Package: MsBackendMetabolomicsWorkbench
Authors: Gabriele Tomè [aut, cre] (ORCID: https://orcid.org/0000-0002-3976-6068, fnd:
MetaRbolomics4Galaxy project (CUP: D53C25001030003) co-funded by the
Autonomous Province of Bolzano under the Joint Projects South
Tyrol–Germany 2025 program.), Philippine Louail [aut] (ORCID: https://orcid.org/0009-0007-5429-6846), Johannes Rainer
[aut] (ORCID: https://orcid.org/0000-0002-6977-7147)
Last modified: 2026-06-23 10:36:01.251798
Compiled: Tue Jun 23 10:37:38 2026
Metabolomics experiments and results including mass spectrometry (MS) data can be deposited in several public repositories, such as Metabolomics Workbench repository, a data resource developed by the NIH Common Fund’s Data Repository and Coordinating Center (DRCC) at the San Diego Supercomputer Center, University of California San Diego. While data is available, manual lookup and download is cumbersome hampering the re-analysis of public data and replication of results. The MsBackendMetabolomicsWorkbench package closes this gap by providing functionality to query, retrieve and cache MS data from Metabolomics Workbench directly from R hence enabling a direct and seamless integration of MS data from Metabolomics Workbench into R-based analysis workflows. MsBackendMetabolomicsWorkbench leverages on Bioconductor’s BiocFileCache for caching remote data locally and provides a MS data backend for the Spectra package to enable loading and integrating cached MS data directly into R.
The package can be installed from within R with the commands below:
Each experiment in Metabolomics Workbench is identified by a unique
accession starting with ST followed by a number. The repository
provides programmatic access via the Metabolomics Workbench REST API and
POST requests, so users can query experiments and associated data files
directly. With MsBackendMetabolomicsWorkbench, you can
resolve these accessions and download supported MS files
(mzML/CDF/mzXML) into a local cache, then load them into a
Spectra object for downstream processing.
Below we list all files from Metabolomics Workbench experiment ST002115.
library(MsBackendMetabolomicsWorkbench)
#' List files of a Metabolomics Workbench data set
all_files <- mwb_list_files("ST002115")
head(all_files)## zip_file sample_file
## 1 ST002115_Data.zip HT1080_DMSO_01_RP.mzXML
## 3 ST002115_Data.zip HT1080_DMSO_02_RP.mzXML
## 5 ST002115_Data.zip HT1080_DMSO_03_RP.mzXML
## 7 ST002115_Data.zip HT1080_ML162_01_RP.mzXML
## 9 ST002115_Data.zip HT1080_ML162_02_RP.mzXML
## 11 ST002115_Data.zip HT1080_ML162_03_RP.mzXMLMS data files in supported formats (mzML, CDF, mzXML) can be directly
loaded using the MsBackendMetabolomicsWorkbench backend
into R as a Spectra object
(MsBackendMetabolomicsWorkbench directly extends
Spectra’s MsBackendMzR backend and therefore
supports import of MS data files in these formats). There are two
supported download modes:
Below we list zip file of Metabolomics Workbench experiment ST002115.
## [1] "ST002115_Data.zip"The FTP archive contains all files for the experiment, which may
include unneeded files. If only a subset of files is needed, the default
POST option (with ftp_zip = FALSE) is more efficient. By
default, all MS data files of the data set would be retrieved, but in
our example below we restrict to a few data files to reduce the amount
of data that needs to be downloaded. To this end we define a pattern
matching the file name of only some data files using the
filePattern parameter.
library(Spectra)
#' Load MS data files of one data set
s <- Spectra("ST002115", filePattern = "01_RP.mzXML$", ftp_zip = FALSE,
source = MsBackendMetabolomicsWorkbench())
s## MSn data (Spectra) with 5751 spectra in a MsBackendMetabolomicsWorkbench backend:
## msLevel rtime scanIndex
## <integer> <numeric> <integer>
## 1 0 0.569753 1
## 2 0 1.627010 2
## 3 0 2.684380 3
## 4 0 3.739880 4
## 5 0 4.795500 5
## ... ... ... ...
## 5747 0 1498.87 1435
## 5748 0 1499.93 1436
## 5749 0 1500.98 1437
## 5750 0 1502.04 1438
## 5751 0 1503.10 1439
## ... 37 more variables/columns.
##
## file(s):
## ST002115_Data_HT1080_DMSO_01_RP.mzXML
## ST002115_Data_HT1080_ML162_01_RP.mzXML
## ST002115_Data_HT1080_ML210_01_RP.mzXML
## ... 1 more filesThis call downloaded 4 files from the experiment into the local cache
and loaded them as a Spectra object. The downloading and
caching of the data is handled by Bioconductor’s BiocFileCache.
The local cache can thus also be managed directly using functionality
from that package. Any subsequent loading of the same data files will
load the locally cached versions avoiding thus repetitive download of
the same data.
The Spectra object with the MS data files of the
Metabolomics Workbench data set enables now any subsequent analysis of
the data in R. On top of the spectra variables and mass peak data values
that are provided by the MS data files also additional information
related to the Metabolomics Workbench data set are available as specific
spectra variables. We list all available spectra variables of
the data set below.
