene ontology (GO) database and informatics resource.</a> Nucleic Acids Res. 32 (Database issue), D258-D261 (2004).</li><li>The Gene Ontology Consortium. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+gene+ontology+resource:+20+years+and+still+going+strong.">The gene ontology resource: 20 years and still going strong.</a> Nucleic Acids Res. 47 (D1), D330-D338 (2019).</li><li>Conesa, A., 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=Blast2go:+A+universal+tool+for+annotation,+visualization+and+analysis+in+functional+genomics+research.">Blast2go: A universal tool for annotation, visualization and analysis in functional genomics research.</a> Bioinformatics. 21 (18), 3674-3676 (2005).</li><li>Carbon, 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=AmiGO:+Online+access+to+ontology+and+annotation+data.">AmiGO: Online access to ontology and annotation data.</a> Bioinformatics. 25 (2), 288-289 (2009).</li><li>Eden, E., Navon, R., Steinfeld, I., Lipson, D., Yakhini, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gorilla:+A+tool+for+discovery+and+visualization+of+enriched+go+terms+in+ranked+gene+lists.">Gorilla: A tool for discovery and visualization of enriched go terms in ranked gene lists.</a> BMC Bioinformatics. 10, 48 (2009).</li><li>Huang Da, W., Sherman, B. T., Lempicki, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bioinformatics+enrichment+tools:+Paths+toward+the+comprehensive+functional+analysis+of+large+gene+lists.">Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists.</a> Nucleic Acids Res. 37 (1), 1-13 (2009).</li><li>Huang Da, W., Sherman, B. T., Lempicki, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+and+integrative+analysis+of+large+gene+lists+using+david+bioinformatics+resources.">Systematic and integrative analysis of large gene lists using david bioinformatics resources.</a> Nat Protoc. 4 (1), 44-57 (2009).</li><li>Ge, S. X., Jung, D., Yao, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ShinyGO:+A+graphical+gene-set+enrichment+tool+for+animals+and+plants.">ShinyGO: A graphical gene-set enrichment tool for animals and plants.</a> Bioinformatics. 36 (8), 2628-2629 (2020).</li><li>Tang, D., 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=SRplot:+A+free+online+platform+for+data+visualization+and+graphing.">SRplot: A free online platform for data visualization and graphing.</a> PLoS One. 18 (11), e0294236 (2023).</li><li>Ram&#237;rez, F., 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=Deeptools2:+A+next+generation+web+server+for+deep-sequencing+data+analysis.">Deeptools2: A next generation web server for deep-sequencing data analysis.</a> Nucleic Acids Res. 44 (W1), W160-W165 (2016).</li><li>Robinson, J. T., 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=Integrative+genomics+viewer.">Integrative genomics viewer.</a> Nat Biotechnol. 29 (1), 24-26 (2011).</li><li>Kent, W. 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=The+human+genome+browser+at+ucsc.">The human genome browser at ucsc.</a> Genome Res. 12 (6), 996-1006 (2002).