Computational Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage and Applications to 15 Tumors.
J Proteome Res
; 20(12): 5392-5401, 2021 12 03.
Article
en En
| MEDLINE
| ID: mdl-34748352
ABSTRACT
Efficient peptide and protein identifications from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on a project-specific spectral library with a suitable size. Here, we describe subLib, a computational strategy for optimizing the spectral library for a specific DIA data set based on a comprehensive spectral library, requiring the preliminary analysis of the DIA data set. Compared with the pan-human library strategy, subLib achieved a 41.2% increase in peptide precursor identifications and a 35.6% increase in protein group identifications in a test data set of six colorectal tumor samples. We also applied this strategy to 389 carcinoma samples from 15 tumor data sets up to a 39.2% increase in peptide precursor identifications and a 19.0% increase in protein group identifications were observed. Our strategy for spectral library size optimization thus successfully proved to deepen the proteome coverages of DIA-MS data.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Proteoma
/
Neoplasias
Límite:
Humans
Idioma:
En
Revista:
J Proteome Res
Asunto de la revista:
BIOQUIMICA
Año:
2021
Tipo del documento:
Article
País de afiliación:
China