Generate formula with SIRIUS

Uses SIRIUS to generate chemical formulae candidates.

Usage

generateFormulasSIRIUS(fGroups, ...)

# S4 method for class 'featureGroups'
generateFormulasSIRIUS(
  fGroups,
  MSPeakLists,
  specSimParams = getDefSpecSimParams(removePrecursor = TRUE),
  adduct = NULL,
  config = NULL,
  getFingerprints = FALSE,
  login = "check",
  alwaysLogin = FALSE,
  calculateFeatures = FALSE,
  featThreshold = 0,
  featThresholdAnn = 0.75,
  absAlignMzDev = defaultLim("mz", "narrow"),
  minIMSSpecSim = 0,
  projectPath = NULL,
  runMode = "execute",
  SIRIUSAPI = NULL,
  verbose = TRUE
)

# S4 method for class 'featureGroupsSet'
generateFormulasSIRIUS(
  fGroups,
  MSPeakLists,
  specSimParams = getDefSpecSimParams(removePrecursor = TRUE),
  adduct = NULL,
  config = NULL,
  login = "check",
  alwaysLogin = FALSE,
  calculateFeatures = FALSE,
  featThreshold = 0,
  featThresholdAnn = 0.75,
  minIMSSpecSim = 0,
  projectPath = NULL,
  ...,
  setThreshold = 0,
  setThresholdAnn = 0,
  setAvgSpecificScores = FALSE
)

Arguments

fGroups

featureGroups object for which formulae should be generated. This should be the same or a subset of the object that was used to create the specified MSPeakLists. In the case of a subset only the remaining feature groups in the subset are considered.

...

(sets workflow) Further arguments passed to the non-sets workflow method.

MSPeakLists

An MSPeakLists object that was generated for the supplied fGroups.

specSimParams

A named list with parameters that influence the calculation of the annotation similarity. See the spectral similarity parameters documentation for more details.

adduct

An adduct object (or something that can be converted to it with as.adduct). Examples: "[M-H]-", "[M+Na]+". If the featureGroups object has adduct annotations then these are used if adducts=NULL.

(sets workflow) The adduct argument is not supported for sets workflows, since the adduct annotations will then always be used.

config

A RSirius::JobSubmission configuration object, typically obtained with getSIRIUSConfig. If NULL, the default SIRIUS configuration is used.

login, alwaysLogin

Specifies if and how account logging of SIRIUS should be handled:

login=FALSE: no automatic login is performed and the active login status is not checked.

login="check": aborts if no active login is present.

login="interactive": interactively ask for login (using getPass).

login=c(username="...", password="..."): perform the login with the given details. For security reasons, please do not enter the details directly, but use e.g. environment variables or store/retrieve them with the keyring package.

if alwaysLogin=TRUE then a login is always performed, otherwise only if SIRIUS reports no active login.

See the SIRIUS website and patRoon handbook for more information.

NOTE: By loggin in you will accept the terms of the Service and Privacy Policy of the SIRIUS Webservice.

calculateFeatures

If TRUE fomulae are first calculated for all features prior to feature group assignment (see Candidate assignment in generateFormulas).

featThreshold

If calculateFeatures=TRUE: minimum presence (0-1) of a formula in all features before it is considered as a candidate for a feature group. For instance, featThreshold=0.75 dictates that a formula should be present in at least 75% of the features inside a feature group.

featThresholdAnn

As featThreshold, but only considers features with annotations. For instance, featThresholdAnn=0.75 dictates that a formula should be present in at least 75% of the features with annotations inside a feature group.

absAlignMzDev

When the group formula annotation consensus is made from feature annotations, the m/z values of annotated MS/MS fragments may slightly deviate from those of the corresponding group MS/MS peak list. The absAlignMzDev argument specifies the maximum m/z window used to re-align the mass peaks.

minIMSSpecSim

(IMS workflow) If the spectrum similarity of an IMS feature group compared to its IMS precursor (see assignMobilities) is at least this value, then the IMS feature group will not be subjected to the annotation algorithm and all feature annotation properties will be copied from its precursor. This assumes that feature annotation is primarily influenced by the MS/MS spectrum, and can be used to speed up the feature annotation process. All scorings, annotation similarities etc. are copied from the IMS precursor. The fragment annotations are also copied (fragInfo result column), however, these are adjusted based on the peak list data of the IMS feature group.

runMode, projectPath

Whether to execute a SIRIUS processing job (runMode="execute") or load results from an existing SIRIUS project (runMode"read"). If runMode="execute" then projectPath can be NULL and a temporary project will be used, otherwise projectPath must point to an existing project.

