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>
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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;RUN sequencing data. 
This CUT&amp;RUN analysis protocol describes the application of several steps to ensure bona fide signals are quantified. The removal of poor-quality reads and adapter sequences from raw read data is one of the first quality control steps, and one of the most critical steps to obtain accurate analysis outcomes. Therefore, this analysis pipeline includes easy-to-apply quality and adapter trimming steps using the Trim-galore program27. Due to the importance of this process, this analysis pipeline includes steps to compare the quality of results before (step 4.3) and after (step 5.3) the trimming process (step 5.5). In addition to quality and adapter trimming, this analysis pipeline also removes non-canonical chromosome reads, TA repeat regions, and blacklist regions, which can introduce GC content bias and false-positive spikes/called peaks. These filtration steps provide an appropriate introductory pipeline for bioinformatics beginners to understand the critical quality control steps for CUT&amp;RUN data analysis.
After the filtration step, this CUT&amp;RUN analysis pipeline provides two normalization options: 'scaled fractional readcount (SFRC)22' and 'Spike-in normalized Reads Per Million mapped reads in the negative Control (SRPMC)24,25 to create input files for downstream peak calling and visualization. If CUT&amp;RUN dataset is expected to reveal local differences only without genome-wide signal differences between samples, the scaled fractional readcount (the fraction of counts times the size of reference gnome) may suffice for downstream analysis. However, if there is possibility that global scale signal differences will be present between CUT&amp;RUN samples, users can choose the SRPMC method that considers the ratio of reads between spike-in and sample (both experimental CUT&amp;RUN and negative control samples) along with reads per million (RPM) normalization for negative control reads to make the negative control reads comparable between different samples. Since the SRPMC provides normalized reads relative to normalized negative control reads, this approach minimizes the negative control signal enables comparison between datasets created in different batches and groups.
An important factor in CUT&amp;RUN sample peak calling is eliminating false positive CUT&amp;RUN peaks during the in silico analysis, in part, through inclusion of IgG samples. Specifically, this analysis pipeline provides peak calling approaches for different peak callers to discard false positive CUT&amp;RUN called peaks. For MACS2/3 peak callers, our analysis pipeline applies IgG mock reads as input sample during peak calling. For SEACR, this analysis pipeline recommends calling the peaks for experimental samples and negative control samples independently first, and then removing the peaks that overlap between experimental samples and negative control samples since SEACR may 'lose' a majority of peaks if provided with the negative control during the peak calling of the experimental samples. Curated peaks exhibit comparable similarity between different peak callers and replicates (Figure 5). Altogether, removal of poor quality, non-canonical chromosomes, blacklist region, and TA repeats reads, adapter sequences trimming, spike-in DNA normalization, and proper handling of negative control during peak calling steps provides users with proper readcounts files that are suitable for downstream analyses. With high-quality normalized read files and curated called peaks, users can proceed to compare similarity between replicates and create heatmaps and metaplots with ultra-clean background signal to validate the effective peak calling.
Peak calling with high-quality reads marks the initiation of drawing biological interpretations from CUT&amp;RUN data. This protocol describes obtaining focused peak signals in heatmaps and metaplots by appointing the highest signal or most statistically significant signal locations as peak centers. Some peak calling approaches do not select the highest signals or most statistically significant signals at their center location. Therefore, re-defining the center of each peak as the highest signal or most statistically significant signal location serves as an important step to create visual data outputs with well-focused signals at the center of the plots. Bed files of original called peaks are retained to perform peak annotation and functional relevance analysis as next steps after completion of the steps described in this protocol.
Although this CUT&amp;RUN analysis pipeline includes steps to describe installation of required programs, beginners in bioinformatics may encounter difficulties in installing the analysis tools. Therefore, an associated Github issue page has been established to provide more detailed step-by-step descriptions for program installation and to facilitate communications to support users during program installation in their own system. Next steps in the CUT&amp;RUN analysis pipeline beyond the protocol described in this article include peak annotation, identifying overlaps between different types of called peaks, and functional annotation for called peaks. Completion of quality control steps and peak calling described in this protocol combined with downstream peak annotation will enable users to draw biological meaning from their CUT&amp;RUN data. 
This CUT&amp;RUN analysis pipeline has been built to provide general introductory step-by-step guidelines for bulk CUT&amp;RUN analysis. This pipeline possesses some limitations. First, although this analysis pipeline is trying to handle GC contents variation driven effect by filtering out reads on blacklist regions (which include "high signal artifact regions" and "artifact repeat regions", and TA repeat regions) this approach may not be sufficient for some organisms that may have distinctive GC content on their genome. Therefore, if users are concerned of any GC content-driven biases, consider adding another step to correct mapped reads. For bioinformatics beginners, 'computeGCBias' and 'correctGCBias' in Deeptools may be options for this objective. Second, this analysis pipeline is handling both regular insert size (100 bp-1 kb) and small insert size reads (&lt; 100 bp), which may be the actual reads of some chromatin-associated proteins, within same file. Since this analysis pipeline is written in shell scripts, users can modify "Script_08_bam-to-BEDPE-BED-bedGraph.sh" to grab the short insert size reads separately from regular fragment size reads during the mapped reads bed file generation step. Subsequently, the short insert size reads bed file can be normalized independently from regular insert size mapped reads to minimize the downsizing effect. Third, to reduce the complexity of analysis pipeline, Easy Shells CUTnRUN does not include a down-sampling step to match the sequencing depth of CUT&amp;RUN samples. However, users can apply a down-sample step after filtering bam files by using samtools view28 or PositionBasedDownsampleSam (Picard)29. 
All the analysis steps in this protocol are written in shell scripts to enable bioinformatics beginners to learn the basics of CUT&amp;RUN analysis by reviewing the scripts. We expect that users can practice bioinformatics analysis in a step-by-step manner by running each shell script sequentially in the terminal. Furthermore, the simplicity of the shell scripts provided in this CUT&amp;RUN analysis pipeline allows users to revise and customize these scripts to apply this analysis pipeline to their own CUT&amp;RUN data. Ultimately, we expect that this CUT&amp;RUN analysis pipeline can reduce common bottlenecks in the CUT&amp;RUN data analysis process to empower wet lab researchers and bioinformatics beginners to draw biological conclusions from their own CUT&amp;RUN sequencing data.

