A novel approach to denoising ion trap tandem mass spectra

1. A novel approach to denoising ion trap tandem mass spectra by Jiarui Ding, Jinhong Shi, Guy Poirier, and Fang-Xiang Wu University of Saskatchewan, Canada Proteome Science 2009 Presenter : Kyowon Joeng

2. Why this paper? • Related to my work (spectral pre-processing) • A good summary on “features” of spectrum • EASY

3. Spectral pre-processing • What they did • Result • Some features of spectrum • Conclusion/criticism/discussion Outline

4. To increase the number of identified peptides • Spectrum clustering (Frank, J proteome Res 08; Tabb, Anal Chem 03) • Precursor charge correction (Klammer, IEEE CSBC 05; Na, Anal Chem 08) • Denoising (Zhang, RCM 08) • Quality assessment (Na, J proteome Res 06; Bern, Bioinformatics 04) • Need to be simple and fast • Need to be generic; otherwise, need to have a killer application Spectral pre-processing

5. Denoising of spectrum • signal peaks: peaks from y or b ions • noisy peaks : other peaks • Intensity normalization • Using interrelation features to assign Score to each peak • New intensity = original intensity * Score • Peak selection • Use morphological reconstruction filter • Select local maxima peaks What they did

6. Intensity normalization : feature selection • Score of a peak p is decided by 5 interrelation features • F1 : # of peaks p’ such that |p-p’| = an a.a. mass (Good diff fraction) • F2 : # of peaks p’ such that p+p’ = precursor mass (Complementary peaks) • F3 : # of peaks p’ such that |p-p’| = H2O or NH3 mass (Neutral loss)

7. Intensity normalization : feature selection • F4 : # of peaks p’ such that |p-p’| =CO or NH mass (Neutral loss) • F5 : # of peaks p’ such that |p-p’| = isotope mass (Isotope) • F1-F5 are normalized to have zero mean and one variance.

8. Intensity normalization : scoring • Score = w0+w1F1+w2F2+w3F3+w4F4+w5F5 • w0 = 5 : Offset for non-negative score • w1 = w2 = 1 : Good diff & complementary • w3 = w4 = 0.2 : Neutral losses • w5 = 0.5 : Isotope • The weights are decided by referring to Sequest scoring function

9. Peak selection • After intensity normalization, it is likely that signal peaks are local maxima. • To select the local maxima, morphological reconstruction filter is adopted

10. Morphological filter • State of the art filter in image processing • Everyone used it at least one time; not so many knows it is the morphological filter. • Flood Fill color tool = morphological filter

11. Morphological filter • Given marker signal (or curve) and mask signal • Dilate mask signal repeatedly until contour of dilated mask signal fits under marker signal. • In each dilation, each point of marker signal takes the maximum value of its neighborhood.

12. Morphological filter

13. Dataset • ISB : ESI ion trap 37,044 spectra • TOV : LCQ DECA XP ion trap 22,576 spectra • Database : ipi.Human protein database • Mascot is used to evaluate denoising

14. Mascot parameters

15. Spectrum is identified if its Mascot ion score is larger than the identity threshold (no target decoy FDR is derived) Number of identified spectra

16. Number of identified spectra

17. A spectrum in ISB dataset is false positive if it is identified in ipi.HUMAN database but it is not from the known 18 proteins. False positive rate

18. Intensity normalization vs. peak selection

19. Features of spectrum • Number of peaks • Total ion current (total intensity of a spectrum) • Good-Diff fraction • Total normalized intensity of peaks with associated isotope peaks • Complements • Water losses • Signal to noise ratio

20. Features of spectrum • The average intensity of the peaks • Total number of peaks having relative intensities greater than x% of TIC • Among them, only features considering m/z differences between peaks turned out to be significant. (Bern, Bioinformatics 04)

21. Conclusion • A denoising algorithm that uses features of spectrum is introduced. • It is simple and improves quality of spectrum • 15-30% more spectra were identified by Mascot after denoising

22. Criticism : method • Intensity normalization is too heuristic. • Among used features, neutral losses are often observed in noisy peaks (e.g., precursor peaks). • Features were manually selected, and no new feature was introduced. • The benefit of morphological filter is not clear.

23. Criticism : result • Standard target-decoy analysis was not shown. • It is about denoising, but the result of denoising is not directly shown. • Proposed scheme may not suitable for other tools. • The running time of their algorithm is not shown; only Mascot search time was shown.

24. Complement peaks associated with their intensities? • For Discussion