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Trans‐Proteomic Pipeline supports and improves analysis of electron transfer dissociation data sets
Author(s) -
Deutsch Eric W.,
Shteynberg David,
Lam Henry,
Sun Zhi,
Eng Jimmy K.,
Carapito Christine,
von Haller Priska D.,
Tasman Natalie,
Mendoza Luis,
Farrah Terry,
Aebersold Ruedi
Publication year - 2010
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200900567
Subject(s) - electron transfer dissociation , dissociation (chemistry) , fragmentation (computing) , computer science , ion , pipeline (software) , chemistry , tandem mass spectrometry , mass spectrum , database search engine , search engine , analytical chemistry (journal) , mass spectrometry , data mining , chromatography , information retrieval , operating system , organic chemistry
Electron transfer dissociation (ETD) is an alternative fragmentation technique to CID that has recently become commercially available. ETD has several advantages over CID. It is less prone to fragmenting amino acid side chains, especially those that are modified, thus yielding fragment ion spectra with more uniform peak intensities. Further, precursor ions of longer peptides and higher charge states can be fragmented and identified. However, analysis of ETD spectra has a few important differences that require the optimization of the software packages used for the analysis of CID data or the development of specialized tools. We have adapted the Trans‐Proteomic Pipeline to process ETD data. Specifically, we have added support for fragment ion spectra from high‐charge precursors, compatibility with charge‐state estimation algorithms, provisions for the use of the Lys‐C protease, capabilities for ETD spectrum library building, and updates to the data formats to differentiate CID and ETD spectra. We show the results of processing data sets from several different types of ETD instruments and demonstrate that application of the ETD‐enhanced Trans‐Proteomic Pipeline can increase the number of spectrum identifications at a fixed false discovery rate by as much as 100% over native output from a single sequence search engine.