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bioRxiv · 10.1101/2020.03.22.002683

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

Ji, H.·Lu, H.·Zhang, Z.
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Abstract

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.

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Ji, H., Lu, H., Zhang, Z.. 2020-03-25. Deep Learning Enable Untargeted Metabolite Extraction from High Throughput Coverage Data-Independent Acquisition. https://doi.org/10.1101/2020.03.22.002683

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