## [1] "msLevel" "rtime"
## [3] "acquisitionNum" "scanIndex"
## [5] "dataStorage" "dataOrigin"
## [7] "centroided" "smoothed"
## [9] "polarity" "precScanNum"
## [11] "precursorMz" "precursorIntensity"
## [13] "precursorCharge" "collisionEnergy"
## [15] "isolationWindowLowerMz" "isolationWindowTargetMz"
## [17] "isolationWindowUpperMz" "peaksCount"
## [19] "totIonCurrent" "basePeakMZ"
## [21] "basePeakIntensity" "electronBeamEnergy"
## [23] "ionisationEnergy" "lowMZ"
## [25] "highMZ" "mergedScan"
## [27] "mergedResultScanNum" "mergedResultStartScanNum"
## [29] "mergedResultEndScanNum" "injectionTime"
## [31] "filterString" "spectrumId"
## [33] "ionMobilityDriftTime" "scanWindowLowerLimit"
## [35] "scanWindowUpperLimit" "mwb_id"
## [37] "zip_file" "file_name"The Metabolomics Workbench-specific variables are
"mwb_id", "zip_file" and
"file_name" providing the Metabolomics Workbench ID of the
data set, the zip file name in the FTP server and the original data file
name in the Metabolomics Workbench for each individual spectrum.
## DataFrame with 5751 rows and 3 columns
## mwb_id zip_file file_name
## <character> <character> <character>
## 1 ST002115 ST002115_Data.zip ST002115_Data_HT1080..
## 2 ST002115 ST002115_Data.zip ST002115_Data_HT1080..
## 3 ST002115 ST002115_Data.zip ST002115_Data_HT1080..
## 4 ST002115 ST002115_Data.zip ST002115_Data_HT1080..
## 5 ST002115 ST002115_Data.zip ST002115_Data_HT1080..
## ... ... ... ...
## 5747 ST002115 ST002115_Data.zip ST002115_Data_HT1080..
## 5748 ST002115 ST002115_Data.zip ST002115_Data_HT1080..
## 5749 ST002115 ST002115_Data.zip ST002115_Data_HT1080..
## 5750 ST002115 ST002115_Data.zip ST002115_Data_HT1080..
## 5751 ST002115 ST002115_Data.zip ST002115_Data_HT1080..## [1] "ST002115_Data_HT1080_DMSO_01_RP.mzXML"
## [2] "ST002115_Data_HT1080_DMSO_01_RP.mzXML"
## [3] "ST002115_Data_HT1080_DMSO_01_RP.mzXML"
## [4] "ST002115_Data_HT1080_DMSO_01_RP.mzXML"
## [5] "ST002115_Data_HT1080_DMSO_01_RP.mzXML"
## [6] "ST002115_Data_HT1080_DMSO_01_RP.mzXML"The mwb_sync() function can be used to
synchronize the local content of a
MsBackendMetabolomicsWorkbench and is useful if, for
example, locally cached files were deleted. The function checks if all
data files of the backend are available locally and eventually downloads
and caches missing files.
## MsBackendMetabolomicsWorkbench with 5751 spectra
## msLevel rtime scanIndex
## <integer> <numeric> <integer>
## 1 0 0.569753 1
## 2 0 1.627010 2
## 3 0 2.684380 3
## 4 0 3.739880 4
## 5 0 4.795500 5
## ... ... ... ...
## 5747 0 1498.87 1435
## 5748 0 1499.93 1436
## 5749 0 1500.98 1437
## 5750 0 1502.04 1438
## 5751 0 1503.10 1439
## ... 37 more variables/columns.
##
## file(s):
## ST002115_Data_HT1080_DMSO_01_RP.mzXML
## ST002115_Data_HT1080_ML162_01_RP.mzXML
## ST002115_Data_HT1080_ML210_01_RP.mzXML
## ... 1 more filesIn addition, it is also possible to manually cache and
download selected files from Metabolomics Workbench using the
mwb_sync_data_files() function. Before downloading, this
function first evaluates if the respective data files are already cached
and only downloads them if needed. As a result, the function returns a
data.frame with the storage location and other information
of the cached file(s). Below we use this function to retrieve the local
storage information on one of the data files of the Metabolomics
Workbench data set ST002115:
## rid mwb_id zip_file file_name
## 1 BFC1 ST002115 ST002115_Data.zip ST002115_Data_HT1080_DMSO_01_RP.mzXML
## rpath
## 1 /github/home/.cache/R/BiocFileCache/ST002115_Data_HT1080_DMSO_01_RP.mzXMLThe mwb_cached_data_files() function can be used to
inspect and list all locally cached Metabolomics Workbench data files.
This function does not require an active internet connection since only
local content is queried. With the default settings, a
data.frame with all available data files is returned.
