MassIVE MSV000083793

Partial Public PXD013888

Fast and accurate bacterial species identification in biological samples using LC-MS/MS mass spectrometry and machine learning (DIA dataset)


We have developed a new strategy for identifying bacterial species in biological samples using specific LC-MS/MS peptidic signatures. In the first training step, deep proteome coverage of bacteria of interest is obtained in Data Independent Acquisition (DIA) mode, followed by the use of machine learning to define the peptides the most susceptible to distinguish each bacterial species from the others. Then, in the second step, this peptidic signature is monitored in biological samples using targeted proteomics. This method, which allows the bacterial identification from clinical specimens in less than 4h, has been applied to 15 species representing 84% of all Urinary Tract Infections (UTI). This dataset contains all the DIA files that has been used by the machine learnings algorithms to define a peptidic signture for UTI. [doi:10.25345/C5KD19] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: Bacterial identification ; Urine ; LC-MSMS ; DIA ; Machine Learning


Principal Investigators: Arnaud Droit, CHU de Quebec Universite Laval, Canada
Submitting User: Arno


Roux-Dalvai F., Gotti C., Leclercq M., Hélie MC., Boissinot M., Arrey T.N., Dauly C., Fournier F., Kelly I., Marcoux J., Bestman-Smith J., Bergeron M.G., Droit A.
Fast and accurate bacterial species identification in urine specimens using LC-MS/MS mass spectrometry and machine learning.
Mol Cell Proteomics. 2019 Oct 4. pii: mcp.TIR119.001559. doi: 10.1074/mcp.TIR119.001559.

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Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.
Number of distinct protein accessions reported by originally submitted search results.