</li><li>Yu, F., Sankaran, V. G., Yuan, G. -. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CUT&amp;RUNTools+2.0:+A+pipeline+for+single-cell+and+bulk-level+CUT&amp;RUN+and+CUT&amp;Tag+data+analysis.">CUT&amp;RUNTools 2.0: A pipeline for single-cell and bulk-level CUT&amp;RUN and CUT&amp;Tag data analysis.</a> Bioinformatics. 38 (1), 252-254 (2021).</li><li>Zhu, Q., Liu, N., Orkin, S. H., Yuan, G. -. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CUT&amp;RUNTools:+A+flexible+pipeline+for+CUT&amp;RUN+processing+and+footprint+analysis.">CUT&amp;RUNTools: A flexible pipeline for CUT&amp;RUN processing and footprint analysis.</a> Genome Biol. 20 (1), 192 (2019).</li><li>. Nf-core/cutandrun: Nf-core/cutandrun v3.2.2 iridium ibis Available from: <a target="_blank" href="https://github.com/nf-core/cutandrun/tree/3.2.2">https://github.com/nf-core/cutandrun/tree/3.2.2</a> (2024)</li><li>Kong, N. R., Chai, L., Tenen, D. G., Bassal, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+modified+CUT&amp;RUN+protocol+and+analysis+pipeline+to+identify+transcription+factor+binding+sites+in+human+cell+lines.">A modified CUT&amp;RUN protocol and analysis pipeline to identify transcription factor binding sites in human cell lines.</a> STAR Protoc. 2 (3), 100750 (2021).</li><li>Meers, M. P., Bryson, T. D., Henikoff, J. G., Henikoff, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+CUT&amp;RUN+chromatin+profiling+tools.">Improved CUT&amp;RUN chromatin profiling tools.</a> eLife. 8, e46314 (2019).</li><li>Amemiya, H. M., Kundaje, A., Boyle, A. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+encode+blacklist:+Identification+of+problematic+regions+of+the+genome.">The encode blacklist: Identification of problematic regions of the genome.</a> Sci Rep. 9 (1), 9354 (2019).</li><li>Deberardine, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BRgenomics+for+analyzing+high-resolution+genomics+data+in+R.">BRgenomics for analyzing high-resolution genomics data in R.</a> Bioinformatics. 39 (6), btad331 (2023).</li><li>Deberardine, M., Booth, G. T., Versluis, P. P., Lis, J. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+nelf+pausing+checkpoint+mediates+the+functional+divergence+of+cdk9.">The nelf pausing checkpoint mediates the functional divergence of cdk9.</a> Nat Commun. 14 (1), 2762 (2023).</li><li>Krueger, F., James, F. O., Ewels, P. A., Afyounian, E., Schuster-Boeckler, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> FelixKrueger/TrimGalore: v0.6.7 - DOI via Zenodo. , (2021).</li><li>. Easy bam downsampling Available from: <a target="_blank" href="https://davemcg.github.io/post/easy-bam-downsampling/">https://davemcg.github.io/post/easy-bam-downsampling/</a> (2018)</li><li>. Positionbaseddownsamplesam (picard) Available from: <a target="_blank" href="https://gatk.broadinstitute.org/hc/en-us/articles/360041850311-PositionBasedDownsampleSam-Picard">https://gatk.broadinstitute.org/hc/en-us/articles/360041850311-PositionBasedDownsampleSam-Picard</a> (2020)</li></ol>

