Journal of the American Society for Mass Spectrometry

, Volume 20, Issue 8, pp 1435–1440

Post-acquisition ETD spectral processing for increased peptide identifications


  • David M. Good
    • Department of ChemistryUniversity of Wisconsin
  • Craig D. Wenger
    • Department of ChemistryUniversity of Wisconsin
  • Graeme C. McAlister
    • Department of ChemistryUniversity of Wisconsin
  • Dina L. Bai
    • Department of ChemistryUniversity of Virginia
  • Donald F. Hunt
    • Department of ChemistryUniversity of Virginia
    • Department of PathologyUniversity of Virginia
  • Joshua J. Coon
    • Department of ChemistryUniversity of Wisconsin
    • Department of Biomolecular ChemistryUniversity of Wisconsin
Focus: The Orbitrap

DOI: 10.1016/j.jasms.2009.03.006

Cite this article as:
Good, D.M., Wenger, C.D., McAlister, G.C. et al. J Am Soc Mass Spectrom (2009) 20: 1435. doi:10.1016/j.jasms.2009.03.006


Tandem mass spectra (MS/MS) produced using electron transfer dissociation (ETD) differ from those derived from collision-activated dissociation (CAD) in several important ways. Foremost, the predominant fragment ion series are different: c- and z ·-type ions are favored in ETD spectra while b- and y-type ions comprise the bulk of the fragments in CAD spectra. Additionally, ETD spectra possess charge-reduced precursors and unique neutral losses. Most database search algorithms were designed to analyze CAD spectra, and have only recently been adapted to accommodate c- and z ·-type ions; therefore, inclusion of these additional spectral features can hinder identification, leading to lower confidence scores and decreased sensitivity. Because of this, it is important to pre-process spectral data before submission to a database search to remove those features that cause complications. Here, we demonstrate the effects of removing these features on the number of unique peptide identifications at a 1% false discovery rate (FDR) using the open mass spectrometry search algorithm (OMSSA). When analyzing two biologic replicates of a yeast protein extract in three total analyses, the number of unique identifications with a ∼1% FDR increased from 4611 to 5931 upon spectral pre-processing—an increase of ∼28. 6%. We outline the most effective pre-processing methods, and provide free software containing these algorithms.

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© American Society for Mass Spectrometry 2009