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TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis
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LFQ-Based Peptide and Protein Intensity Differential Expression Analysis.

Mingze Bai1,2, Jingwen Deng1, Chengxin Dai1,2

  • 1Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

Journal of Proteome Research
|May 23, 2023
PubMed
Summary
This summary is machine-generated.

Different software packages and settings significantly alter results in label-free quantification (LFQ) mass spectrometry proteomics. Choosing the right tools is crucial for accurate protein quantification and identifying significant protein differences.

Keywords:
data analysisimputation and normalization algorithmsmass spectrometry proteomicsprotein expressionprotein fold changes

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Bioinformatics

Background:

  • Label-free quantification (LFQ) mass spectrometry is widely used to identify protein expression differences.
  • Numerous bioinformatics tools and R packages exist for post-quantification analysis, including imputation, normalization, and statistical testing.
  • The choice of software and parameters can impact the final list of significant proteins.

Purpose of the Study:

  • To evaluate the impact of different bioinformatics packages and their settings on the outcomes of LFQ-based proteomics statistical analysis.
  • To compare the performance of various tools using public datasets with known protein fold changes.

Main Methods:

  • Utilized three public proteomics datasets with pre-defined protein fold changes.
  • Assessed multiple R packages and their various parameter settings for imputation, summarization, normalization, and statistical testing.
  • Analyzed the resulting lists of significant proteins for differences across packages and settings.

Main Results:

  • Significant variations in results were observed between different packages and even across different parameter settings within the same package.
  • The choice of package and settings influences the sensitivity and specificity of detecting significant protein differences.
  • Usability, features, and compatibility also varied among the evaluated packages.

Conclusions:

  • The selection of bioinformatics tools and specific parameter settings is critical in LFQ-based proteomics.
  • Researchers must carefully consider the trade-offs in sensitivity and specificity when choosing analysis pipelines.
  • Standardization or clear reporting of chosen methods is essential for reproducibility in proteomics studies.