LIMPIC: A computational method for the separation of protein MALDI-TOF-MS signals from noise

Dante Mantini, Francesca Petrucci, Damiana Pieragostino, Piero Del Boccio, Marta Di Nicola, Carmine Di Ilio, Giorgio Federici, Paolo Sacchetta, Silvia Comani, Andrea Urbani

Research output: Contribution to journalArticle

82 Citations (Scopus)

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 languageEnglish
Article number101
JournalBMC Bioinformatics
Volume8
DOIs
Publication statusPublished - Mar 26 2007

Fingerprint

Matrix-Assisted Laser Desorption-Ionization Mass Spectrometry
Computational methods
Computational Methods
Proteins
Protein
Noise
Biomarkers
Preprocessing
Baseline
Plasma (human)
Coefficient of variation
Proteomics
Noise Reduction
Mass Spectrometry
Profiling
Noise abatement
Specificity
Mass spectrometry
Statistic
Plasma

ASJC Scopus subject areas

  • Medicine(all)
  • Structural Biology
  • Applied Mathematics

Cite this

Mantini, D., Petrucci, F., Pieragostino, D., Del Boccio, P., Di Nicola, M., Di Ilio, C., ... Urbani, A. (2007). LIMPIC: A computational method for the separation of protein MALDI-TOF-MS signals from noise. BMC Bioinformatics, 8, [101]. https://doi.org/10.1186/1471-2105-8-101

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 journalArticle

Mantini, D, Petrucci, F, Pieragostino, D, Del Boccio, P, Di Nicola, M, Di Ilio, C, Federici, G, Sacchetta, P, Comani, S & Urbani, A 2007, 'LIMPIC: A computational method for the separation of protein MALDI-TOF-MS signals from noise', BMC Bioinformatics, vol. 8, 101. https://doi.org/10.1186/1471-2105-8-101
Mantini D, Petrucci F, Pieragostino D, Del Boccio P, Di Nicola M, Di Ilio C et al. LIMPIC: A computational method for the separation of protein MALDI-TOF-MS signals from noise. BMC Bioinformatics. 2007 Mar 26;8. 101. https://doi.org/10.1186/1471-2105-8-101
Mantini, Dante ; Petrucci, Francesca ; Pieragostino, Damiana ; Del Boccio, Piero ; Di Nicola, Marta ; Di Ilio, Carmine ; Federici, Giorgio ; Sacchetta, Paolo ; Comani, Silvia ; Urbani, Andrea. / LIMPIC : A computational method for the separation of protein MALDI-TOF-MS signals from noise. In: BMC Bioinformatics. 2007 ; Vol. 8.
@article{eb2db8e7b1814f38a4e957a9acc8a526,
title = "LIMPIC: A computational method for the separation of protein MALDI-TOF-MS signals from noise",
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.",
author = "Dante Mantini and Francesca Petrucci and Damiana Pieragostino and {Del Boccio}, Piero and {Di Nicola}, Marta and {Di Ilio}, Carmine and Giorgio Federici and Paolo Sacchetta and Silvia Comani and Andrea Urbani",
year = "2007",
month = "3",
day = "26",
doi = "10.1186/1471-2105-8-101",
language = "English",
volume = "8",
journal = "BMC Bioinformatics",
issn = "1471-2105",
publisher = "BioMed Central Ltd.",

}

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.

UR - http://www.scopus.com/inward/record.url?scp=34147114217&partnerID=8YFLogxK

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 -