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Related Concept Videos

Peptide Identification Using Tandem Mass Spectrometry01:33

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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Benchmarking quantitative label-free LC-MS data processing workflows using a complex spiked proteomic standard

Claire Ramus1, Agnès Hovasse2, Marlène Marcellin3

  • 1ProFi, Proteomic French Infrastructure, France; CEA, DSV, iRTSV, Laboratoire de Biologie à Grande Echelle, Grenoble F-38054, France; INSERM U1038, Grenoble F-38054, France; Université Grenoble, F-38054, France.

Journal of Proteomics
|November 21, 2015
PubMed
Summary
This summary is machine-generated.

Benchmarking label-free quantitative proteomics workflows is crucial for accurate differential protein expression analysis. This study evaluates various computational pipelines using a spiked proteomic standard to assess sensitivity and false discovery rates.

Keywords:
Computational proteomicsLabel-free quantificationMS signal analysisNanoLC–MS/MSProteomic standardSpectral counting

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Bioinformatics

Background:

  • Label-free quantitative proteomics using nanoLC-MS/MS is advancing rapidly.
  • High-resolution instruments facilitate label-free quantification (LFQ) methods like spectral counting and MS signal analysis for differential protein expression.
  • Computational challenges persist in processing LFQ data.

Purpose of the Study:

  • To benchmark various label-free quantitative proteomics workflows.
  • To assess the sensitivity and false discovery rate of different software packages.
  • To provide a standardized dataset for evaluating LFQ computational pipelines.

Main Methods:

  • Utilized a proteomic standard (Sigma UPS1) spiked into yeast lysate at varying concentrations.
  • Benchmarked multiple label-free quantitative workflows using different software packages.
  • Assessed performance based on true and false positive identification of proteins.

Main Results:

  • Evaluated the sensitivity and false discovery rate of several label-free bioinformatics tools (MaxQuant, Skyline, MFPaQ, IRMa-hEIDI, Scaffold).
  • Demonstrated the utility of a controlled spiked sample for assessing LFQ workflow performance.
  • Identified the need for objective evaluation of bioinformatic pipelines for LFQ.

Conclusions:

  • Bioinformatic pipelines for label-free quantitative proteomics require rigorous evaluation for sensitivity and false discovery rate.
  • Controlled spiked samples provide a "ground truth" for statistically evaluating data processing workflows.
  • The presented dataset (PXD001819) aids in tuning software parameters, developing new algorithms, and evaluating statistical methods for LFQ.