SpectroVQ: Noise-Aware Compression of Proteomics Data via Vector-Quantized Deep Learning improves MS/MS data storage and Peptide Identification

Abstract

The amount of proteomics data generated has dramatically grown for the past decade due to the wider accessibility to mass spectrometers and technological advances. Current data storage and compression techniques largely treat mass spectra as meaningless series of numbers, wasting storage on useless noise and limiting the compression ratio. Here, we present SpectroVQ, a noise-aware vector-quantized autoencoder to compress and denoise peptide tandem mass spectra without any prior annotation by exploiting peptide fragmentation pattern using deep-learning Evaluation results showed that SpectroVQ can preferentially retain useful signals in spectra from diverse peptide ions, including those in unseen datasets. SpectroVQ achieved over 3-fold increase in compression ratio over mzMLb while maintaining over 0.9 in average cosine similarity and 90% agreement in peptide identifications. In addition, we develop a novel strategy to increase peptide identifications by ~15% via ordinary library searching, by leveraging the tuneable denoising capability of SpectroVQ.