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相关概念视频

Proteomics01:33

Proteomics

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 proteomics...

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相关实验视频

Updated: Jun 13, 2026

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

加强蛋白质学质量控制:来自可视化工具QCeltis的见解

Manasa Vegesna1,2, Niveda Sundararaman1,2, Ajay Bharadwaj1,2

  • 1Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.

Journal of proteome research
|February 24, 2025
PubMed
概括

QCeltis是一个新的Python包,用于大规模蛋白质组学中的自动化质量控制. 它有助于识别技术偏差并验证质谱数据的一致性,提高研究可靠性.

关键词:
时间 时间 时间 时间质谱测量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蛋白质组学 蛋白质组学质量控制质量控制质量控制视觉化工具是一种可视化工具.

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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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Last Updated: Jun 13, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Published on: November 15, 2017

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06:45

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Published on: June 15, 2018

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科学领域:

  • 蛋白质组学是指蛋白质组学.
  • 分析化学 分析化学
  • 生物信息学是一种生物信息学.

背景情况:

  • 大规模的基于质谱的蛋白质组学实验是复杂的,容易产生分析变化.
  • 严格的质量控制 (QC) 在整个工作流程中至关重要,从样本准备到生物信息学.
  • 现有的质量控制方法往往侧重于异常值检测和仪器性能监测.

研究的目的:

  • 介绍QCeltis,一个用于蛋白质组学自动化QC分析的Python包.
  • 为了方便识别技术偏差,并验证大规模蛋白质组学数据的一致性.
  • 帮助在数据独立采集 (DIA) 蛋白质组学中区分质量控制问题和批量效应.

主要方法:

  • 开发用于自动化QC分析的QCeltis Python包.
  • 应用QCeltis在各种蛋白质组学工作流程中,包括样本准备和质谱学.
  • 使用Windows和Linux环境的命令行界面.
  • 案例研究涉及耗尽的血,全血与血,以及干燥的血液斑点样本.

主要成果:

  • QCeltis有效地在蛋白质组学工作流程中自动化QC分析.
  • 该套件有助于识别样本准备和采集问题.
  • 在DIA蛋白质组学数据中,QCeltis协助区分QC问题和批量效应.
  • 在各种样本类型中证明了实用性.

结论:

  • 在大规模的蛋白质组学项目中,QCeltis提高了数据可靠性.
  • 该包允许更细微的下游数据分析和解释.
  • QCeltis是确保蛋白质组学研究的一致性和识别偏差的宝贵工具.