The authors declare no disclosures.

The ability to map protein occupancy on chromatin is fundamental to conducting mechanistic studies in the field of chromatin biology. As laboratories adopt new wet lab techniques to profile chromatin, the ability to analyze sequencing data from those wet lab experiments becomes a common bottleneck for wet lab scientists. Therefore, we describe an introductory step-by-step protocol to enable bioinformatics beginners to overcome the analysis bottleneck, and initiate analysis and quality control checks of their own CUT&amp;...

The ability to measure interactions between proteins and genomic DNA is fundamental to understanding the biology of chromatin regulation. Effective assays that measure chromatin occupancy for a given protein provide at least two key pieces of information: i) genomic localization and ii) protein abundance at a given genomic region. Tracking the recruitment and localization changes of a protein of interest in chromatin can reveal direct target loci of the protein and reveal mechanistic roles of that protein in chromatin-based biological processes such as regulation of transcription, DNA repair, or DNA replication. The techniques available today to profile protein-DNA interactions are enabling researchers to explore regulation at unprecedented resolution. Such technical advances have been enabled through the introduction of new chromatin profiling techniques that include the development of Cleavage Under Targets and Release Using Nuclease (CUT&#38;RUN) by the Henikoff laboratory. CUT&#38;RUN offers several technical advantages over conventional chromatin immunoprecipitation (ChIP) that include lower cell input requirements, lower sequencing depth requirements, and increased sensitivity with reduced background signal due to a lack of cross-linking agents that otherwise mask antibody epitopes. Adopting this technique to study chromatin regulation requires a thorough understanding of the principle underlying the technique, and an understanding of how to analyze, validate, and interpret CUT&#38;RUN data.
The CUT&#38;RUN procedure begins with binding of cells to Concanavalin A conjugated to magnetic beads to enable manipulation of low cell numbers throughout the procedure. Isolated cells are permeabilized using a mild detergent to facilitate introduction of an antibody that targets the protein of interest. Micrococcal nuclease (MNase) is then recruited to the bound antibody using a Protein A or Protein A/G tag tethered to the enzyme. Calcium is introduced to initiate enzymatic activity. MNase digestion results in mono-nucleosomal DNA-protein complexes. Calcium is subsequently chelated to end the digestion reaction, and short DNA fragments from the MNase digestion are released from nuclei, then subjected to DNA purification, library preparation, and high-throughput sequencing1 (Figure 1).
In silico approaches to map and quantify protein occupancy across the genome have developed in parallel with the wet lab approaches used to enrich those DNA-protein interactions. Identification of regions of enriched signals (peaks) is one of the most critical steps in the bioinformatics analysis. Initial ChIP-seq analysis methods used algorithms such as MACS2 and SICER3, which employed statistical models to distinguish bona fide protein-DNA binding sites from background noise. However, the lower background noise and higher resolution of CUT&#38;RUN data render some peak calling programs employed in ChIP-seq analysis unsuitable for CUT&#38;RUN analysis4. This challenge highlights the need for new tools better suited for the analysis of CUT&#38;RUN data. SEACR4 represents one such tool recently developed to enable peak calling from CUT&#38;RUN data while overcoming limitations associated with tools typically employed toward ChIP-seq analysis.
Biological interpretations from CUT&#38;RUN sequencing data are drawn from the outputs downstream of peak calling in the analysis pipeline. Several functional annotation programs can be implemented to predict the potential biological relevance of the called peaks from CUT&#38;RUN data. For example, the Gene Ontology (GO) project provides well-established functional identification of genes of interest5,6,7. Various software tools and resources facilitate GO analysis to reveal genes and gene sets enriched amongst CUT&#38;RUN peaks8,9,10,11,12,13,14. Furthermore, visualization software such as Deeptools15, Integrative genomics viewer (IGV)16, and UCSC Genome Browser17 enable visualization of signal distribution and patterns at regions of interest across the genome.
