RESUMEN
Several protocols have been established for the generation of lens organoids from embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and other cells with regenerative potential in humans or various animal models. It is important to examine how well the regenerated lens organoids reflect lens biology, in terms of its development, homeostasis, and aging. Toward this goal, the iSyTE database (integrated Systems Tool for Eye gene discovery; https://research.bioinformatics.udel.edu/iSyTE/ ), a bioinformatics resource tool that contains meta-analyzed gene expression data in wild-type lens across different embryonic, postnatal, and adult stages, can serve as a resource for comparative analysis. This article outlines the approaches toward effective use of iSyTE to gain insights into normal gene expression in the mouse lens, enriched expression in the lens, and differential gene expression in select mouse gene-perturbation cataract/lens defects models, which in turn can be used to evaluate expression of key lens-relevant genes in lens organoids by transcriptomics (e.g., RNA-sequencing (RNA-seq), microarrays, etc.) or other downstream methods (e.g., RT-qPCR, etc.).
Asunto(s)
Cristalino , Organoides , Regeneración , Cristalino/citología , Cristalino/metabolismo , Organoides/metabolismo , Organoides/citología , Animales , Ratones , Regeneración/genética , Perfilación de la Expresión Génica/métodos , Biología Computacional/métodos , Simulación por Computador , Humanos , Catarata/genética , Catarata/patología , Catarata/metabolismo , Transcriptoma , Bases de Datos GenéticasRESUMEN
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.
Asunto(s)
Biología Computacional , Genoma Bacteriano , Genómica , Mutación , Genómica/métodos , Biología Computacional/métodos , Bacterias/genética , Bacterias/efectos de los fármacos , Farmacorresistencia Bacteriana/genética , Antibacterianos/farmacología , Programas Informáticos , Secuenciación de Nucleótidos de Alto Rendimiento/métodosRESUMEN
Next-generation sequencing revolutionized food safety management these last years providing access to a huge quantity of valuable data to identify, characterize, and monitor bacterial pathogens on the food chain. Shotgun metagenomics emerged as a particularly promising approach as it enables in-depth taxonomic profiling and functional investigation of food microbial communities. In this chapter, we provide a comprehensive step-by-step bioinformatical workflow to characterize bacterial ecology and resistome composition from metagenomic short-reads obtained by shotgun sequencing.
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Bacterias , Biología Computacional , Microbiología de Alimentos , Secuenciación de Nucleótidos de Alto Rendimiento , Metagenómica , Metagenómica/métodos , Biología Computacional/métodos , Microbiología de Alimentos/métodos , Bacterias/genética , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Metagenoma , Microbiota/genéticaRESUMEN
The standardization of the microbiome sequencing of poultry rinsates is essential for generating comparable microbial composition data among poultry processing facilities if this technology is to be adopted by the industry. Samples must first be acquired, DNA must be extracted, and libraries must be constructed. In order to proceed to library sequencing, the samples should meet quality control standards. Finally, data must be analyzed using computer bioinformatics pipelines. This data can subsequently be incorporated into more advanced computer algorithms for risk assessment. Ultimately, *a uniform sequencing pipeline will enable both the government regulatory agencies and the poultry industry to identify potential weaknesses in food safety.This chapter presents the different steps for monitoring the population dynamics of the microbiome in poultry processing using 16S rDNA sequencing.
Asunto(s)
Biblioteca de Genes , Secuenciación de Nucleótidos de Alto Rendimiento , Microbiota , Aves de Corral , ARN Ribosómico 16S , Animales , ARN Ribosómico 16S/genética , Aves de Corral/microbiología , Microbiota/genética , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Análisis de Secuencia de ADN/métodos , Biología Computacional/métodos , ADN Bacteriano/genéticaRESUMEN
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.
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Cromosoma X , Animales , Cromosoma X/genética , Ratones , Programas Informáticos , Cohesinas , Mapeo Cromosómico/métodos , Proteínas de Ciclo Celular/genética , Proteínas de Ciclo Celular/metabolismo , Proteínas Cromosómicas no Histona/genética , Proteínas Cromosómicas no Histona/metabolismo , Biología Computacional/métodosRESUMEN
Three-dimensional (3D) chromosome structures are closely related to various chromosomal functions, and deep analysis of the structures is crucial for the elucidation of the functions. In recent years, chromosome conformation capture (3C) techniques combined with next-generation sequencing analysis have been developed to comprehensively reveal 3D chromosome structures. Micro-C is one such method that can detect the structures at nucleosome resolution. In this chapter, I provide a basic method for Micro-C analysis. I present and discuss a series of data analyses ranging from mapping to basic downstream analyses, including loop detection.
