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

7.4K
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...
7.4K
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

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

Nature communications·2026
Same author

Dynamic subcellular proteomics identifies regulators of adipocyte insulin action.

Nature communications·2026
Same author

Subcellular localization as a driver of protein function.

Nature reviews. Molecular cell biology·2026
Same author

Functional characterisation of tumour suppressor PDCD4 reveals previously undisclosed role in the control of cell adhesion.

Nucleic acids research·2026
Same author

Semi-supervised Bayesian integration of multiple spatial proteomics datasets.

PLoS computational biology·2025
Same author

The distinct roles of genome, methylation, transcription, and translation on protein expression in Arabidopsis thaliana resolve the Central Dogma's information flow.

Genome biology·2025
Same journal

Cyber Military Operations under International Humanitarian Law: Interpreting the Concept of "Attack" and Challenges in Protecting Civilians.

F1000Research·2026
Same journal

Sentiment Analysis of Acceptance TVET Online Courses on the Skill Academy App from Google Play: Leveraging Text Mining with Comparison Machine Learning Model.

F1000Research·2026
Same journal

Emotional intelligence: An important skill to learn now more than ever.

F1000Research·2026
Same journal

East Mediterranean Lineage of <i>Brucella melitensis</i> in Human Isolates and Milk Samples in Oman Using MLVA-14.

F1000Research·2026
Same journal

Application of K-Means Clustering for Job Applicant Analysis in Construction Firms Using R.

F1000Research·2026
Same journal

The influence of self-esteem and emotional intelligence on addiction to social networks in Peruvian university students.

F1000Research·2026
查看所有相关文章

相关实验视频

Updated: Jul 9, 2025

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling
11:19

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Published on: November 17, 2019

16.2K

生物导体工作流用于处理,评估和解释表达式蛋白质组学数据.

Charlotte Hutchings1, Charlotte S Dawson1, Thomas Krueger2

  • 1Cambridge Centre for Proteomics, University of Cambridge, Cambridge, CB2 1QR, UK.

F1000Research
|November 29, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种基于R的工作流程,用于分析定量蛋白质组学数据,涵盖处理,差异表达式分析和对双重质量标记 (TMT) 和无标签定量 (LFQ) 方法的解释.

关键词:
生物导体是一种生物导体.在QFeatures中,我们可以看到QFeatures.从底部向上的蛋白质组学.处理数据的数据处理.不同的表达方式,不同的表达方式.在LimaLima的世界里.质谱测量质谱测量质谱测量质量测量质谱测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量蛋白质组学 蛋白质组学质量控制质量控制质量控制猎枪蛋白质组学 猎枪蛋白质组学

更多相关视频

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis
07:44

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

Published on: June 8, 2020

12.7K
Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples
14:51

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples

Published on: November 13, 2021

5.3K

相关实验视频

Last Updated: Jul 9, 2025

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling
11:19

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Published on: November 17, 2019

16.2K
TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis
07:44

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

Published on: June 8, 2020

12.7K
Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples
14:51

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples

Published on: November 13, 2021

5.3K

科学领域:

  • 蛋白质组学是指蛋白质组学.
  • 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:

  • 表达蛋白质组学在全球范围内评估蛋白质的丰度.
  • 不同表达分析识别了系统干扰后的蛋白质变化.
  • 定量质谱是现代蛋白质组学的关键.

研究的目的:

  • 为定量蛋白质组学数据分析提供全面,逐步的工作流程.
  • 通过使用开源R包引导用户进行处理,分析和解释.
  • 使用HEK293细胞处理数据以实践示例来演示工作流.

主要方法:

  • 使用了来自生物导体的开源R软件包.
  • 开发了一种基于定量质谱的表达蛋白质学的工作流程.
  • 将工作流应用于标记细胞蛋白质的双重质量标签 (TMT) 和分泌蛋白质的无标签量化 (LFQ).
  • 包括数据导入,预处理,质量控制,统计差异表达分析和基因本体学丰富分析.

主要成果:

  • 工作流详细介绍了TMT和LFQ数据集的软件基础设施,数据导入,预处理和质量控制.
  • 通过基因本体学丰富证明了统计差异表达分析和解释.
  • 成功地将工作流应用于来自HEK293细胞的实验数据.

结论:

  • 介绍了表达蛋白质组学数据分析的全面和可访问的工作流.
  • 工作流是蛋白质组学社区的宝贵资源,特别是熟悉R的初学者.
  • 允许用户在蛋白质组学分析中根据数据做出决策.