Machine and Deep Learning Methods for Predicting 3D Genome Organization.

Three-dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, topologically associating domains (TADs), and A/B compartments, play critical roles in a wide range of cellular processes by regulating gene expression. Recent development of chromatin conformation capture technologies has enabled genome-wide profiling of various 3D structures, even with single cells. However, current catalogs of 3D structures remain incomplete and unreliable due to differences in technology, tools, and low data resolution. Machine learning methods have emerged as an alternative to obtain missing 3D interactions and/or improve resolution. Such methods frequently use genome annotation data (ChIP-seq, DNAse-seq, etc.), DNA sequencing information (k-mers and transcription factor binding site (TFBS) motifs), and other genomic properties to learn the associations between genomic features and chromatin interactions. In this review, we discuss computational tools for predicting three types of 3D interactions (EPIs, chromatin interactions, and TAD boundaries) and analyze their pros and cons. We also point out obstacles to the computational prediction of 3D interactions and suggest future research directions.

Extracting Chromosome Structural Information as One-Dimensional Metrics and Integrating Them with Epigenomics.

Hi-C is a powerful method for obtaining genome-wide chromosomal structural information. The typical Hi-C analysis utilizes a two-dimensional (2D) contact matrix, which poses challenges for quantitative comparisons, visualizations, and integrations across multiple datasets. Here, we present a protocol for extracting one-dimensional (1D) features from chromosome structure data by HiC1Dmetrics. Leveraging these 1D features enables integrated analysis of Hi-C and epigenomic data.

Methods for Genome-Wide Chromatin Interaction Analysis.

Recent analyses revealed the essential function of chromatin structure in maintaining and regulating genomic information. Advancements in microscopy, nuclear structure observation techniques, and the development of methods utilizing next-generation sequencers (NGSs) have significantly progressed these discoveries. Methods utilizing NGS enable genome-wide analysis, which is challenging with microscopy, and have elucidated concepts of important chromatin structures such as a loop structure, a domain structure called topologically associating domains (TADs), and compartments. In this chapter, I introduce chromatin interaction techniques using NGS and outline the principles and features of each method.

HOMER for Analysis of Hi-C Data and Assessment of Composite Structure of the X Chromosome.

Hi-C reads, which represent ligation events between different regions of the genome, must be processed into matrices of interaction frequencies for downstream analysis. Here, I describe a procedure for mapping Hi-C reads to the genome and conversion of mapped reads into the HOMER tag directory format and interaction matrix format for visualization with Juicebox. The method is demonstrated for the mouse composite X chromosome in which reads from the active and inactive X chromosomes are combined after mock DMSO treatment or targeted degradation of cohesin.

Acquisition and Analysis Methods for Hi-C Data from Medaka Early Embryos.

The three-dimensional (3D) structure of the genome undergoes dynamic changes during the developmental process. While Hi-C is a technique that enables the acquisition of genome 3D structure data across various species and cell types, existing Hi-C analysis programs may face challenges in detecting and comparing structures effectively depending on the characteristics of the genome or cell type. Here, we describe a method for acquiring Hi-C data from medaka early embryos and quantifying the structural changes during the developmental process.

Hi-C/3C-seq Data Analysis for Prokaryotic Genomes with HiC-Pro.

Hi-C and 3C-seq are powerful tools to study the 3D genomes of bacteria and archaea, whose small cell sizes and growth conditions are often intractable to detailed microscopic analysis. However, the circularity of prokaryotic genomes requires a number of tricks for Hi-C/3C-seq data analysis. Here, I provide a practical guide to use the HiC-Pro pipeline for Hi-C/3C-seq data obtained from prokaryotes.

Read Mapping for Hi-C Analysis.

