Independent component analysis for the extraction of reliable protein signal profiles from MALDI-TOF mass spectra.
Bioinformatics
; 24(1): 63-70, 2008 Jan 01.
Article
en En
| MEDLINE
| ID: mdl-18003646
MOTIVATION: Independent component analysis (ICA) is a signal processing technique that can be utilized to recover independent signals from a set of their linear mixtures. We propose ICA for the analysis of signals obtained from large proteomics investigations such as clinical multi-subject studies based on MALDI-TOF MS profiling. The method is validated on simulated and experimental data for demonstrating its capability of correctly extracting protein profiles from MALDI-TOF mass spectra. RESULTS: The comparison on peak detection with an open-source and two commercial methods shows its superior reliability in reducing the false discovery rate of protein peak masses. Moreover, the integration of ICA and statistical tests for detecting the differences in peak intensities between experimental groups allows to identify protein peaks that could be indicators of a diseased state. This data-driven approach demonstrates to be a promising tool for biomarker-discovery studies based on MALDI-TOF MS technology. AVAILABILITY: The MATLAB implementation of the method described in the article and both simulated and experimental data are freely available at http://www.unich.it/proteomica/bioinf/.
Buscar en Google
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Mapeo Peptídico
/
Reconocimiento de Normas Patrones Automatizadas
/
Espectrometría de Masa por Láser de Matriz Asistida de Ionización Desorción
/
Proteoma
/
Análisis de Secuencia de Proteína
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2008
Tipo del documento:
Article
País de afiliación:
Italia
Pais de publicación:
Reino Unido