RESUMO
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.
Assuntos
Cromatina , Aprendizado Profundo , Cromatina/genética , Cromatina/metabolismo , Humanos , Biologia Computacional/métodos , Aprendizado de Máquina , Genômica/métodos , Elementos Facilitadores Genéticos , Regiões Promotoras Genéticas , Sítios de Ligação , Genoma , SoftwareRESUMO
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.
Assuntos
Cromatina , Sequenciamento de Nucleotídeos em Larga Escala , Cromatina/genética , Cromatina/metabolismo , Cromatina/química , Humanos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Genômica/métodos , Estudo de Associação Genômica Ampla/métodos , AnimaisRESUMO
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.
Assuntos
Embrião não Mamífero , Oryzias , Animais , Oryzias/embriologia , Genoma , Desenvolvimento Embrionário , Genômica/métodosRESUMO
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.
Assuntos
Genoma Bacteriano , Software , Genômica/métodos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Células Procarióticas/metabolismo , Genoma Arqueal , Archaea/genética , Bactérias/genética , Biologia Computacional/métodos , Análise de DadosRESUMO
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.
Assuntos
Mapeamento Cromossômico , Biologia Computacional , Sequenciamento de Nucleotídeos em Larga Escala , Software , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Biologia Computacional/métodos , Humanos , Mapeamento Cromossômico/métodos , Cromossomos/genética , Genômica/métodos , Cromatina/genética , Cromatina/químicaRESUMO
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.
Assuntos
Sequenciamento de Nucleotídeos em Larga Escala , Software , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Biologia Computacional/métodos , Navegador , Fluxo de Trabalho , Humanos , Cromatina/genética , Genômica/métodosRESUMO
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 .
Assuntos
Cromatina , Sequenciamento de Nucleotídeos em Larga Escala , Software , Fluxo de Trabalho , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Cromatina/genética , Cromatina/metabolismo , Humanos , Genômica/métodos , Biologia Computacional/métodos , Sequenciamento de Cromatina por Imunoprecipitação/métodosRESUMO
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.
Assuntos
Cromatina , Biologia Computacional , Genômica , Software , Cromatina/genética , Biologia Computacional/métodos , Genômica/métodos , Humanos , Sequenciamento de Nucleotídeos em Larga Escala/métodosRESUMO
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.
Assuntos
Genômica , Análise de Célula Única , Software , Fluxo de Trabalho , Análise de Célula Única/métodos , Animais , Genômica/métodos , Drosophila/genética , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Biologia Computacional/métodosRESUMO
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.
Assuntos
Software , Biologia Computacional/métodos , Genômica/métodos , Humanos , GenomaRESUMO
Polymer modeling has been playing an increasingly important role in complementing 3D genome experiments, both to aid their interpretation and to reveal the underlying molecular mechanisms. This chapter illustrates an application of Hi-C metainference, a Bayesian approach to explore the 3D organization of a target genomic region by integrating experimental contact frequencies into a prior model of chromatin. The method reconstructs the conformational ensemble of the target locus by combining molecular dynamics simulation and Monte Carlo sampling from the posterior probability distribution given the data. Using prior chromatin models at both 1 kb and nucleosome resolution, we apply this approach to a 30 kb locus of mouse embryonic stem cells consisting of two well-defined domains linking several gene promoters together. Retaining the advantages of both physics-based and data-driven strategies, Hi-C metainference can provide an experimentally consistent representation of the system while at the same time retaining molecular details necessary to derive physical insights.
Assuntos
Teorema de Bayes , Cromatina , Simulação de Dinâmica Molecular , Animais , Camundongos , Cromatina/genética , Cromatina/química , Cromatina/metabolismo , Genoma , Genômica/métodos , Método de Monte Carlo , Células-Tronco Embrionárias Murinas/metabolismoRESUMO
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.