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><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 &amp;amp; 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.

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

Nanyang Technological University

Research

JoVE Journal

Genetics

2.1K Views.  University of Texas MD Anderson Cancer Center. Ons onderzoek verkent de rol van repetitieve DNA-elementen bij het ontstaan en de progressie van kanker. We vragen ons af of deze elementen direct betrokken zijn bij mechanismen van het ontstaan van kanker, en zo ja, kunnen we deze informatie gebruiken om de resultaten voor de patiënt te verbeteren. Veel sequencingtechnologie en analytische pijplijnen van de volgende generatie zijn ontwikkeld om de functie en veranderingen van de epigenetische factoren vast ...

Video: Auteur in de schijnwerpers: onderzoek naar de rol van repetitieve DNA-misregulatie bij het initiëren van kanker en resistentie tegen immunotherapie

Metagenomic Analysis of Silage

Metagenomics is defined as the direct analysis of deoxyribonucleic acid (DNA) purified from environmental samples and enables taxonomic identification of the microbial communities present within them. Two main metagenomic approaches exist; sequencing the 16S rRNA gene coding region, which exhibits sufficient variation between taxa for identification, and shotgun sequencing, in which genomes of the organisms that are present in the sample are analyzed and ascribed to "operational taxonomic units"; species, genera or families depending on the extent of sequencing coverage. 
In this study, shotgun sequencing was used to analyze the microbial community present in cattle silage and, coupled with a range of bioinformatics tools to quality check and filter the DNA sequence reads, perform taxonomic classification of the microbial populations present within the sampled silage, and achieve functional annotation of the sequences. These methods were employed to identify potentially harmful bacteria that existed within the silage, an indication of silage spoilage. If spoiled silage is not remediated, then upon ingestion it could be potentially fatal to the livestock.

