Using dynamic programming to create isotopic distribution maps from mass spectra.
BACKGROUND
This article presents a method to identify the isotopic distributions within a mass spectrum using a probabilistic classifier supplemented with dynamic programming. Such a system is needed for a variety of purposes, including generating robust and meaningful features from mass spectra to be used in classification.


RESULTS
The primary result of this article is that the dynamic programming approach significantly improves sensitivity, without harming specificity, of a probabilistic classifier for identifying the isotopic distributions. When annotating isotopic distributions where an expert has performed the initial 'peak-picking' (removal of noise peaks), the dynamic programming approach gives a true positive rate of 96% and a false positive rate of 0.0%, whereas the classifier alone has a true positive rate of only 47% when the false positive rate is 0.0%. When annotating isotopic distributions in machine peak-picked spectra, which may contain many noise peaks, the dynamic programming approach gives a true positive rate of only 22.0%, but it still keeps a low false positive rate of 1.0% and still outperforms the classifier alone. It is important to note that all these rates are when we require exact matches with the distributions in annotated spectra; in our evaluation a distribution is considered 'entirely incorrect' if it is missing even one peak or contains even one extraneous peak. We compared to the THRASH and AID-MS systems using a looser requirement: correctly identifying the distribution that contains the mono-isotopic mass. Under this measure, our dynamic programming approach achieves a true positive rate of 82% and a false positive rate of 1%, which again outperforms the classifier alone. The dynamic programming approach ends up being more conservative than THRASH and AID-MS, yielding both fewer true and false peaks, but the F-score of the dynamic programming approach is significantly better than those of THRASH and AID-MS. All results were obtained with 10-fold cross-validation of 99 sections of mass spectra with a total of 214 hand-annotated isotopic distributions.


BACKGROUND
Programs are available via http://www.cs.wisc.edu/~mcilwain/IDM.
McIlwain
Sean
S
Department of Computer Sciences, University of Wisconsin, Madison, WI, USA. mcilwain@cs.wisc.edu
Page
David
D
Huttlin
Edward L
EL
Sussman
Michael R
MR
D000465:Algorithms; D003198:Computer Simulation; D007553:Isotope Labeling / Q000379:methods; D013058:Mass Spectrometry / Q000379:methods*; D008956:Models, Chemical; D010363:Pattern Recognition, Automated / Q000379:methods*; D010449:Peptide Mapping; D011868:Radioisotopes / Q000737:chemistry*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D011868:Radioisotopes