NOTE: if runMode="execute" then any existing project at projectPath will be removed.

NOTE: This is primarily intended for internal purposes, but may be of interest to e.g. re-import SIRIUS results.

(sets workflow) projectPath should be a character specifying the paths for each set.

SIRIUSAPI

An rsirius_api object for connecting to the SIRIUS API. If NULL, a new connection will be started automatically.

verbose

If TRUE then more output is shown.

setThreshold

(sets workflow) Minimum abundance for a candidate among all sets (0-1). For instance, a value of 1 means that the candidate needs to be present in all the set data.

setThresholdAnn

(sets workflow) As setThreshold, but only taking into account the set data that contain annotations for the feature group of the candidate.

setAvgSpecificScores

(sets workflow) If TRUE then set specific scorings (e.g. MS/MS match) are also averaged.

Value

A formulasSIRIUS object.

Details

This function uses sirius to generate formula candidates. This function is called when calling generateFormulas with algorithm="sirius".

Note that SIRIUS requires availability of MS/MS data.

Running SIRIUS

By default, patRoon tries to connect to a running instance of SIRIUS. This is generally faster and may be useful for debugging by e.g. checking the logs in SIRIUS. Otherwise, an attempt will be made to start SIRIUS automatically. The binaries are searched from the patRoon.path.SIRIUS package option, patRoonExt package or the system PATH environment variable. Any automatically started SIRIUS instances are automatically closed if jobs are finished. By default, a temporary SIRIUS project is made for SIRIUS data processing and removed afterwards. See the projectPath to change this.

SIRIUS 6 functionality

The interface to SIRIUS 6 is still in development and may be extended in the future. There is a vast amount of functionality available, which will require quite some effort to support all. However, the current functionality in patRoon is mostly equal to what was supported with previous SIRIUS releases. Any feedback on the inclusion of specific functionality is welcome!

References

Hoffmann MA, Nothias L, Ludwig M, Fleischauer M, Gentry EC, Witting M, Dorrestein PC, Dührkop K, Böcker S (2021). “High-confidence structural annotation of metabolites absent from spectral libraries.” Nature Biotechnology, 40(3), 411–421. ISSN 1546-1696. doi:10.1038/s41587-021-01045-9 . http://dx.doi.org/10.1038/s41587-021-01045-9.

Dührkop K, Nothias L, Fleischauer M, Reher R, Ludwig M, Hoffmann MA, Petras D, Gerwick WH, Rousu J, Dorrestein PC, Böcker S (2020). “Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra.” Nature Biotechnology, 39(4), 462–471. ISSN 1546-1696. doi:10.1038/s41587-020-0740-8 . http://dx.doi.org/10.1038/s41587-020-0740-8.

Duhrkop K, Fleischauer M, Ludwig M, Aksenov AA, Melnik AV, Meusel M, Dorrestein PC, Rousu J, Bocker S (2019). “SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information.” Nature Methods, 16(4), 299–302. doi:10.1038/s41592-019-0344-8 .

Duhrkop K, Bocker S (2015). “Fragmentation Trees Reloaded.” In Przytycka TM (ed.), Research in Computational Molecular Biology, 65–79. ISBN 978-3-319-16706-0.

Duhrkop K, Shen H, Meusel M, Rousu J, Bocker S (2015). “Searching molecular structure databases with tandem mass spectra using CSI:FingerID.” Proceedings of the National Academy of Sciences, 112(41), 12580–12585. doi:10.1073/pnas.1509788112 .

Bocker S, Letzel MC, Liptak Z, Pervukhin A (2008). “SIRIUS: decomposing isotope patterns for metabolite identification.” Bioinformatics, 25(2), 218–224. doi:10.1093/bioinformatics/btn603 .