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

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

6.8K
Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
6.8K
Proteomics01:33

Proteomics

7.9K
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...
7.9K
MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

5.3K
Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.
Matrix-assisted laser desorption ionization (MALDI) is a commonly...
5.3K

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

Taxonomic-Level Protein Quantification in Metaproteomics Using a Biomass-Constrained Expectation-Maximization Approach.

Journal of the American Society for Mass Spectrometry·2026
Same author

From Identification to Insight: Making Full Use of the Diagnostic Potential of MS/MS Proteotyping in Clinical Microbiology Using Efficient Bioinformatics.

Journal of proteome research·2025
Same author

Interaction at pre-bonding distances and bond formation for open p-shell atoms with different orientations of their angular momenta.

The Journal of chemical physics·2025
Same author

FastSpel: A Method for Fast Spectral Library Generation.

Journal of proteome research·2025
Same author

Biological Function Assignment Across Taxonomic Levels in Mass-Spectrometry-Based Metaproteomics via a Modified Expectation Maximization Algorithm.

bioRxiv : the preprint server for biology·2025
Same author

MARLOWE: Taxonomic Characterization of Unknown Samples for Forensics Using <i>De Novo</i> Peptide Identification.

bioRxiv : the preprint server for biology·2025

相关实验视频

Updated: Sep 15, 2025

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
07:01

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

172

通过修改的预期最大化算法,在基于质谱的元蛋白学中跨分类学水平的生物功能赋值.

Gelio Alves1, Aleksey Y Ogurtsov1, Yi-Kuo Yu1

  • 1Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States.

Journal of proteome research
|July 18, 2025
PubMed
概括
此摘要是机器生成的。

使用预期最大化 (EM) 算法的新MiCId工作流改善了微生物识别和生物功能赋值在metaproteomics. 与现有方法相比,这种增强的工具提供了更高的准确性和更好的错误发现控制.

关键词:
在EM算法中,EM算法生物功能 生物功能基于质谱的元蛋白质组学.形态保护学与社会主义.无监督的机器学习

更多相关视频

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease
09:52

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

Published on: January 10, 2025

733
Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

12.1K

相关实验视频

Last Updated: Sep 15, 2025

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
07:01

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

172
A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease
09:52

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

Published on: January 10, 2025

733
Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

12.1K

科学领域:

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

背景情况:

  • 基于质谱的元蛋白质组学在准确识别微生物功能方面面临挑战,原因是共享的可靠识别的质问题.
  • 当前的工具经常使用最小共同祖先 (LCA) 算法,导致不完整的分类学和功能赋值.

研究的目的:

  • 加强MiCId工作流程,以改善微生物识别和生物功能的量化.
  • 解决现有的元蛋白组学工具在处理共享和分类谱系方面的局限性.

主要方法:

  • 在MiCId工作流中实现预期最大化 (EM) 算法.
  • 整合生物功能数据库以进行增强分析.
  • 使用合成数据集的验证和对人类微生物组数据集的重新分析.

主要成果:

  • 与Unipept和MetaGOmics相比,增强的MiCId工作流显示出对错误发现的更好控制,以及微生物识别和生物质估计的更高准确性.
  • 更新后的MiCId显示,对生物功能识别的准确性和错误发现控制能力有所提高,与Unipept.
  • 在整个分类谱系中实现了函数丰度的可靠计算.

结论:

  • 增强的MiCId工作流提供了一个更准确,更可靠的方法进行元蛋白质组分析,特别是复杂的微生物群落.
  • 这种方法克服了基于LCA的方法的局限性,使得在整个分类学范围内获得全面的功能洞察力.
  • 这些发现与之前的分析一致,验证了增强的MiCId工作流在真实世界微生物组研究中的实用性.