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Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods.

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Data in Brief
|February 11, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a controlled proteomic dataset with known variant proteins, ideal for evaluating bioinformatics software. The data helps assess label-free quantification methods for sensitivity and accuracy in large-scale proteomic studies.

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Analytical Chemistry

Background:

  • Label-free quantitative proteomics is crucial for large-scale studies.
  • Objective evaluation of bioinformatics pipelines is needed for accurate variant protein detection.
  • Existing datasets may lack controlled
  • ground truth
  • for variant proteins.

Purpose of the Study:

  • To present a controlled, spiked proteomic dataset with known variant protein
  • ground truth
  • for benchmarking.
  • To facilitate the objective evaluation of bioinformatics pipelines for label-free quantitative analysis.
  • To aid in tuning software parameters and developing new algorithms for variant protein detection.

Main Methods:

  • Liquid Chromatography-Mass Spectrometry (LC-MS) analysis of yeast lysate spiked with varying amounts of UPS1 recombinant proteins.
  • Generation of raw data files available via ProteomeXchange (identifier PXD001819).
  • Processing of raw data using multiple bioinformatics tools (MaxQuant, Skyline, MFPaQ, IRMa-hEIDI, Scaffold) to create processed datasets.

Main Results:

  • A comprehensive dataset enabling objective assessment of bioinformatics pipeline sensitivity and false discovery rates.
  • Processed data exemplifies software benchmarking for variant protein detection across different expression levels.
  • Demonstration of how spike levels inform evaluation of quantitative proteomics tools.

Conclusions:

  • The described dataset serves as a valuable resource for validating and improving bioinformatics tools in quantitative proteomics.
  • This data facilitates the development of more sensitive and accurate methods for detecting variant proteins.
  • Objective benchmarking is essential for advancing large-scale proteomic data analysis.