A new algorithm using cross-assignment for label-free quantitation with LC-LTQ-FT MS

Victor P. Andreev, Lingyun Li, Lei Cao, Ye Gu, Tomas Rejtar, Shiaw Lin Wu, Barry L. Karger

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


A new algorithm is described for label-free quantitation of relative protein abundances across multiple complex proteomic samples. Q-MEND is based on the denoising and peak picking algorithm, MEND, previously developed in our laboratory. Q-MEND takes advantage of the high resolution and mass accuracy of the hybrid LTQ-FT MS mass spectrometer (or other high-resolution mass spectrometers, such as a Q-TOF MS). The strategy, termed "cross- assignment", is introduced to increase substantially the number of quantitated proteins. In this approach, all MS/MS identifications for the set of analyzed samples are combined into a master ID list, and then each LC-MS run is searched for the features that can be assigned to a specific identification from that master list. The reliability of quantitation is enhanced by quantitating separately all peptide charge states, along with a scoring procedure to filter out less reliable peptide abundance measurements. The effectiveness of Q-MEND is illustrated in the relative quantitative analysis of Escherichia coli samples spiked with known amounts of non-E. coli protein digests. A mean quantitation accuracy of 7% and mean precision of 15% is demonstrated. Q-MEND can perform relative quantitation of a set of LC-MS data sets without manual intervention and can generate files compatible with the Guidelines for Proteomic Data Publication.

Original languageEnglish (US)
Pages (from-to)2186-2194
Number of pages9
JournalJournal of Proteome Research
Issue number6
StatePublished - Jun 2007


  • Cross-assignment
  • Label-free
  • LC-MS
  • LTQ-PT
  • Quantitation

ASJC Scopus subject areas

  • Genetics
  • Biotechnology
  • Biochemistry


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