DeepMSPeptide: peptide detectability prediction using deep learning.
Bioinformatics
; 36(4): 1279-1280, 2020 02 15.
Article
em En
| MEDLINE
| ID: mdl-31529040
ABSTRACT
SUMMARY:
The protein detection and quantification using high-throughput proteomic technologies is still challenging due to the stochastic nature of the peptide selection in the mass spectrometer, the difficulties in the statistical analysis of the results and the presence of degenerated peptides. However, considering in the analysis only those peptides that could be detected by mass spectrometry, also called proteotypic peptides, increases the accuracy of the results. Several approaches have been applied to predict peptide detectability based on the physicochemical properties of the peptides. In this manuscript, we present DeepMSPeptide, a bioinformatic tool that uses a deep learning method to predict proteotypic peptides exclusively based on the peptide amino acid sequences. AVAILABILITY AND IMPLEMENTATION DeepMSPeptide is available at https//github.com/vsegurar/DeepMSPeptide. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Proteômica
/
Aprendizado Profundo
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Espanha