Hi-C is a popular ligation-based technique to detect 3D physical chromosome structure within the nucleus using cross-linking and next-generation sequencing. As an unbiased genome-wide assay based on chromosome conformation capture, it provides rich insights into chromosome structure, dynamic chromosome folding and interactions, and the regulatory state of a cell. Bioinformatics analyses of Hi-C data require dedicated protocols as most genome alignment tools assume that both paired-end reads will map to the same chromosome, resulting in large two-dimensional matrices as processed data. Here, we outline the necessary steps to generate high-quality aligned Hi-C data by separately mapping each read while correcting for biases from restriction enzyme digests. We introduce our own custom open-source pipeline, which enables users to select an aligner of their choosing with high accuracy and performance. This enables users to generate high-resolution datasets with fast turnaround and fewer unmapped reads. Finally, we discuss recent innovations in experimental techniques, bioinformatics techniques, and their applications in clinical testing for diagnostics.

Exploring Contact Distance Distributions with Google Colaboratory.

Hi-C and Micro-C are the three-dimensional (3D) genome assays that use high-throughput sequencing. In the analysis, the sequenced paired-end reads are mapped to a reference genome to generate a two-dimensional contact matrix for identifying topologically associating domains (TADs), chromatin loops, and chromosomal compartments. On the other hand, the distance distribution of the paired-end mapped reads also provides insight into the 3D genome structure by highlighting global contact frequency patterns at distances indicative of loops, TADs, and compartments. This chapter presents a basic workflow for visualizing and analyzing contact distance distributions from Hi-C data. The workflow can be run on Google Colaboratory, which provides a ready-to-use Python environment accessible through a web browser. The notebook that demonstrates the workflow is available in the GitHub repository at https://github.com/rnakato/Springer_contact_distance_plot.

Systematic Inference of Multi-scale Chromatin Sub-compartments Using Calder2.

The compartmentalization of chromatin reflects its underlying biological activities. Inferring chromatin sub-compartments using Hi-C data is challenged by data resolution constraints. Consequently, comprehensive characterizations of sub-compartments have been limited to a select number of Hi-C experiments, with systematic comparisons across a wide range of tissues and conditions still lacking. Our original Calder algorithm marked a significant advancement in this field, enabling the identification of multi-scale sub-compartments at various data resolutions and facilitating the inference and comparison of chromatin architecture in over 100 datasets. Building on this foundation, we introduce Calder2, an updated version of Calder that brings notable improvements. These include expanded support for a wider array of genomes and organisms, an optimized bin size selection approach for more accurate chromatin compartment detection, and extended support for input and output formats. Calder2 thus stands as a refined analysis tool, significantly advancing genome-wide studies of 3D chromatin architecture and its functional implications.

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 .

Step-by-Step Protocol to Generate Hi-C Contact Maps Using the rfy_hic2 Pipeline.

The Hi-C method has emerged as an indispensable tool for analyzing the 3D organization of the genome, becoming increasingly accessible and frequently utilized in chromatin research. To effectively leverage 3D genomics data obtained through advanced technologies, it is crucial to understand what processes are undertaken and what aspects require special attention within the bioinformatics pipeline. This protocol aims to demystify the Hi-C data analysis process for field newcomers. In a step-by-step manner, we describe how to process Hi-C data, from the initial sequencing of the Hi-C library to the final visualization of Hi-C contact data as heatmaps. Each step of the analysis is clearly explained to ensure an understanding of the procedures and their objectives. By the end of this chapter, readers will be equipped with the knowledge to transform raw Hi-C reads into informative visual representations, facilitating a deeper comprehension of the spatial genomic structures critical to cellular functions.

Single-Cell Hi-C Analysis Workflow with Pairtools.

Single-cell Hi-C (scHi-C) is a collection of protocols for studying genomic interactions within individual cells. Although data analysis for scHi-C resembles data analysis for bulk Hi-C, the unique challenges of scHi-C, such as high noise and protocol-specific biases, require specialized data processing strategies. In this tutorial chapter, we focus on using pairtools, a suite of tools optimized for scHi-C data, demonstrating its application on a Drosophila snHi-C dataset. While centered on pairtools for snHi-C data, the principles outlined are applicable across scHi-C variants with minor adjustments. This educational chapter aims to guide researchers in using open-source tools for scHi-C analysis, emphasizing critical steps of contact pair extraction, detection of ligation junctions, filtration, and deduplication.

