1-14-2011 Spectrum denoising

A novel approach to denoising ion trap tandem mass
spectra" by Jiarui et al, 2009

spectral pre-processing

goal for pre-processing

1. remove the noise

2. decrease the number of non-identified spectra

3. increase the number of identified peptides

procedure

1. denoising of spectrum

-signal peaks: peaks from y or b

-noisy peaks: other peaks

2. intensity normalization

using 5 interrelation features

1. F1: # of peaks p’ such that p-p’ = an a.a. mass

2. F2: # of peaks p’ such that p+p’ = precursor mass

3. F3: # of peaks p’ such that p-p’ = H2O for NH3

4. F4: # of peaks p’ such that p-p’ = CO or NH

5. F5: # of peaks p’ such that p-p’ = isotope mass

6. score: w0 + w1F1 + w2F2 + w3F3 + w4F4 + w5F5

7. if score is minus they exclude the peak(noise)

peak selection

-after intensity normalization it is likely that signal peaks are local maxima

to select the local maxima, morphological reconstruction filter is adopted

dataset

-ISB: ESI ion trap 37044 spectra

-TOV: LCQ DECA XP ion trap 22576 spectra

-database: ipi.Human protein database

-Mascot is used to evaluate denoising

Number of identified spectra

-spectrum is identified if its Mascot ion score is larger than the identity threshold

results

-Denoised spectrum increased the # of identification of Mascot search

Features of spectrum that other people use in preprocessing

-Number of peaks

-total ion current

-Good Diff fraction

-Total normalized intensity of peaks with associated isotope peaks

-complements

-water losses

-signal to noise ratio

Conclusion

-intensity normalization is too heuristic

-among used features, neutral losses are often observed in noisy peaks

-features were manually selected, and no new feature was introduced

the benefit of morphological filter is not clear

-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

discussion

-They increased the # of identifications of MASCOT

-Spectrum preprocessing might be good on De Novo but no significant improvements on Database search
-Preprocessing is highly dependent on scoring function itself

Speaker: Kyowon
Scribe: Sunghee
Slides: here