SPEQ: Quality assessment of peptide tandem mass spectra with deep learning

Abstract

Motivation: In proteomics, database search programs are routinely used for peptide identification from tandem mass spectrometry data. However, many low-quality spectra cannot be interpreted by any programs. Meanwhile, certain high-quality spectra may not be identified due to incompleteness of the database or failure of the software. Thus, spectrum quality (SPEQ) assessment tools are helpful programs that can eliminate poor-quality spectra before the database search and highlight the high-quality spectra that are not identified in the initial search. These spectra may be valuable candidates for further analyses. Results: We propose SPEQ: a spectrum quality assessment tool that uses a deep neural network to classify spectra into high-quality, which are worthy candidates for interpretation, and low-quality, which lack sufficient information for identification. SPEQ was compared with a few other prediction models and demonstrated improved prediction accuracy.