Abstract
Background: Mass spectrometry protein profiling is a promising tool for biomarker discovery in clinical proteomics. However, the development of a reliable approach for the separation of protein signals from noise is required. In this paper, LIMPIC, a computational method for the detection of protein peaks from linear-mode MALDI-TOF data is proposed. LIMPIC is based on novel techniques for background noise reduction and baseline removal. Peak detection is performed considering the presence of a non-homogeneous noise level in the mass spectrum. A comparison of the peaks collected from multiple spectra is used to classify them on the basis of a detection rate parameter, and hence to separate the protein signals from other disturbances. Results: LIMPIC preprocessing proves to be superior than other classical preprocessing techniques, allowing for a reliable decomposition of the background noise and the baseline drift from the MALDI-TOF mass spectra. It provides lower coefficient of variation associated with the peak intensity, improving the reliability of the information that can be extracted from single spectra. Our results show that LIMPIC peak-picking is effective even in low protein concentration regimes. The analytical comparison with commercial and freeware peak-picking algorithms demonstrates its superior performances in terms of sensitivity and specificity, both on in-vitro purified protein samples and human plasma samples. Conclusion: The quantitative information on the peak intensity extracted with LIMPIC could be used for the recognition of significant protein profiles by means of advanced statistic tools: LIMPIC might be valuable in the perspective of biomarker discovery.
| Original language | English |
|---|---|
| Article number | 101 |
| Journal | BMC Bioinformatics |
| Volume | 8 |
| DOIs | |
| Publication status | Published - Mar 26 2007 |
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ASJC Scopus subject areas
- Medicine(all)
- Structural Biology
- Applied Mathematics
Cite this
LIMPIC : A computational method for the separation of protein MALDI-TOF-MS signals from noise. / Mantini, Dante; Petrucci, Francesca; Pieragostino, Damiana; Del Boccio, Piero; Di Nicola, Marta; Di Ilio, Carmine; Federici, Giorgio; Sacchetta, Paolo; Comani, Silvia; Urbani, Andrea.
In: BMC Bioinformatics, Vol. 8, 101, 26.03.2007.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - LIMPIC
T2 - A computational method for the separation of protein MALDI-TOF-MS signals from noise
AU - Mantini, Dante
AU - Petrucci, Francesca
AU - Pieragostino, Damiana
AU - Del Boccio, Piero
AU - Di Nicola, Marta
AU - Di Ilio, Carmine
AU - Federici, Giorgio
AU - Sacchetta, Paolo
AU - Comani, Silvia
AU - Urbani, Andrea
PY - 2007/3/26
Y1 - 2007/3/26
N2 - Background: Mass spectrometry protein profiling is a promising tool for biomarker discovery in clinical proteomics. However, the development of a reliable approach for the separation of protein signals from noise is required. In this paper, LIMPIC, a computational method for the detection of protein peaks from linear-mode MALDI-TOF data is proposed. LIMPIC is based on novel techniques for background noise reduction and baseline removal. Peak detection is performed considering the presence of a non-homogeneous noise level in the mass spectrum. A comparison of the peaks collected from multiple spectra is used to classify them on the basis of a detection rate parameter, and hence to separate the protein signals from other disturbances. Results: LIMPIC preprocessing proves to be superior than other classical preprocessing techniques, allowing for a reliable decomposition of the background noise and the baseline drift from the MALDI-TOF mass spectra. It provides lower coefficient of variation associated with the peak intensity, improving the reliability of the information that can be extracted from single spectra. Our results show that LIMPIC peak-picking is effective even in low protein concentration regimes. The analytical comparison with commercial and freeware peak-picking algorithms demonstrates its superior performances in terms of sensitivity and specificity, both on in-vitro purified protein samples and human plasma samples. Conclusion: The quantitative information on the peak intensity extracted with LIMPIC could be used for the recognition of significant protein profiles by means of advanced statistic tools: LIMPIC might be valuable in the perspective of biomarker discovery.
AB - Background: Mass spectrometry protein profiling is a promising tool for biomarker discovery in clinical proteomics. However, the development of a reliable approach for the separation of protein signals from noise is required. In this paper, LIMPIC, a computational method for the detection of protein peaks from linear-mode MALDI-TOF data is proposed. LIMPIC is based on novel techniques for background noise reduction and baseline removal. Peak detection is performed considering the presence of a non-homogeneous noise level in the mass spectrum. A comparison of the peaks collected from multiple spectra is used to classify them on the basis of a detection rate parameter, and hence to separate the protein signals from other disturbances. Results: LIMPIC preprocessing proves to be superior than other classical preprocessing techniques, allowing for a reliable decomposition of the background noise and the baseline drift from the MALDI-TOF mass spectra. It provides lower coefficient of variation associated with the peak intensity, improving the reliability of the information that can be extracted from single spectra. Our results show that LIMPIC peak-picking is effective even in low protein concentration regimes. The analytical comparison with commercial and freeware peak-picking algorithms demonstrates its superior performances in terms of sensitivity and specificity, both on in-vitro purified protein samples and human plasma samples. Conclusion: The quantitative information on the peak intensity extracted with LIMPIC could be used for the recognition of significant protein profiles by means of advanced statistic tools: LIMPIC might be valuable in the perspective of biomarker discovery.
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UR - http://www.scopus.com/inward/citedby.url?scp=34147114217&partnerID=8YFLogxK
U2 - 10.1186/1471-2105-8-101
DO - 10.1186/1471-2105-8-101
M3 - Article
C2 - 17386085
AN - SCOPUS:34147114217
VL - 8
JO - BMC Bioinformatics
JF - BMC Bioinformatics
SN - 1471-2105
M1 - 101
ER -