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相关概念视频

Proteomics01:33

Proteomics

9.3K
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...
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相关实验视频

Updated: Jan 12, 2026

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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蛋白质水平批量效应校正增强了基于MS的蛋白质组学的稳定性.

Qiaochu Chen1, Zehui Cao1, Yaqing Liu1

  • 1State Key Laboratory of Genetics and Development of Complex Phenotypes, Human Phenome Institute and School of Life Sciences, Fudan University, Shanghai, China.

Nature communications
|November 4, 2025
PubMed
概括
此摘要是机器生成的。

在蛋白质组学中,批量效应校正至关重要. 蛋白质水平校正是最强大的,增强大规模研究的数据整合,特别是与MaxLFQ-Ratio等量化方法相结合时.

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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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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.
  • 数据科学数据科学数据科学

背景情况:

  • 基于质谱 (MS) 的蛋白质组学易受批量效应的影响,即影响蛋白质量化的技术变化.
  • 在蛋白质组学工作流程中应用批量效应校正的最佳阶段尚未确立.

研究的目的:

  • 在基于MS的蛋白质组学中,对不同数据级别 (前体,,蛋白质) 的批量效应校正策略进行比较.
  • 评估量化方法和校正算法的对多批数据集成的影响.

主要方法:

  • 利用现实世界 (四重奏参考材料) 和模拟的多批次蛋白质组学数据.
  • 在三个量化方法 (MaxLFQ,TopPep3,iBAQ) 中比较了七个批量效应校正算法.
  • 在平衡和混杂的实验设计中评估了校正性能.

主要成果:

  • 蛋白质水平批量效应校正在测试场景中表现出最高的稳定性.
  • 量化方法与批量效应校正算法的性能有显著的相互作用.
  • 在大规模临床试验数据中,MaxLFQ-Ratio组合显示出优异的预测性能.

结论:

  • 建议在蛋白质层面进行批量效应校正,以实现可靠的多批量蛋白质组学数据集成.
  • 选择量化方法会影响批量效应校正策略的有效性.
  • 优化的批量效应校正提高了大规模蛋白质组学队列在临床研究中的实用性.