CUT&RUNTools: a flexible pipeline for CUT&RUN processing and footprint analysis.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhu Q
PROVIDER: S-EPMC6734249 | biostudies-literature | 2019 Sep
REPOSITORIES: biostudies-literature
Publications
CUT&RUNTools: a flexible pipeline for CUT&RUN processing and footprint analysis.
Zhu Qian Q Liu Nan N Orkin Stuart H SH Yuan Guo-Cheng GC
Genome biology 20190909 1
We introduce CUT&RUNTools as a flexible, general pipeline for facilitating the identification of chromatin-associated protein binding and genomic footprinting analysis from antibody-targeted CUT&RUN primary cleavage data. CUT&RUNTools extracts endonuclease cut site information from sequences of short-read fragments and produces single-locus binding estimates, aggregate motif footprints, and informative visualizations to support the high-resolution mapping capability of CUT&RUN. CUT&RUNTools is a ...[more]
Similar Datasets
Project description:MotivationGenome-wide profiling of transcription factor binding and chromatin states is a widely-used approach for mechanistic understanding of gene regulation. Recent technology development has enabled such profiling at single-cell resolution. However, an end-to-end computational pipeline for analyzing such data is still lacking.ResultsHere, we have developed a flexible pipeline for analysis and visualization of single-cell CUT&Tag and CUT&RUN data, which provides functions for sequence alignment, quality control, dimensionality reduction, cell clustering, data aggregation and visualization. Furthermore, it is also seamlessly integrated with the functions in original CUT&RUNTools for population-level analyses. As such, this provides a valuable toolbox for the community.Availability and implementationhttps://github.com/fl-yu/CUT-RUNTools-2.0.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8696090 | biostudies-literature
Project description:Single-cell RNA-sequencing analysis to quantify the RNA molecules in individual cells has become popular, as it can obtain a large amount of information from each experiment. We introduce UniverSC ( https://github.com/minoda-lab/universc ), a universal single-cell RNA-seq data processing tool that supports any unique molecular identifier-based platform. Our command-line tool, docker image, and containerised graphical application enables consistent and comprehensive integration, comparison, and evaluation across data generated from a wide range of platforms. We also provide a cross-platform application to run UniverSC via a graphical user interface, available for macOS, Windows, and Linux Ubuntu, negating one of the bottlenecks with single-cell RNA-seq analysis that is data processing for researchers who are not bioinformatically proficient.
| S-EPMC9652453 | biostudies-literature
Project description:Over the past two decades, the dominant technology for chromatin profiling has been chromatin immunoprecipitation (ChIP), in which cellular components are solubilized to fragment chromatin, and an antibody is added to precipitate a DNA/protein complex of interest. We previously described a novel alternative to ChIP, Cleavage Under Targets & Release Using Nuclease (CUT&RUN), in which the antibody is added to permeabilized cells followed by binding of a Protein A-Micrococcal Nuclease (pA/MNase) fusion protein to the antibody (PMID 29651053). Upon activation of tethered MNase, the bound complex is excised and released into the supernatant for DNA extraction and sequencing. Here we introduce four extensions to CUT&RUN: 1) a hybrid Protein A-Protein G-MNase construct that allows simplified purification using a commercial kit; 2) a modified digestion protocol that prevents release of the enzyme and so minimizes artifactual cleavage of accessible DNA; 3) a calibration strategy based on carryover of E. coli DNA introduced with the fusion protein; and 4) a novel peak-calling strategy that is customized for the low-background profiles obtained using CUT&RUN. These new features, coupled with the previously described low-cost, high efficiency, high reproducibility and high-throughput capability of CUT&RUN make it the method of choice for routine epigenomic profiling.
