Spectral Clustering Improves Label-Free Quantification of Low-Abundant Proteins.
J Proteome Res
; 18(4): 1477-1485, 2019 04 05.
Article
em En
| MEDLINE
| ID: mdl-30859831
ABSTRACT
Label-free quantification has become a common-practice in many mass spectrometry-based proteomics experiments. In recent years, we and others have shown that spectral clustering can considerably improve the analysis of (primarily large-scale) proteomics data sets. Here we show that spectral clustering can be used to infer additional peptide-spectrum matches and improve the quality of label-free quantitative proteomics data in data sets also containing only tens of MS runs. We analyzed four well-known public benchmark data sets that represent different experimental settings using spectral counting and peak intensity based label-free quantification. In both approaches, the additionally inferred peptide-spectrum matches through our spectra-cluster algorithm improved the detectability of low abundant proteins while increasing the accuracy of the derived quantitative data, without increasing the data sets' noise. Additionally, we developed a Proteome Discoverer node for our spectra-cluster algorithm which allows anyone to rebuild our proposed pipeline using the free version of Proteome Discoverer.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Espectrometria de Massas
/
Análise por Conglomerados
/
Proteoma
/
Proteômica
Limite:
Humans
Idioma:
En
Revista:
J Proteome Res
Assunto da revista:
BIOQUIMICA
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Áustria