Metagenomics was used to investigate the microbiome of silage cattle feed. Analysis was performed by shotgun sequencing. This approach was used to characterize the composition of the microbial community within the cattle feed.

Metagenomics is defined as the direct analysis of deoxyribonucleic acid (DNA) purified from environmental samples and enables taxonomic identification of ...

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 ...

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The technique is highly efficient with millions of sequencing reads being produced in a short time span and at relatively low cost. Specifically, targeted NGS is able to focus investigations to genomic regions of particular interest based on the disease of study. Not only does this further reduce costs and increase the speed of the process, but it lessens the computational burden that often accompanies NGS. Although targeted NGS is restricted to certain regions of the genome, preventing identification of potential novel loci of interest, it can be an excellent technique when faced with a phenotypically and genetically heterogeneous disease, for which there are previously known genetic associations. Because of the complex nature of the sequencing technique, it is important to closely adhere to protocols and methodologies in order to achieve sequencing reads of high coverage and quality. Further, once sequencing reads are obtained, a sophisticated bioinformatics workflow is utilized to accurately map reads to a reference genome, to call variants, and to ensure the variants pass quality metrics. Variants must also be annotated and curated based on their clinical significance, which can be standardized by applying the American College of Medical Genetics and Genomics Pathogenicity Guidelines. The methods presented herein will display the steps involved in generating and analyzing NGS data from a targeted sequencing panel, using the ONDRISeq neurodegenerative disease panel as a model, to identify variants that may be of clinical significance.

Targeted next-generation sequencing is a time- and cost-efficient approach that is becoming increasingly popular in both disease research and clinical diagnostics. The protocol described here presents the complex workflow required for sequencing and the bioinformatics process used to identify genetic variants that contribute to disease.

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The ...

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been used to analyze the spectrum of clonal mutations in malignancy. Though far more efficient than traditional Sanger methods, NGS struggles with identifying rare clonal and subclonal mutations due to its high error rate of ~0.5&#8211;2.0%. Thus, standard NGS has a limit of detection for mutations that are &#62;0.02 variant allele fraction (VAF). While the clinical significance for mutations this rare in patients without known disease remains unclear, patients treated for leukemia have significantly improved outcomes when residual disease is &#60;0.0001 by flow cytometry. In order to mitigate this artefactual background of NGS, numerous methods have been developed. Here we describe a method for Error-corrected DNA and RNA Sequencing (ECS), which involves tagging individual molecules with both a 16 bp random index for error-correction and an 8 bp patient-specific index for multiplexing. Our method can detect and track clonal mutations at variant allele fractions (VAFs) two orders of magnitude lower than the detection limit of NGS and as rare as 0.0001 VAF.

Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5&#8211;2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as 0.0001.

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been ...

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

The study of small RNAs (sRNAs) by next-generation sequencing (NGS) is challenged by bias issues during library preparation. Several types of sRNA such as plant microRNAs (miRNAs) carry a 2&#39;-O-methyl (2&#39;-OMe) modification at their 3&#39;&#160;terminal nucleotide. This modification adds another difficulty as it inhibits 3&#39;&#160;adapter ligation. We previously demonstrated that modified versions of the &#39;TruSeq (TS)&#39;&#160;protocol have less bias and an improved detection of 2&#39;-OMe RNAs. Here we describe in detail protocol &#39;TS5&#39;, which showed the best overall performance. TS5 can be followed either using homemade reagents or reagents from the TS kit, with equal performance.

We present a detailed small RNA library reparation protocol with less bias than standard methods and an increased sensitivity for 2&#39;-O-methyl RNAs. This protocol can be followed using homemade reagents to save cost or using kits for convenience.

The study of small RNAs (sRNAs) by next-generation sequencing (NGS) is challenged by bias issues during library preparation. Several types of sRNA such as ...