2019-06-24 | GSE126612 | GEO
Project description:We introduce CUT&RUNTools (https://bitbucket.org/qzhudfci/cutruntools/) as a flexible, general pipeline for facilitating the identification of chromatin-associated protein binding and genomic footprinting analysis from antibody-targeted CUT&RUN primary cleavage data. CUT&RUNTools extracts endonuclease cut site information from sequences of short read fragments and produces single-locus binding estimates, aggregate motif footprints, and informative visualizations to support the high-resolution mapping capability of CUT&RUN. We illustrate the functionality of CUT&RUNTools through analysis of CUT&RUN data acquired for GATA1, a master regulator in erythroid lineage cells. Results were compared initially to published GATA1 ChIP-seq data for cells under the same conditions. We performed de novo analysis of CUT&RUN peaks to retrieve not only GATA1’s primary motif, but also the GATA1-TAL1 composite motif, and co-factor motifs GCCCCGCCTC, CMCDCCC, and RTGASTCA that correspond to SP1, KLF1, and NFE2 co-factors. Cofactor binding was verified by independent TAL1 and KLF1 CUT&RUN, and other ChIP-seq experiments. CUT&RUNTools also generated base-pair resolution motif footprint for sequence-specific binding factors, and located likely direct binding sites by quantifying log-odds of binding scores. Overall, CUT&RUNTools should enable biologists to realize advantages of cleavage data provided by CUT&RUN, and make high-quality footprinting analysis accessible to a broad audience.
2019-08-24 | GSE136251 | GEO
Project description:Previously, we described a novel alternative to chromatin immunoprecipitation, CUT&RUN, in which unfixed permeabilized cells are incubated with antibody, followed by binding of a protein A-Micrococcal Nuclease (pA/MNase) fusion protein (Skene and Henikoff, 2017). Here we introduce three enhancements to CUT&RUN: A hybrid protein A-Protein G-MNase construct that expands antibody compatibility and simplifies purification, a modified digestion protocol that inhibits premature release of the nuclease-bound complex, and a calibration strategy based on carry-over of E. coli DNA introduced with the fusion protein. These new features, coupled with the previously described low-cost, high efficiency, high reproducibility and high-throughput capability of CUT&RUN make it the method of choice for routine epigenomic profiling.
| S-EPMC6598765 | biostudies-literature
Project description:BackgroundCUT&RUN is an efficient epigenome profiling method that identifies sites of DNA binding protein enrichment genome-wide with high signal to noise and low sequencing requirements. Currently, the analysis of CUT&RUN data is complicated by its exceptionally low background, which renders programs designed for analysis of ChIP-seq data vulnerable to oversensitivity in identifying sites of protein binding.ResultsHere we introduce Sparse Enrichment Analysis for CUT&RUN (SEACR), an analysis strategy that uses the global distribution of background signal to calibrate a simple threshold for peak calling. SEACR discriminates between true and false-positive peaks with near-perfect specificity from "gold standard" CUT&RUN datasets and efficiently identifies enriched regions for several different protein targets. We also introduce a web server ( http://seacr.fredhutch.org ) for plug-and-play analysis with SEACR that facilitates maximum accessibility across users of all skill levels.ConclusionsSEACR is a highly selective peak caller that definitively validates the accuracy of CUT&RUN for datasets with known true negatives. Its ease of use and performance in comparison with existing peak calling strategies make it an ideal choice for analyzing CUT&RUN data.
| S-EPMC6624997 | biostudies-literature
Project description:Determining the genomic location of DNA-binding proteins is essential to understanding their function. Cleavage Under Targets and Release Using Nuclease (CUT&RUN) is a powerful method for mapping protein-DNA interactions at high resolution. In CUT&RUN, a recombinant protein A-microccocal nuclease (pA-MN) fusion is recruited by an antibody targeting the chromatin protein of interest; this can be done with either uncrosslinked or formaldehyde-crosslinked cells. DNA fragments near sites of antibody binding are released from the insoluble bulk chromatin through endonucleolytic cleavage and used to build barcoded DNA-sequencing libraries that can be sequenced in pools of at least 30. Therefore, CUT&RUN provides an alternative to ChIP-seq approaches for mapping chromatin proteins, which typically have relatively high signal-to-noise ratios, while using fewer cells and at a lower cost. Here, we describe the methods for performing CUT&RUN, generating DNA-sequencing libraries, and analyzing the resulting datasets. © 2019 by John Wiley & Sons, Inc.
