Predicting FingerPrint and compounds Identifications

This function creates an object of class predictParam that can be used to predict molecular fingerprints and compound identifications using CSI:FingerID and CANOPUS.

CSI:FIngerID identifies the structure of a molecule by predicting its molecular fingerprint and using this fingerprint to search in a molecular structure database.

CANOPUS (Dührkop et al.) predicts the presense/absense of more than 2500 compound classes. CANOPUS predicts these classes based solely on MS/MS data and without requiring database information. This means it can identify a class even if no molecular structure of that class exists in the molecular structure database.

Usage

predictParam(useScoreThreshold = TRUE, alwaysPredictHighRefMatches = FALSE)

Arguments

useScoreThreshold: logical whether to use a soft threshold to be applied to only compute FingerPrints for promising formula candidates. Enabling is highly recommended. Default is TRUE.
alwaysPredictHighRefMatches: logical wether to predict FingerPrint/Classes&Structure for formulas candidates with reference spectrum similarity > Sirius.minReferenceMatchScoreToInject no matter which score threshold rules apply. Default is FALSE.

Value

An object of class predictParam with the specified parameters.

Note

For more information, see the Sirius documentation.

References

Dührkop K, Shen H, Meusel M, et al. (2015). Searching molecular structure databases with tandem mass spectra using CSI:FingerID. Proceedings of the National Academy of Sciences, 112, 12580-12585. doi:10.1073/pnas.1509788112

Dührkop K, Nothias L-F, Fleischauer M, et al. (2021). Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra. Nature Biotechnology, 39, 462-471. doi:10.1038/s41587-020-0740-8

Examples

# Example of setting up the parameters for the prediction of molecular
# fingerprints and compound class
param <- predictParam()