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 ...

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 ...

Biology

3' End Sequencing Library Preparation with A-seq2

Studies in the last decade have revealed a complex and dynamic variety of pre-mRNA cleavage and polyadenylation reactions. mRNAs with long 3&#39; untranslated regions (UTRs) are generated in differentiated cells whereas proliferating cells preferentially express transcripts with short 3&#39;UTRs. We describe the A-seq protocol, now at its second version, which was developed to map polyadenylation sites genome-wide and study the regulation of pre-mRNA 3&#39; end processing. Also this current protocol takes advantage of the polyadenylate (poly(A)) tails that are added during the biogenesis of most mammalian mRNAs to enrich for fully processed mRNAs. A DNA adaptor with deoxyuracil at its fourth position allows the precise processing of mRNA 3&#39; end fragments for sequencing. Not including the cell culture and the overnight ligations, the protocol requires about 8 h hands-on time. Along with it, an easy-to-use software package for the analysis of the derived sequencing data is provided. A-seq2 and the associated analysis software provide an efficient and reliable solution to the mapping of pre-mRNA 3&#39; ends in a wide range of conditions, from 106 or fewer cells.

This protocol describes a method for mapping pre-mRNA 3&#39; end processing sites.

Studies in the last decade have revealed a complex and dynamic variety of pre-mRNA cleavage and polyadenylation reactions. mRNAs with long 3&#39; ...

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

As well as the typical analysis of RNA-Seq to measure differential gene expression (DGE) across experimental/biological conditions, RNA-seq data can also be utilized to explore other complex regulatory mechanisms at the exon level. Alternative splicing and polyadenylation play a crucial role in the functional diversity of a gene by generating different isoforms to regulate gene expression at the post-transcriptional level, and limiting analyses to the whole gene level can miss this important regulatory layer. Here, we demonstrate detailed step by step analyses for identification and visualization of differential exon and polyadenylation site usage across conditions, using Bioconductor and other packages and functions, including DEXSeq, diffSplice from the Limma package, and rMATS.

Alternative splicing (AS) and alternative polyadenylation (APA) expand the diversity of transcript isoforms and their products. Here, we describe bioinformatic protocols to analyze bulk RNA-seq and 3' end sequencing assays to detect and visualize AS and APA varying across experimental conditions.

As well as the typical analysis of RNA-Seq to measure differential gene expression (DGE) across experimental/biological conditions, RNA-seq data can also ...

Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&amp;RUN Method and Data Analysis Workflow

Regulatory transcription factors control many important biological processes, including cellular differentiation, responses to environmental perturbations and stresses, and host-pathogen interactions. Determining the genome-wide binding of regulatory transcription factors to DNA is essential to understanding the function of transcription factors in these often complex biological processes. Cleavage under targets and release using nuclease (CUT&amp;RUN) is a modern method for genome-wide mapping of in vivo protein-DNA binding interactions that is an attractive alternative to the traditional and widely used chromatin immunoprecipitation followed by sequencing (ChIP-seq) method. CUT&amp;RUN is amenable to a higher-throughput experimental setup and has a substantially higher dynamic range with lower per-sample sequencing costs than ChIP-seq. Here, a comprehensive CUT&amp;RUN protocol and accompanying data analysis workflow tailored for genome-wide analysis of transcription factor-DNA binding interactions in the human fungal pathogen Candida albicans are described. This detailed protocol includes all necessary experimental procedures, from epitope tagging of transcription factor-coding genes to library preparation for sequencing; additionally, it includes a customized computational workflow for CUT&amp;RUN data analysis.

Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&RUN Method and Data Analysis Workflow

This protocol describes an experimental method and data analysis workflow for cleavage under targets and release using nuclease (CUT&amp;RUN) in the human fungal pathogen Candida albicans.

Regulatory transcription factors control many important biological processes, including cellular differentiation, responses to environmental perturbations ...

Introductory Analysis and Validation of CUT&RUN Sequencing Data

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