An adaptive alignment algorithm for quality-controlled label-free LC-MS

Mol Cell Proteomics. 2013 May;12(5):1407-20. doi: 10.1074/mcp.O112.021907. Epub 2013 Jan 9.


Label-free quantification using precursor-based intensities is a versatile workflow for large-scale proteomics studies. The method however requires extensive computational analysis and is therefore in need of robust quality control during the data mining stage. We present a new label-free data analysis workflow integrated into a multiuser software platform. A novel adaptive alignment algorithm has been developed to minimize the possible systematic bias introduced into the analysis. Parameters are estimated on the fly from the data at hand, producing a user-friendly analysis suite. Quality metrics are output in every step of the analysis as well as actively incorporated into the parameter estimation. We furthermore show the improvement of this system by comprehensive comparison to classical label-free analysis methodology as well as current state-of-the-art software.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Chromatography, Liquid / methods
  • Chromatography, Liquid / standards
  • Phytophthora infestans / physiology
  • Plant Diseases / parasitology
  • Plant Proteins / chemistry
  • Plant Proteins / isolation & purification
  • Plant Proteins / metabolism
  • Proteome / chemistry
  • Proteome / isolation & purification
  • Proteome / metabolism
  • Proteomics
  • Quality Control
  • Software*
  • Solanum tuberosum / metabolism
  • Solanum tuberosum / parasitology
  • Tandem Mass Spectrometry / methods
  • Tandem Mass Spectrometry / standards*


  • Plant Proteins
  • Proteome