Assuntos
Algoritmos , Cromatina , Software , Cromatina/genética , Cromatina/química , Cromatina/metabolismo , Humanos , Genômica/métodos , Genoma , Biologia Computacional/métodosRESUMO
Cohesin is a protein complex that plays a key role in regulating chromosome structure and gene expression. While next-generation sequencing technologies have provided extensive information on various aspects of cohesin, integrating and exploring the vast datasets associated with cohesin are not straightforward. CohesinDB ( https://cohesindb.iqb.u-tokyo.ac.jp ) offers a web-based interface for browsing, searching, analyzing, visualizing, and downloading comprehensive multiomics cohesin information in human cells. In this protocol, we introduce how to utilize CohesinDB to facilitate research on transcriptional regulation and chromatin organization.
Assuntos
Proteínas de Ciclo Celular , Proteínas Cromossômicas não Histona , Coesinas , Navegador , Proteínas Cromossômicas não Histona/metabolismo , Proteínas Cromossômicas não Histona/genética , Proteínas de Ciclo Celular/metabolismo , Proteínas de Ciclo Celular/genética , Humanos , Software , Biologia Computacional/métodos , Genômica/métodos , Bases de Dados Genéticas , Cromatina/metabolismo , Cromatina/genética , Internet , MultiômicaRESUMO
Unveiling the strategies of bacterial adaptation to stress constitute a challenging area of research. The understanding of mechanisms governing emergence of resistance to antimicrobials is of particular importance regarding the increasing threat of antibiotic resistance on public health worldwide. In the last decades, the fast democratization of sequencing technologies along with the development of dedicated bioinformatical tools to process data offered new opportunities to characterize genomic variations underlying bacterial adaptation. Thereby, research teams have now the possibility to dive deeper in the deciphering of bacterial adaptive mechanisms through the identification of specific genetic targets mediating survival to stress. In this chapter, we proposed a step-by-step bioinformatical pipeline enabling the identification of mutational events underlying biocidal stress adaptation associated with antimicrobial resistance development using Escherichia marmotae as an illustrative model.
Assuntos
Biologia Computacional , Genoma Bacteriano , Genômica , Mutação , Genômica/métodos , Biologia Computacional/métodos , Bactérias/genética , Bactérias/efeitos dos fármacos , Farmacorresistência Bacteriana/genética , Antibacterianos/farmacologia , Software , Sequenciamento de Nucleotídeos em Larga Escala/métodosRESUMO
This document outlines the steps necessary to assemble and submit the standard data package required for contributing to the global genomic surveillance of enteric pathogens. Although targeted to GenomeTrakr laboratories and collaborators, these protocols are broadly applicable for enteric pathogens collected for different purposes. There are five protocols included in this chapter: (1) quality control (QC) assessment for the genome sequence data, (2) validation for the contextual data, (3) data submission for the standard pathogen package or Pathogen Data Object Model (DOM) to the public repository, (4) viewing and querying data at NCBI, and (5) data curation for maintaining relevance of public data. The data are available through one of the International Nucleotide Sequence Database Consortium (INSDC) members, with the National Center for Biotechnology Information (NCBI) being the primary focus of this document. NCBI Pathogen Detection is a custom dashboard at NCBI that provides easy access to pathogen data plus results for a standard suite of automated cluster and genotyping analyses important for informing public health and regulatory decision-making.
