Testing the significance of microorganism identification by mass spectrometry and proteome database search.
We derive and validate a simple statistical model that predicts the distribution of false matches between peaks in matrix-assisted laser desorption/ionization mass spectrometry data and proteins in proteome databases. The model allows us to calculate the significance of previously reported microorganism identification results. In particular, for deltam = +/-1.5 Da, we find that the computed significance levels are sufficient to demonstrate the ability to identify microorganisms, provided the number of candidate microorganisms is limited to roughly three Escherichia coli-like or roughly 10 Bacillus subtilis-like microorganisms (in the sense of having roughly the same number of proteins per unit-mass interval). We conclude that, given the cluttered and incomplete nature of the data, it is likely that neither simple ranking nor simple hypothesis testing will be sufficient for truly robust microorganism identification over a large number of candidate microorganisms.
Pineda
F J
FJ
Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland 20723-6099, USA.
Lin
J S
JS
Fenselau
C
C
Demirev
P A
PA
D001412:Bacillus subtilis / Q000737:chemistry*; D015373:Bacterial Typing Techniques; D003628:Database Management Systems; D004926:Escherichia coli / Q000737:chemistry / Q000145:classification*; D020543:Proteome; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
D020543:Proteome