| S-EPMC6422702 | biostudies-literature
Project description:Proteogenomics leverages information derived from proteomic data to improve genome annotations. Of particular interest are "novel" peptides that provide direct evidence of protein expression for genomic regions not previously annotated as protein-coding. We present a modular, automated data analysis pipeline aimed at detecting such "novel" peptides in proteomic data sets. This pipeline implements criteria developed by proteomics and genome annotation experts for high-stringency peptide identification and filtering. Our pipeline is based on the OpenMS computational framework; it incorporates multiple database search engines for peptide identification and applies a machine-learning approach (Percolator) to post-process search results. We describe several new and improved software tools that we developed to facilitate proteogenomic analyses that enhance the wealth of tools provided by OpenMS. We demonstrate the application of our pipeline to a human testis tissue data set previously acquired for the Chromosome-Centric Human Proteome Project, which led to the addition of five new gene annotations on the human reference genome.
| S-EPMC5703597 | biostudies-literature
Project description:Sequencing of bacterial genomes using Illumina technology has become such a standard procedure that often data are generated faster than can be conveniently analyzed. We created a new series of pipelines called Bactopia, built using Nextflow workflow software, to provide efficient comparative genomic analyses for bacterial species or genera. Bactopia consists of a data set setup step (Bactopia Data Sets [BaDs]), which creates a series of customizable data sets for the species of interest, the Bactopia Analysis Pipeline (BaAP), which performs quality control, genome assembly, and several other functions based on the available data sets and outputs the processed data to a structured directory format, and a series of Bactopia Tools (BaTs) that perform specific postprocessing on some or all of the processed data. BaTs include pan-genome analysis, computing average nucleotide identity between samples, extracting and profiling the 16S genes, and taxonomic classification using highly conserved genes. It is expected that the number of BaTs will increase to fill specific applications in the future. As a demonstration, we performed an analysis of 1,664 public Lactobacillus genomes, focusing on Lactobacillus crispatus, a species that is a common part of the human vaginal microbiome. Bactopia is an open source system that can scale from projects as small as one bacterial genome to ones including thousands of genomes and that allows for great flexibility in choosing comparison data sets and options for downstream analysis. Bactopia code can be accessed at https://www.github.com/bactopia/bactopiaIMPORTANCE It is now relatively easy to obtain a high-quality draft genome sequence of a bacterium, but bioinformatic analysis requires organization and optimization of multiple open source software tools. We present Bactopia, a pipeline for bacterial genome analysis, as an option for processing bacterial genome data. Bactopia also automates downloading of data from multiple public sources and species-specific customization. Because the pipeline is written in the Nextflow language, analyses can be scaled from individual genomes on a local computer to thousands of genomes using cloud resources. As a usage example, we processed 1,664 Lactobacillus genomes from public sources and used comparative analysis workflows (Bactopia Tools) to identify and analyze members of the L. crispatus species.
| S-EPMC7406220 | biostudies-literature
Project description:BackgroundThe study of microbiomes using whole-metagenome shotgun sequencing enables the analysis of uncultivated microbial populations that may have important roles in their environments. Extracting individual draft genomes (bins) facilitates metagenomic analysis at the single genome level. Software and pipelines for such analysis have become diverse and sophisticated, resulting in a significant burden for biologists to access and use them. Furthermore, while bin extraction algorithms are rapidly improving, there is still a lack of tools for their evaluation and visualization.ResultsTo address these challenges, we present metaWRAP, a modular pipeline software for shotgun metagenomic data analysis. MetaWRAP deploys state-of-the-art software to handle metagenomic data processing starting from raw sequencing reads and ending in metagenomic bins and their analysis. MetaWRAP is flexible enough to give investigators control over the analysis, while still being easy-to-install and easy-to-use. It includes hybrid algorithms that leverage the strengths of a variety of software to extract and refine high-quality bins from metagenomic data through bin consolidation and reassembly. MetaWRAP's hybrid bin extraction algorithm outperforms individual binning approaches and other bin consolidation programs in both synthetic and real data sets. Finally, metaWRAP comes with numerous modules for the analysis of metagenomic bins, including taxonomy assignment, abundance estimation, functional annotation, and visualization.ConclusionsMetaWRAP is an easy-to-use modular pipeline that automates the core tasks in metagenomic analysis, while contributing significant improvements to the extraction and interpretation of high-quality metagenomic bins. The bin refinement and reassembly modules of metaWRAP consistently outperform other binning approaches. Each module of metaWRAP is also a standalone component, making it a flexible and versatile tool for tackling metagenomic shotgun sequencing data. MetaWRAP is open-source software available at https://github.com/bxlab/metaWRAP .
| S-EPMC6138922 | biostudies-literature