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

Ribosome Profiling02:24

Ribosome Profiling

4.2K
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...
4.2K
DNA Microarrays02:34

DNA Microarrays

21.6K
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...
21.6K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

16.2K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
16.2K

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

Stereotactic centralized ablative radiation therapy: a framework for ultra-heterogeneous radiotherapy of bulky tumors.

Frontiers in oncology·2026
Same author

Preliminary Comparative Analysis of Circulatory Glycosaminoglycan Concentrations and Disaccharide Profiles in Diabetic and Healthy Subjects.

The Eurasian journal of medicine·2026
Same author

Treatable traits of small airway dysfunction in children with asthma and advances in small airway-targeted therapeutic research: a narrative review.

Translational pediatrics·2026
Same author

Deep Prior Framework: integrating functional specificity with general plausibility for targeted protein evolution.

Briefings in bioinformatics·2026
Same author

Targeting ATF3-mediated asparagine biosynthesis reverses acquired resistance to KRAS<sup>G12C</sup> inhibitors.

Oncogene·2026
Same author

StackAge: an ensemble-based clock for precise quantification of biological age using multi-omics data.

Briefings in bioinformatics·2026

相关实验视频

Updated: Mar 7, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

686

Castl:通过基于集合的框架在空间转录组学中对空间变量的基因进行可靠的识别.

Yiyi Yu1, Jiyuan Yang2, Ping-An He1

  • 1Department of Mathematics, College of Science, Zhejiang Sci-Tech University, 928 2nd Avenue, Qiantang District, Hangzhou, Zhejiang 310018, China.

Briefings in bioinformatics
|March 6, 2026
PubMed
概括

Castl是一个新的计算框架,集成了多种方法来识别组织中的空间变量基因 (SVGs). 它准确地检测空间模式,并控制各种数据集的错误发现.

关键词:
达成共识的框架框架空间分辨的转录学 编译学空间变量的基因

更多相关视频

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

920
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

5.5K

相关实验视频

Last Updated: Mar 7, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

686
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

920
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

5.5K

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:

  • 空间分辨的转录学使得组织组织的研究成为可能.
  • 识别空间变量基因 (SVGs) 对这个领域至关重要.
  • 现有的SVG识别方法存在局限性,包括由于算法特定假设的敏感度变化和高错误发现率 (FDR).

研究的目的:

  • 开发一个强大而灵活的框架来识别空间变量基因 (SVGs).
  • 解决现有的SVG检测方法的局限性,例如假设依赖性和不一致的性能.
  • 通过空间转录学数据,提供一种标准化的方法,用于复杂的生物系统中发现特征.

主要方法:

  • 开发了Castl,这是用于SVG识别的基于集合的计算框架.
  • 使用统计设计的聚合模块集成多种SVG检测方法.
  • 评估了Castl在模拟和现实世界的空间转录组数据集上的表现.

主要成果:

  • 卡斯特尔始终确定了生物学上有意义的空间基因表达模式.
  • 该框架有效地减轻了个别检测方法固有的偏差.
  • 在不同的生物环境,分辨率和空间技术中,Castl展示了对错误发现率 (FDR) 的强有力的控制.
  • 综合性评估证实了Castl与现有方法相比的优越性能.

结论:

  • 卡斯特尔为可靠的SVG识别提供了一个灵活的,无假设的框架.
  • 这种合体方法为空间信息的特征发现提供了标准化的基础.
  • 通过精确的空间转录学数据解释,Castl 增强了复杂生物系统的分析.