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Related Concept Videos

Proteomics01:33

Proteomics

7.7K
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...
7.7K

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Updated: Aug 20, 2025

Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions
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Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions

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Label-free proteome quantification and evaluation.

Jianbo Fu1, Qingxia Yang1,2, Yongchao Luo1

  • 1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.

Briefings in Bioinformatics
|November 20, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces EVALFQ, an R package for evaluating over 3000 label-free quantification (LFQ) chains in proteomics. It helps researchers identify optimal quantification strategies for their specific data.

Keywords:
R packagecomprehensive evaluationlabel-free quantificationproteomicswell-performing chains

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Label-free quantification (LFQ) is crucial in proteomics for its wide coverage and reproducibility.
  • LFQ requires complex data processing chains involving transformation, pretreatment, and imputation.
  • Selecting the best LFQ chain is challenging due to data-specific performance and numerous combination possibilities.

Purpose of the Study:

  • To develop and introduce the EVALFQ R package for comprehensive performance evaluation of LFQ quantification chains.
  • To provide a standardized method for assessing the accuracy and reliability of different LFQ data processing pipelines.
  • To facilitate the discovery of optimal LFQ chains tailored to specific proteomic datasets.

Main Methods:

  • Developed the EVALFQ R package to automate the evaluation of over 3000 distinct LFQ chains.
  • Implemented multiple performance criteria for automated assessment.
  • Incorporated spiking protein experiments to evaluate quantification accuracy.
  • Utilized comprehensive assessment strategies to identify superior LFQ chains.

Main Results:

  • EVALFQ successfully evaluates >3000 LFQ chains, offering a systematic approach to pipeline selection.
  • The package assesses quantification accuracy using spiking proteins, providing empirical validation.
  • Identified well-performing LFQ chains through comprehensive, multi-perspective analysis.
  • Demonstrated the package's capability to scan and compare a vast number of quantification strategies.

Conclusions:

  • EVALFQ offers a superior, multi-faceted approach to assessing proteomic quantification chains.
  • The package is expected to significantly benefit the field of proteomic quantification by enabling data-driven pipeline optimization.
  • Provides a valuable resource for researchers seeking to improve the accuracy and reproducibility of their LFQ experiments.