CWL-Based Analysis Pipeline for Hi-C Data: From FASTQ Files to Matrices.

Over a decade has passed since the development of the Hi-C method for genome-wide analysis of 3D genome organization. Hi-C utilizes next-generation sequencing (NGS) technology to generate large-scale chromatin interaction data, which has accumulated across a diverse range of species and cell types, particularly in eukaryotes. There is thus a growing need to streamline the process of Hi-C data analysis to utilize these data sets effectively. Hi-C generates data that are much larger compared to other NGS techniques such as chromatin immunoprecipitation sequencing (ChIP-seq) or RNA-seq, making the data reanalysis process computationally expensive. In an effort to bridge this resource gap, the 4D Nucleome (4DN) Data Portal has reanalyzed approximately 600 Hi-C data sets, allowing users to access and utilize the analyzed data. In this chapter, we provide detailed instructions for the implementation of the common workflow language (CWL)-based Hi-C analysis pipeline adopted by the 4DN Data Portal ecosystem. This reproducible and portable pipeline generates standard Hi-C contact matrices in formats such as .hic or .mcool from FASTQ files. It enables users to output their own Hi-C data in the same format as those registered in the 4DN Data portal, facilitating comparative analysis using data registered in the portal. Our custom-made scripts are available on GitHub at https://github.com/kuzobuta/4dn_cwl_pipeline .

Processing and Visualization of Protec-Seq Data for the Generation of Calibrated, Genome-Wide Double Double-Strand Break (dDSB) Maps.

This chapter describes the computational pipeline for the processing and visualization of Protec-Seq data, a method for purification and genome-wide mapping of double-stranded DNA protected by a specific protein at both ends. In the published case, the protein of choice was Saccharomyces cerevisiae Spo11, a conserved topoisomerase-like enzyme that makes meiotic double-strand breaks (DSBs) to initiate homologous recombination, ensuring proper segregation of homologous chromosomes and fertility. The isolated DNA molecules were thus termed double DSB (dDSB) fragments and were found to represent 34 to several hundred base-pair long segments that are generated by Spo11 and are enriched at DSB hotspots, which are sites of topological stress. In order to allow quantitative comparisons between dDSB profiles across experiments, we implemented calibrated chromatin immunoprecipitation sequencing (ChIP-Seq) using the meiosis-competent yeast species Saccharomyces kudriavzevii as calibration strain. Here, we provide a detailed description of the computational methods for processing, analyzing, and visualizing Protec-Seq data, comprising the download of the raw data, the calibrated genome-wide alignments, and the scripted creation of either arc plots or Hi-C-style heatmaps for the illustration of chromosomal regions of interest. The workflow is based on Linux shell scripts (including wrappers for publicly available, open-source software) as well as R scripts and is highly customizable through its modular structure.

Learning Enhancer-Gene associations from Bulk Transcriptomic and Epigenetic Sequencing Data with STITCHIT.

To reveal gene regulation mechanisms, it is essential to understand the role of regulatory elements, which are possibly distant from gene promoters. Integrative analysis of epigenetic and transcriptomic data can be used to gain insights into gene-expression regulation in specific phenotypes. Here, we discuss STITCHIT, an approach to dissect epigenetic variation in a gene-specific manner across many samples for the identification of regulatory elements without relying on peak calling algorithms. The obtained genomic regions are then further refined using a regularized linear model approach, which can also be used to predict gene expression. We illustrate the use of STITCHIT using H3k27ac ChIP-seq and RNA-seq data from the International Human Epigenome Consortium (IHEC).

Nuclei Isolation from Ocular Tissues of the Embryonic Chicken for Single-Nucleus Profiling.

The generation of quality data from a single-nucleus profiling experiment requires nuclei to be isolated from tissues in a gentle and efficient manner. Nuclei isolation must be carefully optimized across tissue types to preserve nuclear architecture, prevent nucleic acid degradation, and remove unwanted contaminants. Here, we present an optimized workflow for generating a single-nucleus suspension from ocular tissues of the embryonic chicken that is compatible with various downstream workflows. The described protocol enables the rapid isolation of a high yield of aggregate-free nuclei from the embryonic chicken eye without compromising nucleic acid integrity, and the nuclei suspension is compatible with single-nucleus RNA and ATAC sequencing. We detail several stopping points, either via cryopreservation or fixation, to enhance workflow adaptability. Further, we provide a guide through multiple QC points and demonstrate proof-of-principle using two commercially available kits. Finally, we demonstrate that existing in silico genotyping methods can be adopted to computationally derive biological replicates from a single pool of chicken nuclei, greatly reducing the cost of biological replication and allowing researchers to consider sex as a variable during analysis. Together, this tutorial represents a cost-effective, simple, and effective approach to single-nucleus profiling of embryonic chicken eye tissues and is likely to be easily modified to be compatible with similar tissue types.

