Search research articles

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

Cell Specific Gene Expression

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

Cell Specific Gene Expression

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

SCOT+: a comprehensive software suite for single-cell alignment using optimal transport.

Bioinformatics advances·2026

Same author

A complete human pancreatic cancer genome.

bioRxiv : the preprint server for biology·2026

Same author

Diffusion-based Representation Integration for Foundation Models Improves Spatial Transcriptomics Analysis.

bioRxiv : the preprint server for biology·2025

Same author

Inferring the regulation dynamics of oscillatory networks from scRNA-seq data.

bioRxiv : the preprint server for biology·2025

Same author

A complete diploid human genome benchmark for personalized genomics.

bioRxiv : the preprint server for biology·2025

Same author

A deep learning model to predict glioma recurrence using integrated genomic and clinical data.

Communications medicine·2025

Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026

Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026

Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026

Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026

Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026

Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026

查看所有相关文章

Search research articles

相关实验视频

Updated: May 10, 2026

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

scGrapHiC:使用单细胞基因表达的Hi-C的基于深度学习的图形解卷.

Ghulam Murtaza¹, Byron Butaney¹, Justin Wagner²

¹Department of Computer Science, Brown University, 115 Waterman Street, Providence, RI, 02912, United States.

Bioinformatics (Oxford, England)

|June 28, 2024

概括

此摘要是机器生成的。

我们开发了scGrapHiC,这是一个深度学习工具,从scRNA-seq数据中预测单细胞Hi-C接触地图. 这种方法增强了对细胞类型特定的染色质相互作用的研究,克服了当前实验协议的局限性.

更多相关视频

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Capturing Chromosome Conformation Across Length Scales

Capturing Chromosome Conformation Across Length Scales

Published on: January 20, 2023

相关实验视频

Last Updated: May 10, 2026

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Capturing Chromosome Conformation Across Length Scales

Capturing Chromosome Conformation Across Length Scales

Published on: January 20, 2023

科学领域:

基因组学和生物信息学
计算生物学计算生物学
表观遗传学在表观遗传学中,表观遗传学是指表观遗传学.

背景情况:

单细胞Hi-C (scHi-C) 揭示了细胞类型特定的染色质相互作用,这对于理解细胞分化和疾病至关重要.
高成本和实验复杂性限制了scHi-C数据的广泛使用.
现有的方法往往不充分利用scHi-C数据集中的丰富信息.

研究的目的:

将 scGrapHiC 引入,这是一个深度学习框架,用于从伪批量 scRNA-seq 数据中预测伪批量 scHi-C 联系地图.
为了使细胞类型特定的scHi-C接触地图能够使用更容易获得的基因组信号生成.
促进对全基因组单细胞相互作用和染色体组织的研究.

主要方法:

scGrapHiC使用图形解卷方法推断单细胞相互作用.
它利用scRNA-seq数据作为指导信号,从批量Hi-C接触地图中提取信息.
该框架在七个细胞类型共同测试数据集上进行了培训和评估.

主要成果:

scGrapHiC在预测scHi-C接触地图方面优于传统的序列编码方法.
与基线方法相比,在恢复特定于细胞类型的拓学关联域方面取得了23.2%的改善.
在未见的胚胎和脑组织样本上展示了概括能力.

结论:

scGrapHiC提供了一种新且有效的方法,用于生成特定细胞类型的scHi-C接触地图.
该框架通过利用广泛可用的scRNA-seq数据,使染色质相互作用的研究民主化.
能够更深入地了解细胞类型特定的基因组组织及其在生物过程中的作用.