The ability to draw biological interpretations from CUT&#38;RUN data depends critically upon validation of data quality. Critical components to validate include the assessment of: i) CUT&#38;RUN library sequencing quality, ii) replicate similarity, and iii) signal distribution at peak centers. Completing the validation of all three components is crucial to ensure the reliability of CUT&#38;RUN library samples and downstream analysis results. Therefore, it is essential to establish introductory CUT&#38;RUN analysis guides to enable bioinformatics beginners and wet lab researchers to conduct such validation steps as part of their standard CUT&#38;RUN analysis pipelines.
Alongside the development of wet lab CUT&#38;RUN experiment, various in silico CUT&#38;RUN analysis pipelines, such as CUT&#38;RUNTools 2.018,19, nf-core/cutandrun20, and CnRAP21, have been developed to support CUT&#38;RUN data analysis. These tools provide powerful approaches to analyzing single-cell and bulk CUT&#38;RUN and CUT&#38;Tag datasets. However, the relatively complex modular program structure and required familiarity with multiple programming languages to conduct these analysis pipelines may hinder adoption by bioinformatics beginners who seek to thoroughly understand the CUT&#38;RUN analysis steps and customize their own pipelines. Circumvention of this barrier requires a new introductory CUT&#38;RUN analysis pipeline that is provided in simple step-by-step scripts encoded using a simple single programming language.
In this article, we describe a simple single-language CUT&#38;RUN analysis pipeline protocol that provides step-by-step scripts supported with detailed descriptions to enable new and novice users to conduct CUT&#38;RUN sequencing analysis. Programs used in this pipeline are publicly available by the original developer groups. Major steps described in this protocol include read alignment, peak calling, functional analysis and, most critically, validation steps to assess sample quality to determine data suitability and reliability for biological interpretation (Figure 2). Furthermore, this pipeline provides users the opportunity to cross-reference analysis results against publicly available CUT&#38;RUN datasets. Ultimately, this CUT&#38;RUN analysis pipeline protocol serves as an introductory guide and reference for bioinformatic analysis beginners and wet lab researchers.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>bedGraphToBigWig</td>
			<td>ENCODE</td>
			<td>https://hgdownload.soe.ucsc.edu/admin/exe/</td>
			<td>Software to compress and convert readcounts bedGraph to bigWig</td>
		</tr>
		<tr>
			<td>bedtools-2.31.1</td>
			<td>The Quinlan Lab @ the U. of Utah</td>
			<td>https://bedtools.readthedocs.io/en/latest/index.html</td>
			<td>Software to process bam/bed/bedGraph files</td>
		</tr>
		<tr>
			<td>bowtie2 2.5.4</td>
			<td>Johns Hopkins University</td>
			<td>https://bowtie-bio.sourceforge.net/bowtie2/index.shtml</td>
			<td>Software to build bowtie index and perform alignment</td>
		</tr>
		<tr>
			<td>CollectInsertSizeMetrics (Picard)</td>
			<td>Broad institute</td>
			<td>https://github.com/broadinstitute/picard</td>
			<td>Software to perform insert size distribution analysis</td>
		</tr>
		<tr>
			<td>Cutadapt</td>
			<td>NBIS</td>
			<td>https://cutadapt.readthedocs.io/en/stable/index.html</td>
			<td>Software to perform adapter trimming</td>
		</tr>
		<tr>
			<td>Deeptoolsv3.5.1</td>
			<td>Max Planck Institute</td>
			<td>https://deeptools.readthedocs.io/en/develop/index.html</td>
			<td>Software to perform Pearson coefficient correlation analysis, Principal component analysis, and Heatmap/average plot analysis</td>
		</tr>
		<tr>
			<td>FastQC Version 0.12.0</td>
			<td>Babraham Bioinformatics</td>
			<td>https://github.com/s-andrews/FastQC</td>
			<td>Software to check quality of fastq file</td>
		</tr>
		<tr>
			<td>Intervenev0.6.1</td>
			<td>Computational Biology &#38; Gene regulation - Mathelier group</td>
			<td>https://intervene.readthedocs.io/en/latest/index.html</td>
			<td>Software to perform venn diagram analysis using peak files</td>
		</tr>
		<tr>
			<td>MACSv2.2.9.1</td>
			<td>Chan Zuckerberg initiative</td>
			<td>https://github.com/macs3-project/MACS/tree/macs_v2</td>
			<td>Software to call peaks</td>
		</tr>
		<tr>
			<td>MACSv3.0.2</td>
			<td>Chan Zuckerberg initiative</td>
			<td>https://github.com/macs3-project/MACS/tree/master</td>
			<td>Software to call peaks</td>
		</tr>
		<tr>
			<td>Samtools-1.21</td>
			<td>Wellcome Sanger Institute</td>
			<td>https://github.com/samtools/samtools</td>
			<td>Software to process sam/bam files</td>
		</tr>
		<tr>
			<td>SEACRv1.3</td>
			<td>Howard Hughes Medial institute</td>
			<td>https://github.com/FredHutch/SEACR</td>
			<td>Software to call peaks</td>
		</tr>
		<tr>
			<td>SRA Toolkit Release 3.1.1</td>
			<td>NCBI</td>
			<td>https://github.com/ncbi/sra-tools</td>
			<td>Software to download SRR from GEO</td>
		</tr>
		<tr>
			<td>Trim_Galore v0.6.10</td>
			<td>Babraham Bioinformatics</td>
			<td>https://github.com/FelixKrueger/TrimGalore</td>
			<td>Software to perform quality and atapter trimming</td>
		</tr>
	</tbody>
</table>

