DIA Proteomics and Machine Learning for the Fast Identification of Bacterial Species in Biological Samples.
Methods Mol Biol
; 2456: 299-317, 2022.
Article
en En
| MEDLINE
| ID: mdl-35612751
ABSTRACT
Identification of bacterial species in biological samples is essential in many applications. However, the standard methods usually use a time-consuming bacterial culture (24-48 h) and sometimes lack in specificity. To overcome these limitations, we developed a new protocol, combining LC-MS/MS analysis in Data Independent Acquisition mode and machine learning algorithms, enabling the accurate identification of the bacterial species contaminating a sample in a few hours without bacterial culture. In this chapter, we describe the three steps of the protocol (spectral libraries generation, training step, identification step) to generate customized peptide signatures and use them for bacterial identification in biological samples through targeted proteomics analyses and prediction models.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Proteómica
/
Espectrometría de Masas en Tándem
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
Methods Mol Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2022
Tipo del documento:
Article
País de afiliación:
Canadá