A database searching approach c
an be used for metabolite identification in metabolomics by matching measured t
andem mass spectra (MS/MS) against the predicted fragments of metabolites in a database. Here, we present the open-source MIDAS algorithm (Metabolite Identification via Database Searching). To evaluate a metabolite-spectrum match (MSM), MIDAS first enumerates possible fragments from a metabolite by systematic bond dissociation, then calculates the plausibility of the fragments based on their fragmentation pathways,
and finally scores the MSM to assess how well the experimental MS/MS spectrum from collision-induced dissociation (CID) is explained by the metabolite鈥檚 predicted CID MS/MS spectrum. MIDAS was designed to search high-resolution t
andem mass spectra acquired on time-of-flight or Orbitrap mass spectrometer against a metabolite database in
an automated
and high-throughput m
anner. The accuracy of metabolite identification by MIDAS was benchmarked using four sets of st
andard t
andem mass spectra from MassB
ank. On average, for 77% of original spectra
and 84% of composite spectra, MIDAS correctly r
anked the true compounds as the first MSMs out of all MetaCyc metabolites as decoys. MIDAS correctly identified 46% more original spectra
and 59% more composite spectra at the first MSMs th
an an existing database-searching algorithm, MetFrag. MIDAS was showcased by searching a published real-world measurement of a metabolome from
Synechococcus sp. PCC 7002 against the MetaCyc metabolite database. MIDAS identified m
any metabolites missed in the previous study. MIDAS identifications should be considered only as c
andidate metabolites, which need to be confirmed using st
andard compounds. To facilitate m
anual validation, MIDAS provides
annotated spectra for MSMs
and labels observed mass spectral peaks with predicted fragments. The database searching
and m
anual validation c
an be performed online at
http://midas.omicsbio.org.