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相关概念视频

Proteomics01:33

Proteomics

7.2K
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...
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相关实验视频

Updated: Jun 13, 2025

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

Published on: January 10, 2025

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多阶段搜索:一种代工作流程,用于使用蛋白质基因组学对病原体进行公正的分类学分析.

Julian Pipart1, Tanja Holstein1,2,3,4,5, Lennart Martens2,3,4,5

  • 1Data Competence Center MF 2, Robert Koch Institute, Berlin 13353, Germany.

Journal of proteome research
|May 19, 2025
PubMed
概括

MultiStageSearch通过结合基因组和蛋白质组数据来增强病原体诊断. 这种新的方法克服了数据库偏见,在研究和公共卫生中更准确地识别了菌株.

关键词:
反转录-聚合酶链反应 (RT-PCR) 是一种这就是SARS-CoV-2病毒.开放的阅读框架 (ORF)光谱匹配 (PSM) 是一种诺病毒GII

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Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions
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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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An Aquatic Microbial Metaproteomics Workflow: From Cells to Tryptic Peptides Suitable for Tandem Mass Spectrometry-based Analysis

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相关实验视频

Last Updated: Jun 13, 2025

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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.
  • 基因组学和蛋白质组学

背景情况:

  • SARS-CoV-2 流行病突出了对精确病原体诊断的关键需求.
  • 基因组学是标准的,但基于质谱的蛋白质组学提供了补充数据.
  • 现有的基因组和蛋白质组数据库存在分类学偏见和不完整性,阻碍了准确的识别.

研究的目的:

  • 开发一种强大的计算方法,用于对病原体进行准确的分类学分析.
  • 解决当前参考数据库在病原体识别方面的局限性.
  • 改进传染病研究和诊断的菌株级别识别能力.

主要方法:

  • 推出了MultiStageSearch,一个多步骤的数据库搜索策略.
  • 组合的通用蛋白质组数据库用于初始物种推断.
  • 生成专门的,预处理的蛋白质基因组数据库,用于精确识别,减少冗余和偏差.
  • 工作流程独立于现有的菌株级分类学运行.

主要成果:

  • 多阶段搜索在病毒和细菌样本的菌株级分类学推断中表现出卓越的性能.
  • 该方法有效地克服了参考数据库中不完整的搜索空间和偏差.
  • 成功识别出目前分类学中没有代表的菌株.

结论:

  • 多阶段搜索为病原体研究和诊断提供了一种灵活而准确的方法.
  • 这种方法提高了识别和表征微生物菌株的可靠性.
  • 解决传染病监测和疫情应对方面的关键挑战.