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

20.6K
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...
20.6K
Gene Duplication and Divergence02:37

Gene Duplication and Divergence

7.8K
The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are...
7.8K
RNA-seq03:21

RNA-seq

11.7K
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...
11.7K
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.0K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.0K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K
Ribosome Profiling02:24

Ribosome Profiling

4.0K
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...
4.0K

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

Riemannian metric learning for alignment of spatial multiomics.

Bioinformatics (Oxford, England)·2026
Same author

Regulation of Laser-Deposited Silver Microstructures on Ceramic Surfaces and Their Effects on Electrical Conductivity.

Micromachines·2026
Same author

Artificial intelligence for detecting fetal orofacial clefts and advancing medical education.

Nature communications·2026
Same author

Germination Dynamics and Seedling Development of Wheat Under Various Ionic Salt Stresses.

Plants (Basel, Switzerland)·2026
Same author

Mitochondrial Dysfunction in Myoblasts: A TSPO-Dependent Mechanism of Sarcopenia.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026
Same author

Natural-Origin Bioadhesive Injectable Hydrogels Composed of Polyphenol and Chitosan with Antibacterial Activity for Wound Healing.

Gels (Basel, Switzerland)·2026

相关实验视频

Updated: Jan 11, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

626

跨空间转录组学平台的基因表达的联合归算和解卷.

Hongyu Zheng1, Hirak Sarkar1,2, Benjamin J Raphael3

  • 1Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA.

Genome research
|November 17, 2025
PubMed
概括

计算和解卷空间集成 (SIID) 集成来自多个空间解析的转录组学 (SRT) 技术的数据. 这个算法准确地重建了空间基因表达,赋予了缺失的数据,并识别了组织中的细胞类型.

更多相关视频

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

5.3K
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

4.3K

相关实验视频

Last Updated: Jan 11, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

626
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

5.3K
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

4.3K

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:

  • 空间解析转录学 (SRT) 技术为组织微环境中的基因表达提供了洞察力.
  • 现有的SRT平台在空间分辨率,基因覆盖和测序深度方面各不相同.
  • 整合来自互补的SRT技术的数据可以克服单个平台的限制.

研究的目的:

  • 引入空间整合计算和解卷 (SIID),这是一个用于整合来自不同SRT技术的数据的新算法.
  • 通过利用对联观测,使空间基因表达矩阵的准确重建成为可能.
  • 解决单个SRT方法固有的基因归算和细胞类型解卷的局限性.

主要方法:

  • SIID使用空间对齐来记录来自不同SRT模式的数据.
  • 一个共同的非负因子模型被用于数据集成和分析.
  • 该算法从配对的SRT数据中重建了一个潜在的空间基因表达矩阵.

主要成果:

  • 与现有的工具相比,模拟表明SIID在点对细胞类型分配和细胞类型特定基因表达恢复方面的优异性能.
  • 当应用到配对的SRT数据集时,SIID有效地归算出缺失的基因表达数据.
  • 应用到现实世界的人类乳腺和结肠癌数据显示,在赋予坚持不变的基因表达方面具有很高的准确性.

结论:

  • SIID提供了一个强大的框架,用于整合多式SRT数据,增强空间基因表达分析.
  • 该算法克服了单个SRT技术的局限性,使得组织分析更全面.
  • SIID对推进癌症研究和其他利用空间转录学的领域有很大的潜力.