DeepQuality: Mass Spectra Quality Assessment via Compressed Sensing and Deep Learning

Abstract

Motivation: Mass spectrometry-based proteomics is among the most commonly used methods for scrutinizing proteomic profiles in different organs for biological or medical researches. All the proteomic analyses including peptide/protein identification and quantification, differential expression analysis, biomarker discovery and so on are all based on the matching of mass spectra with peptide sequences, which is significantly influenced by the quality of the spectra, such as the peak numbers, noisy peaks, signal-to-noise ratios, etc. Hence, it is crucial to assess the quality of the spectra in order for filtering and/or post-processing after identification. The handcrafted features representing spectra quality, however, need human expertise to design and are difficult to optimize, and thus the existing assessing algorithms are still lacking in accuracy. Thus, there is a critical need for the robust and adaptive algorithm for mass spectra quality assessment. Results: We have developed a novel mass spectrum assessment software DeepQuality, based on the state-of-the-art compressed sensing and deep learning algorithms. We evaluated the algorithm on two publicly available tandem MS data sets, resulting in the AUC of 0.96 and 0.92, respectively, a significant improvement compared with the AUC of 0.85 and 0.91 of the existing method SpectrumQuality v2.0. Availability: Software available at https://github.com/horsepurve/DeepQuality