introductory analysis and validation of cut&#38;run sequencing data

The CUT&amp;RUN technique facilitates detection of protein-DNA interactions across the genome. Typical applications of CUT&amp;RUN include profiling changes in histone tail modifications or mapping transcription factor chromatin occupancy. Widespread adoption of CUT&amp;RUN is driven, in part, by technical advantages over conventional ChIP-seq that include lower cell input requirements, lower sequencing depth requirements, and increased sensitivity with reduced background signal due to a lack of cross-linking agents that otherwise mask antibody epitopes. Widespread adoption of CUT&amp;RUN has also been achieved through the generous sharing of reagents by the Henikoff lab and the development of commercial kits to accelerate adoption for beginners. As technical adoption of CUT&amp;RUN increases, CUT&amp;RUN sequencing analysis and validation become critical bottlenecks that must be surmounted to enable complete adoption by predominantly wet lab teams. CUT&amp;RUN analysis typically begins with quality control checks on raw sequencing reads to assess sequencing depth, read quality, and potential biases. Reads are then aligned to a reference genome sequence assembly, and several bioinformatics tools are subsequently employed to annotate genomic regions of protein enrichment, confirm data interpretability, and draw biological conclusions. Although multiple in silico analysis pipelines have been developed to support CUT&amp;RUN data analysis, their complex multi-module structure and usage of multiple programming languages render the platforms difficult for bioinformatics beginners who may lack familiarity with multiple programming languages but wish to understand the CUT&amp;RUN analysis procedure and customize their analysis pipelines. Here, we provide a single-language step-by-step CUT&amp;RUN analysis pipeline protocol designed for users with any level of bioinformatics experience. This protocol includes completing critical quality checks to validate that the sequencing data is suitable for biological interpretation. We expect that following the introductory protocol provided in this article combined with downstream peak annotation will allow users to draw biological insights from their own CUT&amp;RUN datasets.

Quality and adapter trimming retains reads with high sequencing quality 
High-throughput sequencing techniques are prone to generating sequencing errors such as sequence &#39;mutations&#39; in reads. Furthermore, sequencing adapter dimers can be enriched in sequencing datasets due to poor adapter removal during library preparation. Excessive sequencing errors, such as read mutations, generation of reads shorter than required for proper mapping, and enrichment of adapter dimers, can increase read map...

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

This protocol guides bioinformatics beginners through an introductory CUT&amp;RUN analysis pipeline that enables users to complete an initial analysis and validation of CUT&amp;RUN sequencing data. Completing the analysis steps described here, combined with downstream peak annotation, will allow users to draw mechanistic insights into chromatin regulation.

Introductory Analysis and Validation of CUT&#38;RUN Sequencing Data

The CUT&amp;RUN technique facilitates detection of protein-DNA interactions across the genome. Typical applications of CUT&amp;RUN include profiling ...

introductory-analysis-and-validation-of-cut-run-sequencing-data

Research

JoVE Journal

Genetics

1.3K Views.  University of Texas MD Anderson Cancer Center. This protocol guides bioinformatics beginners through an introductory CUT&RUN analysis pipeline that enables users to complete an initial analysis and validation of CUT&RUN sequencing data. Completing the analysis steps described here, combined with downstream peak annotation, will allow users to draw mechanistic insights into chromatin regulation.

Video: Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged as a powerful tool for gene-expression analysis and transcriptome mapping. However, handling RNA-Seq datasets requires sophisticated computational expertise and poses inherent challenges for biology researchers. This bottleneck has been mitigated by the open access Galaxy project that allows users without bioinformatics skills to analyze RNA-Seq data, and the Database for Annotation, Visualization, and Integrated Discovery (DAVID), a Gene Ontology (GO) term analysis suite that helps derive biological meaning from large data sets. However, for first-time users and bioinformatics' amateurs, self-learning and familiarization with these platforms can be time-consuming and daunting. We describe a straightforward workflow that will help C. elegans researchers to isolate worm RNA, conduct an RNA-Seq experiment and analyze the data using Galaxy and DAVID platforms. This protocol provides stepwise instructions for using the various Galaxy modules for accessing raw NGS data, quality-control checks, alignment, and differential gene expression analysis, guiding the user with parameters at every step to generate a gene list that can be screened for enrichment of gene classes or biological processes using DAVID. Overall, we anticipate that this article will provide information to C. elegans researchers undertaking RNA-Seq experiments for the first time as well as frequent users running a small number of samples.