Quantitative analysis of cis-regulatory elements in transcription with KAS-ATAC-seq.

Cis-regulatory elements (CREs) are pivotal in orchestrating gene expression throughout diverse biological systems. Accurate identification and in-depth characterization of functional CREs are crucial for decoding gene regulation networks during cellular processes. In this study, we develop Kethoxal-Assisted Single-stranded DNA Assay for Transposase-Accessible Chromatin with Sequencing (KAS-ATAC-seq) to quantitatively analyze the transcriptional activity of CREs. A main advantage of KAS-ATAC-seq lies in its precise measurement of ssDNA levels within both proximal and distal ATAC-seq peaks, enabling the identification of transcriptional regulatory sequences. This feature is particularly adept at defining Single-Stranded Transcribing Enhancers (SSTEs). SSTEs are highly enriched with nascent RNAs and specific transcription factors (TFs) binding sites that define cellular identity. Moreover, KAS-ATAC-seq provides a detailed characterization and functional implications of various SSTE subtypes. Our analysis of CREs during mouse neural differentiation demonstrates that KAS-ATAC-seq can effectively identify immediate-early activated CREs in response to retinoic acid (RA) treatment. Our findings indicate that KAS-ATAC-seq provides more precise annotation of functional CREs in transcription. Future applications of KAS-ATAC-seq would help elucidate the intricate dynamics of gene regulation in diverse biological processes.

aChIP is an efficient and sensitive ChIP-seq technique for economically important plant organs.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is crucial for profiling histone modifications and transcription factor binding throughout the genome. However, its application in economically important plant organs (EIPOs) such as seeds, fruits and flowers is challenging due to their sturdy cell walls and complex constituents. Here we present advanced ChIP (aChIP), an optimized method that efficiently isolates chromatin from plant tissues while simultaneously removing cell walls and cellular constituents. aChIP precisely profiles histone modifications in all 14 tested EIPOs and identifies transcription factor and chromatin-modifying enzyme binding sites. In addition, aChIP enhances ChIP efficiency, revealing numerous novel modified sites compared with previous methods in vegetative tissues. aChIP reveals the histone modification landscape for rapeseed dry seeds, highlighting the intricate roles of chromatin dynamics during seed dormancy and germination. Altogether, aChIP is a powerful, efficient and sensitive approach for comprehensive chromatin profiling in virtually all plant tissues, especially in EIPOs.

scaDA: A novel statistical method for differential analysis of single-cell chromatin accessibility sequencing data.

Single-cell ATAC-seq sequencing data (scATAC-seq) has been widely used to investigate chromatin accessibility on the single-cell level. One important application of scATAC-seq data analysis is differential chromatin accessibility (DA) analysis. However, the data characteristics of scATAC-seq such as excessive zeros and large variability of chromatin accessibility across cells impose a unique challenge for DA analysis. Existing statistical methods focus on detecting the mean difference of the chromatin accessible regions while overlooking the distribution difference. Motivated by real data exploration that distribution difference exists among cell types, we introduce a novel composite statistical test named "scaDA", which is based on zero-inflated negative binomial model (ZINB), for performing differential distribution analysis of chromatin accessibility by jointly testing the abundance, prevalence and dispersion simultaneously. Benefiting from both dispersion shrinkage and iterative refinement of mean and prevalence parameter estimates, scaDA demonstrates its superiority to both ZINB-based likelihood ratio tests and published methods by achieving the highest power and best FDR control in a comprehensive simulation study. In addition to demonstrating the highest power in three real sc-multiome data analyses, scaDA successfully identifies differentially accessible regions in microglia from sc-multiome data for an Alzheimer's disease (AD) study that are most enriched in GO terms related to neurogenesis and the clinical phenotype of AD, and AD-associated GWAS SNPs.

Efficient Enrichment of Synchronized Mouse Spermatocytes Suitable for Genome-Wide Analysis.

Chromatin undergoes extensive remodeling during meiosis, leading to specific patterns of gene expression and chromosome organization, which ultimately controls fundamental meiotic processes such as recombination and homologous chromosome associations. Recent game-changing advances have been made by analysis of chromatin binding sites of meiotic specific proteins genome-wide in mouse spermatocytes. However, further progress is still highly dependent on the reliable isolation of sufficient quantities of spermatocytes at specific stages of prophase I. Here, we describe a combination of methodologies we adapted for rapid and reliable isolation of synchronized fixed mouse spermatocytes. We show that chromatin isolated from these cells can be used to study chromatin-binding sites by ChIP-seq. High-quality data we obtained from INO80 ChIP-seq in zygotene cells was used for functional analysis of chromatin-binding sites.

