AI Article Synopsis
- Single-cell Hi-C (scHi-C) is a set of methods for analyzing genomic interactions at the individual cell level.
- Although the data analysis for scHi-C is similar to that of bulk Hi-C, scHi-C presents unique challenges like high noise and specific biases that require tailored data processing strategies.
- This tutorial focuses on using pairtools for scHi-C data analysis, with a hands-on example from a Drosophila snHi-C dataset, and aims to help researchers navigate essential steps in scHi-C analysis using open-source tools.
Article Abstract
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.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/978-1-0716-4136-1_14 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
scHiGex: predicting single-cell gene expression based on single-cell Hi-C data.
NAR Genom Bioinform
March 2025
Department of Computer Science, University of Miami, Coral Gables, FL 33146, United States.
A novel biochemistry experiment named HiRES has been developed to capture both the chromosomal conformations and gene expression levels of individual single cells simultaneously. Nevertheless, when compared to the extensive volume of single-cell Hi-C data generated from individual cells, the number of datasets produced from this experiment remains limited in the scientific community. Hence, there is a requirement for a computational tool that can forecast the levels of gene expression in individual cells using single-cell Hi-C data from the same cells.
View Article and Find Full Text PDFAberrant c-AMP signalling in richter syndrome revealed by single-cell transcriptome and 3D chromatin analysis.
Biomark Res
January 2025
Department of Hematology, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China.
Richter syndrome (RS), characterized by aggressive lymphoma arising from chronic lymphocytic leukaemia (CLL), presents a poor response to treatment and grim prognosis. To elucidate RS mechanisms, paired samples from a patient with DLBCL-RS were subjected to single-cell RNA sequencing (scRNA-seq) and high-throughput chromosome conformation capture (Hi-C) sequencing. Over 10,000 cells were profiled via scRNA-seq, revealing the comprehensive B cell transformation in RS.
View Article and Find Full Text PDFCutting-edge tools for unveiling the dynamics of plasmid-host interactions.
Trends Microbiol
January 2025
Ineos Oxford Institute for Antimicrobial Research, Department of Biology, University of Oxford, Oxford OX1 3RE, UK. Electronic address:
The plasmid-mediated transfer of antibiotic resistance genes (ARGs) in complex microbiomes presents a significant global health challenge. This review examines recent technological advancements that have enabled us to move beyond the limitations of culture-dependent detection of conjugation and have enhanced our ability to track and understand the movement of ARGs in real-world scenarios. We critically assess the applications of single-cell sequencing, fluorescence-based techniques and advanced high-throughput chromatin conformation capture (Hi-C) approaches in elucidating plasmid-host interactions at unprecedented resolution.
View Article and Find Full Text PDFDroplet-based high-throughput 3D genome structure mapping of single cells with simultaneous transcriptomics.
Cell Discov
January 2025
Biomedical Pioneering Innovation Center (BIOPIC), and School of Life Sciences, Peking University, Beijing, China.
Single-cell three-dimensional (3D) genome techniques have advanced our understanding of cell-type-specific chromatin structures in complex tissues, yet current methodologies are limited in cell throughput. Here we introduce a high-throughput single-cell Hi-C (dscHi-C) approach and its transcriptome co-assay (dscHi-C-multiome) using droplet microfluidics. Using dscHi-C, we investigate chromatin structural changes during mouse brain aging by profiling 32,777 single cells across three developmental stages (3 months, 12 months, and 23 months), yielding a median of 78,220 unique contacts.
View Article and Find Full Text PDFscHiClassifier: a deep learning framework for cell type prediction by fusing multiple feature sets from single-cell Hi-C data.
Brief Bioinform
November 2024
School of Software, Shandong University, No. 1500, Shunhua Road, Hi-Tech Industrial Development Zone, Jinan 250100, Shandong, China.
Single-cell high-throughput chromosome conformation capture (Hi-C) technology enables capturing chromosomal spatial structure information at the cellular level. However, to effectively investigate changes in chromosomal structure across different cell types, there is a requisite for methods that can identify cell types utilizing single-cell Hi-C data. Current frameworks for cell type prediction based on single-cell Hi-C data are limited, often struggling with features interpretability and biological significance, and lacking convincing and robust classification performance validation.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!