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Galaxy and DAVID have emerged as popular tools that allow investigators without bioinformatics training to analyze and interpret RNA-Seq data. We describe a protocol for C. elegans researchers to perform RNA-Seq experiments, access and process the dataset using Galaxy and obtain meaningful biological information from the gene lists using DAVID.

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged ...

Optimization and Comparative Analysis of Plant Organellar DNA Enrichment Methods Suitable for Next-generation Sequencing

Plant organellar genomes contain large, repetitive elements that may undergo pairing or recombination to form complex structures and/or sub-genomic fragments. Organellar genomes also exist in admixtures within a given cell or tissue type (heteroplasmy), and an abundance of subtypes may change throughout development or when under stress (sub-stoichiometric shifting). Next-generation sequencing (NGS) technologies are required to obtain deeper understanding of organellar genome structure and function. Traditional sequencing studies use several methods to obtain organellar DNA: (1) If a large amount of starting tissue is used, it is homogenized and subjected to differential centrifugation and/or gradient purification. (2) If a smaller amount of tissue is used (i.e., if seeds, material, or space is limited), the same process is performed as in (1), followed by whole-genome amplification to obtain sufficient DNA. (3) Bioinformatics analysis can be used to sequence the total genomic DNA and to parse out organellar reads. All these methods have inherent challenges and tradeoffs. In (1), it may be difficult to obtain such a large amount of starting tissue; in (2), whole-genome amplification could introduce a sequencing bias; and in (3), homology between nuclear and organellar genomes could interfere with assembly and analysis. In plants with large nuclear genomes, it is advantageous to enrich for organellar DNA to reduce sequencing costs and sequence complexity for bioinformatics analyses. Here, we compare a traditional differential centrifugation method with a fourth method, an adapted CpG-methyl pulldown approach, to separate the total genomic DNA into nuclear and organellar fractions. Both methods yield sufficient DNA for NGS, DNA that is highly enriched for organellar sequences, albeit at different ratios in mitochondria and chloroplasts. We present the optimization of these methods for wheat leaf tissue and discuss major advantages and disadvantages of each approach in the context of sample input, protocol ease, and downstream application.

The comparison and optimization of two plant organellar DNA enrichment methods are presented: traditional differential centrifugation and fractionation of the total gDNA based on methylation status. We assess the resulting DNA quantity and quality, demonstrate performance in short-read next-generation sequencing, and discuss the potential for use in long-read single-molecule sequencing.

Plant organellar genomes contain large, repetitive elements that may undergo pairing or recombination to form complex structures and/or sub-genomic ...

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow

Integration Site (IS) assays are a critical component of the study of retroviral integration sites and their biological significance. In recent retroviral gene therapy studies, IS assays, in combination with next-generation sequencing, have been used as a cell-tracking tool to characterize clonal stem cell populations sharing the same IS. For the accurate comparison of repopulating stem cell clones within and across different samples, the detection sensitivity, data reproducibility, and high-throughput capacity of the assay are among the most important assay qualities. This work provides a detailed protocol and data analysis workflow for bidirectional IS analysis. The bidirectional assay can simultaneously sequence both upstream and downstream vector-host junctions. Compared to conventional unidirectional IS sequencing approaches, the bidirectional approach significantly improves IS detection rates and the characterization of integration events at both ends of the target DNA. The data analysis pipeline described here accurately identifies and enumerates identical IS sequences through multiple steps of comparison that map IS sequences onto the reference genome and determine sequencing errors. Using an optimized assay procedure, we have recently published the detailed repopulation patterns of thousands of Hematopoietic Stem Cell (HSC) clones following transplant in rhesus macaques, demonstrating for the first time the precise time point of HSC repopulation and the functional heterogeneity of HSCs in the primate system. The following protocol describes the step-by-step experimental procedure and data analysis workflow that accurately identifies and quantifies identical IS sequences.

This manuscript describes the experimental procedure and software analysis for a bidirectional integration site assay that can simultaneously analyze upstream and downstream vector-host junction DNA. Bidirectional PCR products can be used for any downstream sequencing platform. The resulting data are useful for a high-throughput, quantitative comparison of integrated DNA targets.

