Jove
Visualize
联系我们
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

相关概念视频

DNA Microarrays02:34

DNA Microarrays

17.4K
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
17.4K
Regulated mRNA Transport02:22

Regulated mRNA Transport

6.3K
In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing...
6.3K
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

Retinoic acid drives cell fate specification, maturation and retinal regionality in human retinal organoids.

Nature communications·2026
Same author

Interpretable deep generative ensemble learning for single-cell omics with Hydra.

Molecular systems biology·2026
Same author

Single-Cell and Spatial Transcriptomic Profiling Reveals Epithelial Functional States and Fibroblast Phenotypes in Hormone Therapy-Naïve Localized Prostate Cancer.

Cancer research·2026
Same author

Phosphoproteomics of aged insulin-resistant bone identifies P70S6K phosphorylation of AFF4 as a gene-specific transcriptional regulator.

Nature communications·2025
Same author

Histone methyltransferase PRDM9 promotes survival of drug-tolerant persister cells in glioblastoma.

Nature communications·2025
Same author

Connecting cilium, stress response, and proteostasis abnormalities inform variant and therapy assessment in RPGRIP1 retinal organoids.

Stem cell reports·2025
Same journal

Integrated lipidomic and transcriptomic profiling of the host response in human malaria.

Genome biology·2026
Same journal

Centromeric satellite expansion drives genome evolution in the snowy owl.

Genome biology·2026
Same journal

Mapping the landscape of allele-specific expression in porcine genomes.

Genome biology·2026
Same journal

Genomic sequence evolution underlying human neocortical interareal diversification.

Genome biology·2026
Same journal

Regulatory mechanisms driven by functional 3'-UTR variants in alcohol use disorder and related traits.

Genome biology·2026
Same journal

A longitudinal single-nucleus transcriptomic atlas of bovine placentation reveals dynamic cellular hierarchies and regulatory programs.

Genome biology·2026
查看所有相关文章

相关实验视频

Updated: Jul 5, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

4.9K

评估空间变量基因检测方法用于空间转录组学数据.

Carissa Chen1, Hani Jieun Kim2, Pengyi Yang3,4,5

  • 1Computational Systems Biology Group, Faculty of Medicine and Health, Children's Medical Research Institute, The University of Sydney, Westmead, NSW, 2145, Australia.

Genome biology
|January 15, 2024
PubMed
概括
此摘要是机器生成的。

这项研究对空间转录组学中识别空间变量基因 (SVG) 的方法进行了基准测试. 它揭示了方法之间的显著差异,影响下游分析,并强调需要仔细选择.

更多相关视频

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level
09:45

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

3.0K
Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics
07:43

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Published on: May 3, 2024

2.8K

相关实验视频

Last Updated: Jul 5, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

4.9K
Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level
09:45

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

3.0K
Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics
07:43

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Published on: May 3, 2024

2.8K

科学领域:

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

背景情况:

  • 识别组织内空间变异的基因对于空间转录组学至关重要.
  • 有许多用于检测空间变量基因 (SVG) 的方法,但缺乏基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因

研究的目的:

  • 在各种空间转录学数据集中系统评估流行的SVG检测方法.
  • 评估方法一致性,统计学意义可靠性,准确性,稳定性和下游性能.
  • 考虑SVG识别的计算效率等实际方面.

主要方法:

  • 多个SVG检测算法的全面基准测试.
  • 分析各种组织类型,生物技术和空间分辨率.
  • 评估统计学意义,准确性,稳定性和下游应用程序性能.

主要成果:

  • 在不同的SVG检测方法中观察到显著的差异.
  • 在不同的空间转录组学数据集中,方法性能各不相同.
  • 统计学意义报告的可靠性在不同方法之间显著不同.

结论:

  • 该研究强调了各种方法在SVG识别中的关键差异,影响了数据解释.
  • 为选择适合空间转录学SVG检测工具提供指导.
  • 作为未来开发SVG识别方法的基础参考.