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相关概念视频

Proteomics01:33

Proteomics

7.3K
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...
7.3K

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相关实验视频

Updated: Jun 22, 2025

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

547

生物信息工作流程用于metaproteomics.

Tanja Holstein1,2,3, Thilo Muth4,5

  • 1Section eScience (S.3), Federal Institute for Materials Research and Testing, Berlin, Germany.

Methods in molecular biology (Clifton, N.J.)
|June 28, 2024
PubMed
概括
此摘要是机器生成的。

超蛋白组学分析微生物社区蛋白质,以了解微生物组的功能和组成. 本研究回顾了精确的元蛋白质组分析的基本生物信息工具和协议.

关键词:
生物信息工作流程功能分配的功能分配.甲型蛋白质组学是什么意思?微生物组就是微生物组.蛋白质量化量化的蛋白质.蛋白质组学是指蛋白质组学.分类学赋值的分配.

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An Aquatic Microbial Metaproteomics Workflow: From Cells to Tryptic Peptides Suitable for Tandem Mass Spectrometry-based Analysis
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Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples
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Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples

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An Aquatic Microbial Metaproteomics Workflow: From Cells to Tryptic Peptides Suitable for Tandem Mass Spectrometry-based Analysis
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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.
  • 系统生物学 系统生物学

背景情况:

  • 人们越来越认识到微生物群对生态和人类健康的影响.
  • 研究微生物群落的组成和功能需要专门的技术.
  • 甲型蛋白质组学为微生物分类学,功能和数量提供了洞察力.

研究的目的:

  • 为了提供一个全面的概述当前生物信息解决方案的metaproteomics.
  • 详细介绍一下元体的后处理步骤.
  • 要突出十个特定的生物信息软件解决方案.

主要方法:

  • 审查现有的生物信息软件和协议的metaproteomics.
  • 解释蛋白质基因数据库搜索和后处理.
  • 专注于用于识别,量化和分类/功能分配的软件.

主要成果:

  • 确定10个重要的生物信息软件解决方案用于metaproteomics.
  • 详细解释基于数据库的识别和量化方法.
  • 覆盖了分类学和功能赋值策略.

结论:

  • 由于社区的复杂性,专门的生物信息学方法对于元蛋白质组分析至关重要.
  • 审查的软件和协议有助于全面的微生物组调查.
  • 这项工作是当前超蛋白质组生物信息学方法的指南.