Assuntos
Genômica , Controle de Qualidade , Humanos , Genômica/métodos , Genômica/normas , Bases de Dados Genéticas , Software , Genoma Bacteriano , Curadoria de Dados/métodosRESUMO
Purpose/objectives: Biomarkers for extracranial oligometastatic disease remain elusive and few studies have attempted to correlate genomic data to the presence of true oligometastatic disease. Methods: Patients with non-small cell lung cancer (NSCLC) and brain metastases were identified in our departmental database. Electronic medical records were used to identify patients for whom liquid biopsy-based comprehensive genomic profiling (Guardant Health) was available. Extracranial oligometastatic disease was defined as patients having ≤5 non-brain metastases without diffuse involvement of a single organ. Widespread disease was any spread beyond oligometastatic. Fisher's exact tests were used to screen for mutations statistically associated (p<0.1) with either oligometastatic or widespread extracranial disease. A risk score for the likelihood of oligometastatic disease was generated and correlated to the likelihood of having oligometastatic disease vs widespread disease. For oligometastatic patients, a competing risk analysis was done to assess for cumulative incidence of oligometastatic progression. Cox regression was used to determine association between oligometastatic risk score and oligoprogression. Results: 130 patients met study criteria and were included in the analysis. 51 patients (39%) had extracranial oligometastatic disease. Genetic mutations included in the Guardant panel that were associated (p<0.1) with the presence of oligometastatic disease included ATM, JAK2, MAP2K2, and NTRK1, while ARID1A and CCNE1 were associated with widespread disease. Patients with a positive, neutral and negative risk score for oligometastatic disease had a 78%, 41% and 11.5% likelihood of having oligometastatic disease, respectively (p<0.0001). Overall survival for patients with positive, neutral and negative risk scores for oligometastatic disease was 86% vs 82% vs 64% at 6 months (p=0.2). Oligometastatic risk score was significantly associated with the likelihood of oligoprogression based on the Wald chi-square test. Patients with positive, neutral and negative risk scores for oligometastatic disease had a cumulative incidence of oligometastatic progression of 77% vs 35% vs 33% at 6 months (p=0.03). Conclusions: Elucidation of a genomic signature for extracranial oligometastatic disease derived from non-invasive liquid biopsy appears feasible for NSCLC patients. Patients with this signature exhibited higher rates of early oligoprogression. External validation could lead to a biomarker that has the potential to direct local therapies in oligometastatic patients.
Assuntos
Neoplasias Encefálicas , Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Humanos , Carcinoma Pulmonar de Células não Pequenas/genética , Carcinoma Pulmonar de Células não Pequenas/patologia , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/patologia , Masculino , Neoplasias Encefálicas/secundário , Neoplasias Encefálicas/genética , Feminino , Pessoa de Meia-Idade , Idoso , Biomarcadores Tumorais/genética , Adulto , Mutação , Genômica/métodos , Prognóstico , Idoso de 80 Anos ou mais , Progressão da DoençaRESUMO
Personalized treatment for patients with advanced solid tumors critically depends on the deep characterization of tumor cells from patient biopsies. Here, we comprehensively characterize a pan-cancer cohort of 150 malignant serous effusion (MSE) samples at the cellular, molecular, and functional level. We find that MSE-derived cancer cells retain the genomic and transcriptomic profiles of their corresponding primary tumors, validating their use as a patient-relevant model system for solid tumor biology. Integrative analyses reveal that baseline gene expression patterns relate to global ex vivo drug sensitivity, while high-throughput drug-induced transcriptional changes in MSE samples are indicative of drug mode of action and acquired treatment resistance. A case study exemplifies the added value of multi-modal MSE profiling for patients who lack genetically stratified treatment options. In summary, our study provides a functional multi-omics view on a pan-cancer solid tumor cohort and underlines the feasibility and utility of MSE-based precision oncology.
Assuntos
Neoplasias , Medicina de Precisão , Humanos , Medicina de Precisão/métodos , Neoplasias/genética , Neoplasias/tratamento farmacológico , Neoplasias/patologia , Feminino , Transcriptoma , Regulação Neoplásica da Expressão Gênica , Masculino , Perfilação da Expressão Gênica/métodos , Idoso , Pessoa de Meia-Idade , Derrame Pleural Maligno/genética , Derrame Pleural Maligno/patologia , Derrame Pleural Maligno/metabolismo , Estudos de Coortes , Biomarcadores Tumorais/genética , Biomarcadores Tumorais/metabolismo , Genômica/métodos , Antineoplásicos/uso terapêutico , Antineoplásicos/farmacologia , Resistencia a Medicamentos Antineoplásicos/genéticaRESUMO
Many mammal species have declining populations, but the consequences of small population size on the genomic makeup of species remain largely unknown. We investigated the evolutionary history, genetic load and adaptive potential of the Cat Ba langur (Trachypithecus poliocephalus), a primate species endemic to Vietnam's famous Ha Long Bay and with less than 100 living individuals one of the most threatened primates in the world. Using high-coverage whole genome data of four wild individuals, we revealed the Cat Ba langur as sister species to its conspecifics of the northern limestone langur clade and found no evidence for extensive secondary gene flow after their initial separation. Compared to other primates and mammals, the Cat Ba langur showed low levels of genetic diversity, long runs of homozygosity, high levels of inbreeding and an excess of deleterious mutations in homozygous state. On the other hand, genetic diversity has been maintained in protein-coding genes and on the gene-rich human chromosome 19 ortholog, suggesting that the Cat Ba langur retained most of its adaptive potential. The Cat Ba langur also exhibits several unique non-synonymous variants that are related to calcium and sodium metabolism, which may have improved adaptation to high calcium intake and saltwater consumption.
