Availability of MudPIT data for classification of biological samples

Journal of Clinical Bioinformatics

Pierluigi Mauri²  Giancarlo Mauri¹  Valeria Bellettato²  Francesca Brambilla²  Italo Zoppis¹  Dario Di Silvestre² 

[1] Department of Informatics, Systems and Communication, Viale Sarca 336, University of Milano-Bicocca, Milan, Italy;, Institute for Biomedical Technologies (ITB-CNR), via F.lli Cervi 93, Segrate (Milan), Italy

关键词: Label-free quantification;    Clinical proteomics;    SVM;    MudPIT;    Sample classification;   

Others  :  804215
DOI  :  10.1186/2043-9113-3-1

 received in 2012-10-02, accepted in 2013-01-07,  发布年份 2013

【 摘 要 】

Background

Mass spectrometry is an important analytical tool for clinical proteomics. Primarily employed for biomarker discovery, it is increasingly used for developing methods which may help to provide unambiguous diagnosis of biological samples. In this context, we investigated the classification of phenotypes by applying support vector machine (SVM) on experimental data obtained by MudPIT approach. In particular, we compared the performance capabilities of SVM by using two independent collection of complex samples and different data-types, such as mass spectra (m/z), peptides and proteins.

Results

Globally, protein and peptide data allowed a better discriminant informative content than experimental mass spectra (overall accuracy higher than 87% in both collection 1 and 2). These results indicate that sequencing of peptides and proteins reduces the experimental noise affecting the raw mass spectra, and allows the extraction of more informative features available for the effective classification of samples. In addition, proteins and peptides features selected by SVM matched for 80% with the differentially expressed proteins identified by the MAProMa software.

Conclusions

These findings confirm the availability of the most label-free quantitative methods based on processing of spectral count and SEQUEST-based SCORE values. On the other hand, it stresses the usefulness of MudPIT data for a correct grouping of sample phenotypes, by applying both supervised and unsupervised learning algorithms. This capacity permit the evaluation of actual samples and it is a good starting point to translate proteomic methodology to clinical application.

【 授权许可】

   
2013 Di Silvestre et al.; licensee BioMed Central Ltd.

【 图 表 】

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