Asunto(s)
Secuenciación de Nucleótidos de Alto Rendimiento , Programas Informáticos , Flujo de Trabajo , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Humanos , Cromosomas/genética , Biología Computacional/métodos , Mapeo Cromosómico/métodos , Nucleosomas/química , Nucleosomas/genética , Nucleosomas/metabolismoRESUMEN
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.
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Genoma Bacteriano , Programas Informáticos , Genómica/métodos , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Células Procariotas/metabolismo , Genoma Arqueal , Archaea/genética , Bacterias/genética , Biología Computacional/métodos , Análisis de DatosRESUMEN
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.
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Mapeo Cromosómico , Biología Computacional , Secuenciación de Nucleótidos de Alto Rendimiento , Programas Informáticos , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Biología Computacional/métodos , Humanos , Mapeo Cromosómico/métodos , Cromosomas/genética , Genómica/métodos , Cromatina/genética , Cromatina/químicaRESUMEN
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.
Asunto(s)
Secuenciación de Nucleótidos de Alto Rendimiento , Programas Informáticos , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Biología Computacional/métodos , Navegador Web , Flujo de Trabajo , Humanos , Cromatina/genética , Genómica/métodosRESUMEN
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.
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Algoritmos , Cromatina , Programas Informáticos , Cromatina/genética , Cromatina/metabolismo , Biología Computacional/métodos , Humanos , AnimalesRESUMEN
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 .
Asunto(s)
Cromatina , Secuenciación de Nucleótidos de Alto Rendimiento , Programas Informáticos , Flujo de Trabajo , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Cromatina/genética , Cromatina/metabolismo , Humanos , Genómica/métodos , Biología Computacional/métodos , Secuenciación de Inmunoprecipitación de Cromatina/métodosRESUMEN
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.
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Cromatina , Biología Computacional , Genómica , Programas Informáticos , Cromatina/genética , Biología Computacional/métodos , Genómica/métodos , Humanos , Secuenciación de Nucleótidos de Alto Rendimiento/métodosRESUMEN
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.
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Genómica , Análisis de la Célula Individual , Programas Informáticos , Flujo de Trabajo , Análisis de la Célula Individual/métodos , Animales , Genómica/métodos , Drosophila/genética , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Biología Computacional/métodosRESUMEN
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.
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Cromatina , Biología Computacional , Programas Informáticos , Cromatina/metabolismo , Cromatina/genética , Biología Computacional/métodos , Humanos , Aprendizaje Automático Supervisado , Conformación de Ácido NucleicoRESUMEN
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.
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Programas Informáticos , Biología Computacional/métodos , Genómica/métodos , Humanos , GenomaRESUMEN
Disentangling the relationship of enhancers and genes is an ongoing challenge in epigenomics. We present STARE, our software to quantify the strength of enhancer-gene interactions based on enhancer activity and chromatin contact data. It implements the generalized Activity-by-Contact (gABC) score, which allows predicting putative target genes of candidate enhancers over any desired genomic distance. The only requirement for its application is a measurement of enhancer activity. In addition to regulatory interactions, STARE calculates transcription factor (TF) affinities on gene level. We illustrate its usage on a public single-cell data set of the human heart by predicting regulatory interactions on cell type level, by giving examples on how to integrate them with other data modalities, and by constructing TF affinity matrices.
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Cromatina , Elementos de Facilitación Genéticos , Epigenómica , Programas Informáticos , Humanos , Cromatina/genética , Cromatina/metabolismo , Epigenómica/métodos , Epigenoma , Factores de Transcripción/metabolismo , Factores de Transcripción/genética , Biología Computacional/métodosRESUMEN
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.
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Algoritmos , Cromatina , Programas Informáticos , Cromatina/genética , Cromatina/química , Cromatina/metabolismo , Humanos , Genómica/métodos , Genoma , Biología Computacional/métodosRESUMEN
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.
Asunto(s)
Roturas del ADN de Doble Cadena , Saccharomyces cerevisiae , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/metabolismo , Secuenciación de Inmunoprecipitación de Cromatina/métodos , Programas Informáticos , Meiosis/genética , Genoma Fúngico , Mapeo Cromosómico/métodos , Endodesoxirribonucleasas/metabolismo , Endodesoxirribonucleasas/genética , Biología Computacional/métodos , Proteínas de Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/metabolismo , ADN de Hongos/genética , ADN de Hongos/metabolismoRESUMEN
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).
Asunto(s)
Epigénesis Genética , Epigenómica , Transcriptoma , Humanos , Epigenómica/métodos , Transcriptoma/genética , Elementos de Facilitación Genéticos , Programas Informáticos , Biología Computacional/métodos , Secuenciación de Inmunoprecipitación de Cromatina/métodos , Regulación de la Expresión Génica , Algoritmos , Histonas/genética , Histonas/metabolismo , Perfilación de la Expresión Génica/métodosRESUMEN
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.