MOCHA's advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts.

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is being increasingly used to study gene regulation. However, major analytical gaps limit its utility in studying gene regulatory programs in complex diseases. In response, MOCHA (Model-based single cell Open CHromatin Analysis) presents major advances over existing analysis tools, including: 1) improving identification of sample-specific open chromatin, 2) statistical modeling of technical drop-out with zero-inflated methods, 3) mitigation of false positives in single cell analysis, 4) identification of alternative transcription-starting-site regulation, and 5) modules for inferring temporal gene regulatory networks from longitudinal data. These advances, in addition to open chromatin analyses, provide a robust framework after quality control and cell labeling to study gene regulatory programs in human disease. We benchmark MOCHA with four state-of-the-art tools to demonstrate its advances. We also construct cross-sectional and longitudinal gene regulatory networks, identifying potential mechanisms of COVID-19 response. MOCHA provides researchers with a robust analytical tool for functional genomic inference from scATAC-seq data.

Native ChIP: Studying the Genome-Wide Distribution of Histone Modifications in Cells and Tissue.

For the genome-wide mapping of histone modifications, chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing remains the benchmark method. While crosslinked ChIP can be used for all kinds of targets, native ChIP is predominantly used for strong and direct DNA interactors like histones and their modifications. Here we describe a native ChIP protocol that can be used for cells and tissue material.

Bioinformatics Core Workflow for ChIP-Seq Data Analysis.

Chromatin immunoprecipitation (ChIP) followed by next-generation sequencing (-seq) has been the most common genomics method for studying DNA-protein interactions in the last decade. ChIP-seq technology became standard both experimentally and computationally. This chapter presents a core workflow that covers data processing and initial analytical steps of ChIP-seq data. We provide a step-by-step protocol of the commands as well as a fully assembled Snakemake workflow. Along the protocol, we discuss key tool parameters, quality control, output reports, and preliminary results.

Differential Analysis of Protein-DNA Binding Using ChIP-Seq Data.

Chromatin immunoprecipitation in combination with next-generation sequencing (ChIP-Seq) allows probing of protein-DNA binding in a rapid and genome-wide fashion. Herein we describe the required steps to preprocess ChIP-Seq data and to analyze the differential binding of proteins to DNA for perturbation experiments. In these experiments, different conditions are compared to find the underlying biological mechanisms caused by the stimulus or treatment. In addition, we provide a sample analysis using the steps outlined in the chapter.

Sequential ChIP-Seq.

ChIP-Seq has been used extensively to profile genome-wide transcription factor binding and post-translational histone modifications. A sequential ChIP assay determines the in vivo co-localization of two proteins to the same genomic locus. In this chapter, we combine the two protocols in Sequential ChIP-Seq, a method for identifying genome-wide sites of in vivo protein co-occupancy.

Estrogen Receptor Chromatin Profiling by CUT&RUN.

Gonadal steroid hormones, namely, testosterone, progesterone, and estrogens, influence the physiological state of an organism through the regulation of gene transcription. Steroid hormones activate nuclear hormone receptor (HR), transcription factors (TFs), which bind DNA in a tissue- and cell type-specific manner to influence cellular function. Identifying the genomic binding sites of HRs is essential to understanding mechanisms of hormone signaling across tissues and disease contexts. Traditionally, chromatin immunoprecipitation followed by sequencing (ChIP-seq) has been used to map the genomic binding of HRs in cancer cell lines and large tissues. However, ChIP-seq lacks the sensitivity to detect TF binding in small numbers of cells, such as genetically defined neuronal subtypes in the brain. Cleavage Under Targets & Release Under Nuclease (CUT&RUN) resolves most of the technical limitations of ChIP-seq, enabling the detection of protein-DNA interactions with as few as 100-1000 cells. In this chapter, we provide a stepwise CUT&RUN protocol for detecting and analyzing the genome-wide binding of estrogen receptor α (ERα) in mouse brain tissue. The steps described here can be used to identify the genomic binding sites of most TFs in the brain.

Cleavage Under Targets and Release Using Nuclease (CUT&RUN) of Epigenetic Regulators.

Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) allows for the identification of genomic targeting of DNA-binding proteins. Cleavage Under Targets and Release Using Nuclease (CUT&RUN) modifies this process by including a nuclease to digest DNA around a protein of interest. The result is a higher signal-to-noise ratio and decreased required starting material. This allows for high-fidelity sequence identification from as few as 500 cells, enabling chromatin profiling of precious tissue samples or primary cell types, as well as less abundant chromatin-binding proteins: all at significantly increased throughput.

