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
in 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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Proteome
/
Neoplasms
Limits:
Humans
Language:
En
Journal:
J Proteome Res
Journal subject:
BIOQUIMICA
Year:
2021
Document type:
Article
Affiliation country:
China