Supervised Chromatin Loop Detection Using Peakachu Version 2.

Peakachu is a supervised-learning-based approach that identifies chromatin loops from chromatin contact data. Here, we present Peakachu version 2, an updated version that significantly improves extensibility, usability, and computational efficiency compared to its predecessor. It features pretrained models tailored for a wide range of experimental platforms, such as Hi-C, Micro-C, ChIA-PET, HiChIP, HiCAR, and TrAC-loop. This chapter offers a step-by-step tutorial guiding users through the process of training Peakachu models from scratch and utilizing pretrained models to predict chromatin loops across various platforms.

Reconstruction of 3D Chromosome Structure from Single-Cell Hi-C Data via Recurrence Plots.

We describe an approach for reconstructing three-dimensional (3D) structures from single-cell Hi-C data. This approach has been inspired by a method of recurrence plots and visualization tools for nonlinear time series data. Some examples are also presented.

Analysis and Visualization of Multiple Hi-C and Micro-C Data with CustardPy.

Three-dimensional (3D) genome structure plays crucial roles in biological processes and disease pathogenesis. Hi-C and Micro-C, well-established methods for 3D genome analysis, can identify a variety of 3D genome structures. However, selecting appropriate pipelines and tools for the analysis and setting up the required computing environment can sometimes pose challenges. To address this, we have introduced CustardPy, a Docker-based pipeline specifically designed for 3D genome analysis. CustardPy is designed to compare and evaluate multiple samples and wraps several existing tools to cover the entire workflow from FASTQ mapping to visualization. In this chapter, we demonstrate how to analyze and visualize Hi-C data using CustardPy and introduce several 3D genome features observed in Hi-C data.

Construction of Coarse-Grained Molecular Dynamics Model of Nuclear Global Chromosomes Dynamics in Mammalian Cells.

Biomolecules contain various heterogeneities in their structures and local chemical properties, and their functions emerge through the dynamics encoded by these heterogeneities. Molecular dynamics model-based studies will greatly contribute to the elucidation of such chemical/mechanical structure-dynamics-function relationships and the mechanisms that generate them. Coarse-grained molecular dynamics models with appropriately designed nonuniform local interactions play an important role in considering the various phenomena caused by large molecular complexes consisting of various proteins and DNA such as nuclear chromosomes. Therefore, in this chapter, we will introduce a method for constructing a coarse-grained molecular dynamics model that simulates the global behavior of each chromosome in the nucleus of a mammalian cell containing many giant chromosomes.

4D Genome Analysis Using PHi-C2.

Hi-C methods reveal 3D genome features but lack correspondence to dynamic chromatin behavior. PHi-C2, Python software, addresses this gap by transforming Hi-C data into polymer models. After the optimization algorithm, it enables us to calculate 3D conformations and conduct dynamic simulations, providing insights into chromatin dynamics, including the mean-squared displacement and rheological properties. This chapter introduces PHi-C2 usage, offering a tutorial for comprehensive 4D genome analysis.

Natural variation at the cotton HIC locus increases trichome density and enhances resistance to aphids.

Plant trichomes are an excellent model for studying cell differentiation and development, providing crucial defenses against biotic and abiotic stresses. There is a well-established inverse relationship between trichome density and aphid prevalence, indicating that higher trichome density leads to reduced aphid infestations. Here we present the cloning and characterization of a dominant quantitative trait locus, HIC (hirsute cotton), which significantly enhances cotton trichome density. This enhancement leads to markedly improved resistance against cotton aphids. The HIC encodes an HD-ZIP IV transcriptional activator, crucial for trichome initiation. Overexpression of HIC leads to a substantial increase in trichome density, while knockdown of HIC results in a marked decrease in density, confirming its role in trichome regulation. We identified a variant in the HIC promoter (-810 bp A to C) that increases transcription of HIC and trichome density in hirsute cotton compared with Gossypium hirsutum cultivars with fewer or no trichomes. Interestingly, although the -810 variant in the HIC promoter is the same in G. barbadense and hirsute cotton, the presence of a copia-like retrotransposon insertion in the coding region of HIC in G. barbadense causes premature transcription termination. Further analysis revealed that HIC positively regulates trichome density by directly targeting the EXPANSIN A2 gene, which is involved in cell wall development. Taken together, our results underscore the pivotal function of HIC as a primary regulator during the initial phases of trichome formation, and its prospective utility in enhancing aphid resistance in superior cotton cultivars via selective breeding.

