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

Proteomics01:33

Proteomics

9.2K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
9.2K

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

Cell-type-resolved genetic variation shapes inflammatory bowel disease risk.

Nature·2026
Same author

The spatial proteome of the Plasmodium falciparum schizont illuminates the composition and evolutionary trajectories of its organelles.

Nature communications·2026
Same author

Annealed variational mixtures for disease subtyping and biomarker discovery.

Statistical applications in genetics and molecular biology·2026
Same author

Correction: Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses.

PLoS genetics·2026
Same author

Design and interpretation of eQTL-GWAS colocalisation studies: Lessons from a large-scale evaluation.

PLoS genetics·2026
Same author

Dynamic subcellular proteomics identifies regulators of adipocyte insulin action.

Nature communications·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
Same journal

Exploring the structural lexicon of the Proteome via Metric Geometry.

PLoS computational biology·2026
Same journal

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026
Same journal

CAdir: Joint clustering of cells and genes for single-cell transcriptomics with visualization-driven cluster quality assessment.

PLoS computational biology·2026
Same journal

Systematic design of auxotrophic strains and media conditions to probe metabolic functions in E. coli.

PLoS computational biology·2026
Same journal

Neuronal excitability and parameter variability in the Hodgkin-Huxley model.

PLoS computational biology·2026
查看所有相关文章

相关实验视频

Updated: Jan 8, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.6K

多个空间蛋白质组学数据集的半监督贝叶斯集成.

Stephen Coleman1, Lisa Breckels2, Ross F Waller2

  • 1MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom.

PLoS computational biology
|December 15, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的贝叶斯方法,将空间蛋白质组学与其他数据相结合,以便更好地预测蛋白质定位. 该方法通过分析Toxoplasma gondii细胞周期数据来增强对寄生虫蛋白功能和局部化的理解.

更多相关视频

Dual-modality Molecular Cartography: Integrating Multiplex mRNA Detection with Protein Imaging Mass Cytometry
06:51

Dual-modality Molecular Cartography: Integrating Multiplex mRNA Detection with Protein Imaging Mass Cytometry

Published on: November 14, 2025

256
Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
12:11

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

7.2K

相关实验视频

Last Updated: Jan 8, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.6K
Dual-modality Molecular Cartography: Integrating Multiplex mRNA Detection with Protein Imaging Mass Cytometry
06:51

Dual-modality Molecular Cartography: Integrating Multiplex mRNA Detection with Protein Imaging Mass Cytometry

Published on: November 14, 2025

256
Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
12:11

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

7.2K

科学领域:

  • 蛋白质组学是指蛋白质组学.
  • 系统生物学 系统生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 蛋白质亚细胞局部化对于功能至关重要.
  • 空间蛋白质组学和其他奥米克数据为蛋白质定位提供了洞察力.
  • 现有的整合方法在数据类型和不确定性量化方面是有限的.

研究的目的:

  • 开发一种半监督的贝叶斯方法,将空间蛋白质组与多种数据源集成在一起.
  • 通过量化预测不确定性来改善蛋白质细胞下定位的推断.
  • 为整合分类,连续和时间数据提供灵活的方法.

主要方法:

  • 开发了一种半监督的贝叶斯模型,用于将空间蛋白质组学与其他数据集成.
  • 从标记标记蛋白和未标记数据推断的模型参数.
  • 蛋白质局部化推断中的量化预测不确定性.
  • 将该方法应用于Toxoplasma gondii空间蛋白质组学和细胞周期基因表达数据.

主要成果:

  • 建议的贝叶斯式方法的性能优于现有的转移学习方法.
  • 在建模各种数据类型中表现出灵活性,包括注释,丰度和时间序列表达式.
  • 确定了蛋白质表达程序,在T. gondii的第一个细胞周期结束时达到峰值.
  • 在密集的颗粒蛋白中揭示了异质群体,表明了多样化的功能.

结论:

  • 新的贝叶斯方法显著改善了蛋白质亚细胞局部化推断.
  • 这种方法为整合性奥米克分析提供了一个灵活而强大的框架.
  • 这些发现为T. gondii蛋白质的功能作用和定位提供了新的见解.
  • 该方法作为mdir R包提供,用于更广泛的科学用途.