NDLI: Chromatographic alignment of LC-MS and LC-MS/MS datasets by genetic algorithm feature extraction

Content Provider	Springer Nature Link
Author	Palmblad, Magnus Mills, Davinia J. Bindschedler, Laurence V. Cramer, Rainer
Copyright Year	2007
Abstract	Liquid chromatography-mass spectrometry (LC-MS) datasets can be compared or combined following chromatographic alignment. Here we describe a simple solution to the specific problem of aligning one LC-MS dataset and one LC-MS/MS dataset, acquired on separate instruments from an enzymatic digest of a protein mixture, using feature extraction and a genetic algorithm. First, the LC-MS dataset is searched within a few ppm of the calculated theoretical masses of peptides confidently identified by LC-MS/MS. A piecewise linear function is then fitted to these matched peptides using a genetic algorithm with a fitness function that is insensitive to incorrect matches but sufficiently flexible to adapt to the discrete shifts common when comparing LC datasets. We demonstrate the utility of this method by aligning ion trap LC-MS/MS data with accurate LC-MS data from an FTICR mass spectrometer and show how hybrid datasets can improve peptide and protein identification by combining the speed of the ion trap with the mass accuracy of the FTICR, similar to using a hybrid ion trap-FTICR instrument. We also show that the high resolving power of FTICR can improve precision and linear dynamic range in quantitative proteomics. The alignment software, msalign, is freely available as open source.
Starting Page	1835
Ending Page	1843
Page Count	9
File Format	PDF
ISSN	10440305
Journal	Journal of The American Society for Mass Spectrometry
Volume Number	18
Issue Number	10
e-ISSN	18791123
Language	English
Publisher	Springer-Verlag
Publisher Date	2007-10-01
Publisher Institution	The American Society for Mass Spectrometry
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Analytical Chemistry Biotechnology Organic Chemistry Proteomics Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Spectroscopy Structural Biology

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