Integrative Analysis of CUT&Tag and RNA-Seq Data Through Bioinformatics: A Unified Workflow for Enhanced Insights.

Cleavage Under Targets and Tagmentation (CUT&Tag) is a recent methodology used for robust epigenomic profiling that, unlike conventional chromatin immunoprecipitation (ChIP-Seq), requires only a limited amount of cells as starting material. RNA sequencing (RNA-Seq) reveals the presence and quantity of RNA in a biological sample, describing the continuously changing cellular transcriptome. The integrated analysis of transcriptional activity, histone modifications, and chromatin accessibility via CUT&Tag is still in its infancy compared to the well-established ChIP-Seq. This chapter describes a robust bioinformatics methodology and workflow to perform an integrative CUT&Tag/RNA-Seq analysis.

SPO-Seq: An Accessible Method for Efficient Evaluation of Spo11 Catalytic Activity and Profiling Meiotic DSB Hotspots in Saccharomyces cerevisiae.

Meiotic recombination is a key process facilitating the formation of crossovers and the exchange of genetic material between homologous chromosomes in early meiosis. This involves controlled double-strand breaks (DSBs) formation catalyzed by Spo11. DSBs exhibit a preferential location in specific genomic regions referred to as hotspots, and their variability is tied to varying Spo11 activity levels. We have refined a ChIP-Seq technique, called SPO-Seq, to map Spo11-specific DSB formation in Saccharomyces cerevisiae. The chapter describes our streamlined approach and the developed bioinformatic tools for processing data and comparing with existing DSB hotspot maps. Through this combined experimental and computational approach, we aim to enhance our understanding of meiotic recombination and genetic exchange processes in budding yeast, with the potential to expand this methodology to other organisms by applying a few modifications.

Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data.

Gene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA sequencing (RNA-seq) data, which helps to decipher single-cell heterogeneity and cell type-specific variability by incorporating prior knowledge from functional gene sets. Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a powerful technique for interrogating single-cell chromatin-based gene regulation, and genes or gene sets with dynamic regulatory potentials can be regarded as cell type-specific markers as if in single-cell RNA-seq (scRNA-seq). However, there are few GSS tools specifically designed for scATAC-seq, and the applicability and performance of RNA-seq GSS tools on scATAC-seq data remain to be investigated. Here, we systematically benchmarked ten GSS tools, including four bulk RNA-seq tools, five scRNA-seq tools, and one scATAC-seq method. First, using matched scATAC-seq and scRNA-seq datasets, we found that the performance of GSS tools on scATAC-seq data was comparable to that on scRNA-seq, suggesting their applicability to scATAC-seq. Then, the performance of different GSS tools was extensively evaluated using up to ten scATAC-seq datasets. Moreover, we evaluated the impact of gene activity conversion, dropout imputation, and gene set collections on the results of GSS. Results show that dropout imputation can significantly promote the performance of almost all GSS tools, while the impact of gene activity conversion methods or gene set collections on GSS performance is more dependent on GSS tools or datasets. Finally, we provided practical guidelines for choosing appropriate preprocessing methods and GSS tools in different application scenarios.

Measuring B Cell Chromatin Accessibility by One-Step ATAC-seq.

The Assay for Transposase Accessible Chromatin (ATAC)-seq protocol is optimized to generate global maps of accessible chromatin using limited cell inputs. The Tn5 transposase tagmentation reaction simultaneously fragments and tags the accessible DNA with Illumina Nextera sequencing adapters. Fragmented and adapter tagged DNA is then purified and PCR amplified with dual indexing primers to generate a size-specific sequencing library. The One-Step workflow below outlines the Tn5 nuclei transposition from a range of cell inputs followed by PCR amplification to generate a sequencing library.

GeF-seq: A Simple Procedure for Base-Pair Resolution ChIP-seq.

Nucleotide sequences recognized and bound by DNA-binding proteins (DBPs) are critical to controlling and maintaining gene expression, replication, chromosome segregation, cell division, and nucleoid structure in bacterial cells. Therefore, determination of the binding sequences of DBPs is important not only to study DBP recognition mechanisms but also to understand the fundamentals of cell homeostasis. While ChIP-seq analysis appears to be an effective way to determine DBP binding sites on the genome, the resolution is sometimes not sufficient to identify the sites precisely. Here we introduce a simple and effective method named Genome Footprinting with high-throughput sequencing (GeF-seq) to determine binding sites of DBPs with single base-pair resolution. GeF-seq detects binding sites of DBPs as sharp peaks and thus makes it possible to identify the recognition sequence in each "binding peak" more easily and accurately compared to the common ChIP-seq.