A modular and expandable ecosystem for metabolomics data annotation in R
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A modular and expandable ecosystem for metabolomics data annotation in R. / Rainer, Johannes; Vicini, Andrea; Salzer, Liesa; Stanstrup, Jan; Badia, Josep M; Neumann, Steffen; Stravs, Michael A; Verri Hernandes, Vinicius; Gatto, Laurent; Gibb, Sebastian; Witting, Michael.
In: Metabolites, Vol. 12, No. 2, 173, 2022.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - A modular and expandable ecosystem for metabolomics data annotation in R
AU - Rainer, Johannes
AU - Vicini, Andrea
AU - Salzer, Liesa
AU - Stanstrup, Jan
AU - Badia, Josep M
AU - Neumann, Steffen
AU - Stravs, Michael A
AU - Verri Hernandes, Vinicius
AU - Gatto, Laurent
AU - Gibb, Sebastian
AU - Witting, Michael
N1 - CURIS 2022 NEXS 059
PY - 2022
Y1 - 2022
N2 - Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics experiments have become increasingly popular because of the wide range of metabolites that can be analyzed and the possibility to measure novel compounds. LC-MS instrumentation and analysis conditions can differ substantially among laboratories and experiments, thus resulting in non-standardized datasets demanding customized annotation workflows. We present an ecosystem of R packages, centered around the MetaboCoreUtils, MetaboAnnotation and CompoundDb packages that together provide a modular infrastructure for the annotation of untargeted metabolomics data. Initial annotation can be performed based on MS1 properties such as m/z and retention times, followed by an MS2-based annotation in which experimental fragment spectra are compared against a reference library. Such reference databases can be created and managed with the CompoundDb package. The ecosystem supports data from a variety of formats, including, but not limited to, MSP, MGF, mzML, mzXML, netCDF as well as MassBank text files and SQL databases. Through its highly customizable functionality, the presented infrastructure allows to build reproducible annotation workflows tailored for and adapted to most untargeted LC-MS-based datasets. All core functionality, which supports base R data types, is exported, also facilitating its re-use in other R packages. Finally, all packages are thoroughly unit-tested and documented and are available on GitHub and through Bioconductor.
AB - Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics experiments have become increasingly popular because of the wide range of metabolites that can be analyzed and the possibility to measure novel compounds. LC-MS instrumentation and analysis conditions can differ substantially among laboratories and experiments, thus resulting in non-standardized datasets demanding customized annotation workflows. We present an ecosystem of R packages, centered around the MetaboCoreUtils, MetaboAnnotation and CompoundDb packages that together provide a modular infrastructure for the annotation of untargeted metabolomics data. Initial annotation can be performed based on MS1 properties such as m/z and retention times, followed by an MS2-based annotation in which experimental fragment spectra are compared against a reference library. Such reference databases can be created and managed with the CompoundDb package. The ecosystem supports data from a variety of formats, including, but not limited to, MSP, MGF, mzML, mzXML, netCDF as well as MassBank text files and SQL databases. Through its highly customizable functionality, the presented infrastructure allows to build reproducible annotation workflows tailored for and adapted to most untargeted LC-MS-based datasets. All core functionality, which supports base R data types, is exported, also facilitating its re-use in other R packages. Finally, all packages are thoroughly unit-tested and documented and are available on GitHub and through Bioconductor.
KW - Faculty of Science
KW - Metabolomics
KW - Untargeted analysis
KW - Annotation
KW - R programming
KW - Small-compound databases
KW - Reproducible research
U2 - 10.3390/metabo12020173
DO - 10.3390/metabo12020173
M3 - Journal article
C2 - 35208247
VL - 12
JO - Metabolites
JF - Metabolites
SN - 2218-1989
IS - 2
M1 - 173
ER -
ID: 298628557