Integration Site (IS) assays are a critical component of the study of retroviral integration sites and their biological significance. In recent retroviral ...

Generation of Native Chromatin Immunoprecipitation Sequencing Libraries for Nucleosome Density Analysis

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) experimental protocol compatible with a Gaussian mixture distribution based analysis methodology (nucleosome density ChIP-seq; ndChIP-seq) that enables the generation of combined measurements of micrococcal nuclease (MNase) accessibility with histone modification genome-wide. Nucleosome position and local density, and the posttranslational modification of their histone subunits, act in concert to regulate local transcription states. Combinatorial measurements of nucleosome accessibility with histone modification generated by ndChIP-seq allows for the simultaneous interrogation of these features. The ndChIP-seq methodology is applicable to small numbers of primary cells inaccessible to cross-linking based ChIP-seq protocols. Taken together, ndChIP-seq enables the measurement of histone modification in combination with local nucleosome density to obtain new insights into shared mechanisms that regulate RNA transcription within rare primary cell populations.

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) methodology for the generation of sequence datasets suitable for a nucleosome density ChIP-seq analytical framework integrating micrococcal nuclease (MNase) accessibility with histone modification measurements.

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) experimental protocol compatible with a Gaussian mixture distribution ...

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an excellent model for rapid assessment of whole transcriptome from whole animal or individual cell populations due to the ease of isolation of RNA from large numbers of animals. Here a protocol for global gene expression analysis in zebrafish embryos using RNA sequencing (RNASeq) is presented. We describe preparation of RNA from whole embryos or from cell populations obtained using cell sorting in transgenic animals. We also describe an approach for analysis of RNASeq data to identify enriched pathways and Gene Ontology (GO) terms in global gene expression data sets. Finally, we provide a protocol for validation of gene expression changes using quantitative reverse transcriptase PCR (qRT-PCR). These protocols can be used for comparative analysis of control and experimental sets of zebrafish to identify novel gene expression changes, and provide molecular insight into phenotypes of interest.

This protocol presents an approach for whole transcriptome analysis from zebrafish embryos, larvae, or sorted cells. We include isolation of RNA, pathway analysis of RNASeq data, and qRT-PCR-based validation of gene expression changes.

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an ...

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing high-throughput short-read DNA sequencing. The resulting sequence datasets can not only track what genes in a population that are enriched during selection for positive yeast 2-hybrid interactions, but also give detailed information about the relevant subdomains of proteins sufficient for interaction. Here, we describe a full suite of stand-alone software programs that allow non-experts to perform all the bioinformatics and statistical steps to process and analyze DNA sequence fastq files from a batch yeast 2-hybrid assay. The processing steps covered by these software include: 1) mapping and counting sequence reads corresponding to each candidate protein encoded within a yeast 2-hybrid prey library; 2) a statistical analysis program that evaluates the enrichment profiles; and 3) tools to examine the translational frame and position within the coding region of each enriched plasmid that encodes the interacting proteins of interest.

Deep sequencing of yeast populations selected for positive yeast 2-hybrid interactions potentially yields a wealth of information about interacting partner proteins. Here, we describe the operation of specific bioinformatics tools and customized updated software to analyze sequence data from such screens.

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing ...

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing in remote locations and resource-poor settings presents unique challenges. Nanopore sequencing can be successfully used under such conditions, and was deployed to West Africa during the recent Ebola virus epidemic, highlighting this possibility. In addition to its practical advantages (low cost, ease of equipment transport and use), this technology also provides fundamental advantages over second-generation sequencing approaches, particularly the very long read length, ability to directly sequence RNA, and real-time availability of data. Raw read accuracy is lower than with other sequencing platforms, which represents the main limitation of this technology; however, this can be partially mitigated by the high read depth generated. Here, we present a field-compatible protocol for sequencing of the mRNAs encoding for Niemann-Pick C1, which is the cellular receptor for ebolaviruses. This protocol encompasses extraction of RNA from animal blood samples, followed by RT-PCR for target enrichment, barcoding, library preparation, and the sequencing run itself, and can be easily adapted for use with other DNA or RNA targets.

