DIA-NN: Deep neural networks substantially improve the identification performance of Data-independent acquisition (DIA) in proteomics

Data-independent acquisition (DIA-MS) strategies, like SWATH-MS, have been developed to increase consistency, quantification precision and proteomic depth in label-free proteomic experiments. They aim to overcome stochasticity in the selection of precursor ions by utilising (mass-) windowed acquisition that is followed by computational reconstruction of the chromatograms. While DIA methods increasingly outperform typical data-dependent methods in identification consistency and precision specifically on large sample series, possibilities remain for further improvements. At present, only a fraction of the information recorded in the complex DIA spectra is extracted by the software analysis pipelines. Here we present a software tool (DIA-NN) that introduces artificial neural nets and a new quantification strategy to enhance signal processing in DIA-data. DIA-NN greatly improves identification of precursor ions and, as a consequence, protein quantification accuracy. The performance of DIA-NN demonstrates that deep learning provides opportunities to boost the analysis of data-independent acquisition workflows in proteomics.