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A Peptide-Level Fully Annotated Data Set for Quantitative Evaluation of Precursor-Aware Mass Spectrometry Data

Jessica Henning1, Annika Tostengard1, Rob Smith1,2

  • 1Department of Computer Science , University of Montana , Missoula , Montana 59812 , United States.

Journal of Proteome Research
|November 6, 2018
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Summary
This summary is machine-generated.

This study introduces a novel, manually curated dataset for mass spectrometry (MS) data analysis. This ground truth data enables rigorous evaluation of computational methods for proteomics, improving quantitative accuracy.

Keywords:
LC-MS ground truthMass spectrometryUPS2XICXIC feature detectionbenchmark datafeature detectionground truthproteomicsquantitative evaluation

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Computational Biology

Background:

  • Label-free quantitative mass spectrometry workflows are complex and sensitive to experimental and computational choices.
  • Evaluating post-acquisition data processing methods is challenging due to a lack of ground truth data.

Purpose of the Study:

  • To create a novel, high-quality ground truth dataset for mass spectrometry data analysis at the precursor (MS1) signal level.
  • To facilitate the evaluation of computational algorithms for proteomics data processing.

Main Methods:

  • Manual curation of isolated peptide signals from the UPS2 standard over 1000 hours.
  • Generation of a dataset containing over 62 million data points, organized into extracted ion chromatograms and isotopic envelopes.

Main Results:

  • A comprehensive ground truth dataset for MS1 signal analysis was successfully generated.
  • The dataset includes 1,294,008 grouped signals, 57,518 extracted ion chromatograms, and 14,111 isotopic envelopes.

Conclusions:

  • This curated dataset provides a vital resource for assessing the accuracy and biases of mass spectrometry data processing algorithms.
  • Enables objective evaluation of precursor mapping and signal extraction techniques in proteomics.