Chromosome-scale genome of the polyphagous pest Anastrepha ludens (Diptera: Tephritidae) provides insights on sex chromosome evolution in Anastrepha.

The Mexican fruit fly, Anastrepha ludens, is a polyphagous true fruit fly (Diptera: Tephritidae) considered one of the most serious insect pests in Central and North America to various economically relevant fruits. Despite its agricultural relevance, a high-quality genome assembly has not been reported. Here, we described the generation of a chromosome-level genome for the A. ludens using a combination of PacBio high fidelity long-reads and chromatin conformation capture sequencing data. The final assembly consisted of 140 scaffolds (821 Mb, N50 = 131 Mb), containing 99.27% complete conserved orthologs (BUSCO) for Diptera. We identified the sex chromosomes using three strategies: 1) visual inspection of Hi-C contact map and coverage analysis using the HiFi reads, 2) synteny with Drosophila melanogaster, and 3) the difference in the average read depth of autosomal versus sex chromosomal scaffolds. The X chromosome was found in one major scaffold (100 Mb) and eight smaller contigs (1.8 Mb), and the Y chromosome was recovered in one large scaffold (6.1 Mb) and 35 smaller contigs (4.3 Mb). Sex chromosomes and autosomes showed considerable differences of transposable elements and gene content. Moreover, evolutionary rates of orthologs of A. ludens and Anastrepha obliqua revealed a faster evolution of X-linked, compared to autosome-linked, genes, consistent with the faster-X effect, leading us to new insights on the evolution of sex chromosomes in this diverse group of flies. This genome assembly provides a valuable resource for future evolutionary, genetic, and genomic translational research supporting the management of this important agricultural pest.

Chromosome-level genome assembly of the sacoglossan sea slug Elysia timida (Risso, 1818).

BACKGROUND: Sequencing and annotating genomes of non-model organisms helps to understand genome architecture, the genetic processes underlying species traits, and how these genes have evolved in closely-related taxa, among many other biological processes. However, many metazoan groups, such as the extremely diverse molluscs, are still underrepresented in the number of sequenced and annotated genomes. Although sequencing techniques have recently improved in quality and quantity, molluscs are still neglected due to difficulties in applying standardized protocols for obtaining genomic data. RESULTS: In this study, we present the chromosome-level genome assembly and annotation of the sacoglossan sea slug species Elysia timida, known for its ability to store the chloroplasts of its food algae. In particular, by optimizing the long-read and chromosome conformation capture library preparations, the genome assembly was performed using PacBio HiFi and Arima HiC data. The scaffold and contig N50s, at 41.8 Mb and 1.92 Mb, respectively, are approximately 30-fold and fourfold higher compared to other published sacoglossan genome assemblies. Structural annotation resulted in 19,904 protein-coding genes, which are more contiguous and complete compared to publicly available annotations of Sacoglossa with respect to metazoan BUSCOs. We found no evidence for horizontal gene transfer (HGT), i.e. no photosynthetic genes encoded in the sacoglossan nucleus genome. However, we detected genes encoding polyketide synthases in E. timida, indicating that polypropionates are produced. HPLC-MS/MS analysis confirmed the presence of a large number of polypropionates, including known and yet uncharacterised compounds. CONCLUSIONS: We can show that our methodological approach helps to obtain a high-quality genome assembly even for a "difficult-to-sequence" organism, which may facilitate genome sequencing in molluscs. This will enable a better understanding of complex biological processes in molluscs, such as functional kleptoplasty in Sacoglossa, by significantly improving the quality of genome assemblies and annotations.