Deep Learning Enable Untargeted Metabolite Extraction from High Throughput Coverage Data-Independent Acquisition

The sequential window acquisition of all theoretical spectra (SWATH) technique is a specific variant of data-independent acquisition (DIA), which is supposed to increase the metabolite coverage and the reproducibility compared to data-dependent acquisition (DDA). However, SWATH technique lost the direct link between the precursor ion and the fragments. Here, we propose a deep-learning-based approach (DeepSWATH) to reconstruct the association between the MS/MS spectra and their precursors. Comparing with MS-DIAL, the proposed method can extract more accurate spectra with less noise to improve the identification accuracy of metabolites. Besides, DeepSWATH can also handle severe coelution conditions.