Publicaciones científicas

DeepMSPeptide: peptide detectability prediction using deep learning

15-feb-2020 | Revista: Bioinformatics

Guillermo Serrano, Elizabeth Guruceaga, Victor Segura


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.

CITA DEL ARTÍCULO  Bioinformatics. 2020 Feb 15;36(4):1279-1280. doi: 10.1093/bioinformatics/btz708.

Nuestros autores

Guillermo Serrano Sanz
Técnico de Investigación Bioinformático Programa de Biología Computacional
Elizabet Guruceaga Martínez
Técnico de Investigación Bioinformático Plataforma Bioinformática