Jove
Visualize
联系我们

相关概念视频

RNA-seq03:21

RNA-seq

9.9K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
9.9K
Ribosome Profiling02:24

Ribosome Profiling

3.5K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.5K

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

Sublytic C5b-9 triggers glomerular mesangial cell apoptosis in rat Thy-1 nephritis via Gadd45 activation mediated by Egr-1 and p300-dependent ATF3 acetylation.

Journal of molecular cell biology·2016
Same author

Improving the hydrogen selectivity of graphene oxide membranes by reducing non-selective pores with intergrown ZIF-8 crystals.

Chemical communications (Cambridge, England)·2016
Same author

The Association Between Genetic Polymorphism rs703842 in CYP27B1 and Multiple Sclerosis: A Meta-Analysis.

Medicine·2016
Same author

High-Flexibility, High-Toughness Double-Cross-Linked Chitin Hydrogels by Sequential Chemical and Physical Cross-Linkings.

Advanced materials (Deerfield Beach, Fla.)·2016
Same author

Synthesis, structure, and magnetic and catalytic properties of metal frameworks with 2,2'-dinitro-4,4'-biphenyldicarboxylate and imidazole-containing tripodal ligands.

Dalton transactions (Cambridge, England : 2003)·2016
Same author

Reactive oxygen species and hormone signaling cascades in endophytic bacterium induced essential oil accumulation in Atractylodes lancea.

Planta·2016
Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024
查看所有相关文章
JoVE
x logofacebook logolinkedin logoyoutube logo
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 JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jun 23, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.5K

使用核心集分析大规模单细胞RNA-Seq数据.

Khalid Usman, Fangping Wan, Dan Zhao

    IEEE/ACM transactions on computational biology and bioinformatics
    |June 24, 2024
    PubMed
    概括
    此摘要是机器生成的。

    单细胞Coreset (scCoreset) 是一个新的框架,用于总结大型单细胞RNA测序数据集. 它有效地提取小部分的细胞,使得更快和可比的下游分析,如集群和可视化.

    更多相关视频

    Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies
    05:45

    Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

    Published on: March 29, 2024

    2.2K
    Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy
    04:21

    Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy

    Published on: January 19, 2024

    2.8K

    相关实验视频

    Last Updated: Jun 23, 2025

    Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
    10:12

    Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

    Published on: January 10, 2019

    18.5K
    Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies
    05:45

    Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

    Published on: March 29, 2024

    2.2K
    Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy
    04:21

    Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy

    Published on: January 19, 2024

    2.8K

    科学领域:

    • 基因组学就是基因组学.
    • 计算生物学 计算生物学
    • 生物信息学是一种生物信息学.

    背景情况:

    • 单细胞测序技术为细胞转录组提供了深入的见解.
    • 对大规模单细胞数据集的分析带来了重大的计算挑战.
    • 现有的缩小维度的方法与大,稀疏的数据集作斗争.

    研究的目的:

    • 为大型单细胞RNA-seq数据开发一个高效的数据总结框架.
    • 为了促进下游分析,如集群和可视化.
    • 克服当前单细胞数据分析方法的计算局限性.

    主要方法:

    • 引入单细胞Coreset (scCoreset),一个新的数据总结框架.
    • 从大,稀疏的单细胞RNA-seq数据中提取一个小的,加权的细胞子集.
    • 对各种单细胞数据集的scCoreset性能进行评估,用于常见的下游任务.

    主要成果:

    • scCoreset有效地总结了大型单细胞数据集.
    • 对scCoreset子集进行的下游分析产生与原始数据可比的结果.
    • 与现有的可视化和聚类总结方法相比,scCoreset表现出卓越的性能.

    结论:

    • scCoreset为处理大型单细胞RNA-seq数据集提供了一种高效的解决方案.
    • 该框架显著提高了常见下游分析任务的效率.
    • scCoreset是一个有价值的插件工具,用于增强单细胞RNA-seq数据分析工作流.