Nanopore sequencing is a novel technology that allows cost-effective sequencing in remote locations and resource-poor settings. Here, we present a protocol for sequencing of mRNAs from whole blood that is compatible with such conditions.

Sequencing in remote locations and resource-poor settings presents unique challenges. Nanopore sequencing can be successfully used under such conditions, ...

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to normal and pathological conditions. The goal of this protocol is to generate high-quality, large-scale transcriptome data of each endocrine cell type with the use of a droplet-based microfluidic single-cell RNA sequencing technology. Such data can be utilized to build the gene expression profile of each endocrine cell type in normal or specific conditions. The process requires careful handling, accurate measurement, and rigorous quality control. In this protocol, we describe detailed steps for human pancreatic islets dissociation, sequencing, and data analysis. The representative results of about 20,000 human single islet cells demonstrate the successful application of the protocol.

Here, we present a protocol to generate high-quality, large-scale transcriptome data of single cells from isolated human pancreatic islets using a droplet-based microfluidic single-cell RNA sequencing technology.

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to ...

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Gene expression analysis by RNA sequencing (RNA-seq) enables unique insights into clinical samples that can potentially lead to mechanistic understanding of the basis of various diseases as well as resistance and/or susceptibility mechanisms. However, FFPE tissues, which represent the most common method for preserving tissue morphology in clinical specimens, are not the best sources for gene expression profiling analysis. The RNA obtained from such samples is often degraded, fragmented, and chemically modified, which leads to suboptimal sequencing libraries. In turn, these generate poor quality sequence data that may not be reliable for gene expression analysis and mutation discovery. In order to make the most of FFPE samples and obtain the best possible data from low quality samples, it is important to take certain precautions while planning experimental design, preparing sequencing libraries, and during data analysis. This includes the use of appropriate metrics for precise sample quality control (QC), identifying the best methods for various steps during the sequencing library generation, and careful library QC. In addition, applying correct software tools and parameters for sequence data analysis is critical in order to identify artifacts in RNA-seq data, filter out contamination and low quality reads, assess uniformity of gene coverage, and measure the reproducibility of gene expression profiles among biological replicates. These steps can ensure high accuracy and reproducibility for profiling of very heterogeneous RNA samples. Here we describe the various steps for sample QC, library preparation and QC, sequencing, and data analysis that can help to increase the amount of useful data obtained from low quality RNA, such as that obtained from FFPE-RNA tissues.

This method describes the steps to improve the quality and quantity of sequence data that can be obtained from formalin-fixed paraffin-embedded (FFPE) RNA samples. We describe the methodology to more accurately assess the quality of FFPE-RNA samples, prepare sequencing libraries, and analyze the data from FFPE-RNA samples.

Gene expression analysis by RNA sequencing (RNA-seq) enables unique insights into clinical samples that can potentially lead to mechanistic understanding ...

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data

There is growing attention toward closed biological genomes in the environment and in health. To explore and reveal the intergroup differences among different samples or environments, it is crucial to discover biomarkers with statistical differences among groups. The application of Linear discriminant analysis Effect Size (LEfSe) can help find good biomarkers. Based on the original genome data, quality control, and quantification of different sequences based on taxa or genes are carried out. First, the Kruskal-Wallis rank test was used to distinguish between specific differences among statistical and biological groups. Then, the Wilcoxon rank test was performed between the two groups obtained in the previous step to assess whether the differences were consistent. Finally, a linear discriminant analysis (LDA) was conducted to evaluate the influence of biomarkers on significantly different groups based on LDA scores. To sum up, LEfSe provided the convenience for identifying genomic biomarkers that characterize statistical differences among biological groups.

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

LEfSe (LDA Effect Size) is a tool for high-dimensional biomarker mining to identify genomic features (such as genes, pathways, and taxonomies) that significantly characterize two or more groups in microbiome data.

There is growing attention toward closed biological genomes in the environment and in health. To explore and reveal the intergroup differences among ...