Assuntos
Espécies em Perigo de Extinção , Variação Genética , Densidade Demográfica , Animais , Vietnã , Adaptação Fisiológica/genética , Genoma/genética , Filogenia , Cromossomos Humanos Par 19/genética , Homozigoto , Humanos , Fluxo Gênico , Genômica/métodos , Endogamia , Cálcio/metabolismoRESUMO
Aedes aegypti and Aedes albopictus mosquitoes spread major vector-borne viral diseases in tropical and sub-tropical regions of the globe. In this study, we sequenced the genome of Indian Ae. aegypti and Ae. albopictus and mapped to their reference genomes. Comparative genomics were performed between our strain and the reference strains. A total of 14,416,484 single nucleotide polymorphisms (SNPs) and 156,487 insertions and deletions (InDels) were found in Ae. aegypti, and 28,940,433 SNPs and 188,987 InDels in Ae. albopictus. Particular emphasis was given to gene families involved in mosquito digestion, development, and innate immunity, which could be putative candidates for vector control. Serine protease cascades and their inhibitors called serpins, play a central role in these processes. We extracted high-impact variants in genes associated with serine proteases and serpins. This study reports for the first time a high coverage genome sequence data of an Indian Ae. albopictus mosquito. The results from this study will provide insights into Indian Aedes specific polymorphisms and the evolution of immune related genes in mosquitoes, which can serve as a resource for future comparative genomics and those pursuing the development of targeted biopesticides for effective mosquito control strategies.
Assuntos
Aedes , Mosquitos Vetores , Polimorfismo de Nucleotídeo Único , Aedes/genética , Animais , Índia , Mosquitos Vetores/genética , Genoma de Inseto , Mutação INDEL , Sequenciamento Completo do Genoma/métodos , Genômica/métodosRESUMO
Patients with relapsed/refractory (r/r) diffuse large B-cell lymphoma (DLBCL) show varied responses to PD-1 monoclonal antibody (mAb) containing regimens. The mechanisms and predictive biomarkers for the efficacy of this regimen are unclear. This study retrospectively collected r/r DLBCL patients who received PD-1 mAb and rituximab regimens as salvage therapy. Clinical and genomic features were collected, and mechanisms were explored by multiplex immunofluorescence and digital spatial profiling. An artificial neural network (ANN) model was constructed to predict the response. Between October 16th, 2018 and May 4th, 2023, 50 r/r DLBCL patients were collected, 29 were response patients and 21 were non-response patients. CREBBP (p = 0.029) and TP53 (p = 0.015) alterations were statistically higher in non-response patients. Patients with PD-L1 CPS ≥ 5 were correlated with a longer overall survival (OS) than those with PD-L1 CPS < 5 (median OS: not reached vs. 9.7 months, hazard ratio [HR]: 3.8, 95% confidence interval [CI] 0.64-22.44, p = 0.016). Immune-related pathways were activated in response patients. The proportion and spatial organization of tumor-infiltrating immune cells affect the response. PD-L1 CPS level, age, and alterations of TP53, MYD88, CREBBP, EP300, GNA13 were used to build an ANN predictive model that showed high prediction efficiency (training set area under curve [AUC] of 0.97 and test set AUC of 0.94). The proportion and spatial distribution of tumor-infiltrating immune cells may be related to the function of immune-related pathways, thereby influencing the efficacy of PD-1 mAb containing regimens. The ANN predictive model showed potential value in predicting the responses of r/r DLBCL patients received PD-1 mAb and rituximab regimens.