## rid mwb_id zip_file file_name
## 1 BFC1 ST002115 ST002115_Data.zip ST002115_Data_HT1080_DMSO_01_RP.mzXML
## 2 BFC2 ST002115 ST002115_Data.zip ST002115_Data_HT1080_ML162_01_RP.mzXML
## 3 BFC3 ST002115 ST002115_Data.zip ST002115_Data_HT1080_ML210_01_RP.mzXML
## 4 BFC4 ST002115 ST002115_Data.zip ST002115_Data_HT1080_RSL3_01_RP.mzXML
## rpath
## 1 /github/home/.cache/R/BiocFileCache/ST002115_Data_HT1080_DMSO_01_RP.mzXML
## 2 /github/home/.cache/R/BiocFileCache/ST002115_Data_HT1080_ML162_01_RP.mzXML
## 3 /github/home/.cache/R/BiocFileCache/ST002115_Data_HT1080_ML210_01_RP.mzXML
## 4 /github/home/.cache/R/BiocFileCache/ST002115_Data_HT1080_RSL3_01_RP.mzXMLLocally cached files for a Metabolomics Workbench data set can be
removed using the mwb_delete_cache() function providing the
ID of the Metabolomics Workbench data set for which local data files
should be removed.
Next to the MsBackendMetabolomicsWorkbench backend for
Spectra objects, the
MsBackendMetabolomicsWorkbench package provides also various
utility functions to query and retrieve information from Metabolomics
Workbench.
The mwb_rest_request() queries the Metabolomics
Workbench REST API for a given study/analysis ID and output item
(e.g. summary, factors). Returns the raw
response as a character string in the format specified by
outputFormat (json or txt).
Below we query the REST API for the summary of the Metabolomics Workbench data set ST002115:
summary <- mwb_rest_request("ST002115", outputItem = "summary",
outputFormat = "json")
fromJSON(summary)## $study_id
## [1] "ST002115"
##
## $study_title
## [1] "LC-MS analysis of metabolic changes induced by GPX4 inhibitor treatment in cultured HT1080 cells"
##
## $species
## [1] "Homo sapiens"
##
## $institute
## [1] "University of Texas MD Anderson Cancer Center"
##
## $analysis_type
## [1] "LC-MS"
##
## $number_of_samples
## [1] "12"
##
## $submission_date
## [1] "2022-04-20"
##
## $release_date
## [1] "2022-04-14"
##
## $version
## [1] "1"
##
## $revision_no
## [1] "1"
##
## $revision_datetime
## [1] "-"
##
## $revision_comment
## [1] "-"
##
## $license
## [1] "CC BY 4.0"
##
## $license_url
## [1] "https://creativecommons.org/licenses/by/4.0/"
##
## $study_url
## [1] "https://www.metabolomicsworkbench.org/data/DRCCMetadata.php?StudyID=ST002115"The mwb_ftp_download() function allows to download the
zip archive of the experiment (directly, i.e., without caching). As an
example we download below the zip archive to a temporary folder. In our
example below we do not run it to reduce the amount of data that needs
to be downloaded.
The mwb_metadata() function retrieves the metadata of a
given Metabolomics Workbench data set as a list of 2
data.frame: - MS_run: contains the metadata of
the MS runs of the data set, identified by the analysis ID(s), -
sample_annotation: contains the metadata of the samples of
the data set. Not all the experiments have a column with the associated
sample file name, the association cab be retrieved by the
mwb_list_files() function.
The function handles the case of multiple analysis IDs by combining
the metadata of all analysis IDs into a single data.frame
for the experiment and a single data.frame for the sample
annotation.
Below we retrieve the metadata of the data set ST002115:
## STUDY_ID ANALYSIS_ID VERSION CREATED_ON
## 1 ST002115 AN003513 1 02-08-2024
## 2 ST002115 AN003514 1 02-08-2024
## PROJECT_TITLE
## 1 A ferroptosis defense mechanism mediated by glycerol 3-phosphate dehydrogenase 2 in mitochondria
## 2 A ferroptosis defense mechanism mediated by glycerol 3-phosphate dehydrogenase 2 in mitochondria
## PROJECT_SUMMARY
## 1 Mechanisms of defense against ferroptosis (an iron-dependent form of cell death induced by lipid peroxidation) in cellular organelles remain poorly understood, hindering our ability to target ferroptosis in disease treatment. In this study, metabolomic analyses revealed that treatment of cancer cells with glutathione peroxidase 4 (GPX4) inhibitors results in intracellular glycerol 3-phosphate (G3P) depletion. We further showed that supplementation of cancer cells with G3P attenuates ferroptosis induced by GPX4 inhibitors in a G3P dehydrogenase 2 (GPD2)-dependent manner; GPD2 deletion sensitizes cancer cells to GPX4 inhibition-induced mitochondrial lipid peroxidation and ferroptosis, and combined deletion of GPX4 and GPD2 synergistically suppresses tumor growth by inducing ferroptosis in vivo. Mechanistically, inner mitochondrial membrane-localized GPD2 couples G3P oxidation with ubiquinone reduction to ubiquinol, which acts as a radical-trapping antioxidant to suppress ferroptosis in mitochondria. Taken together, these results reveal that GPD2 participates in ferroptosis defense in mitochondria by generating ubiquinol.
## 2 Mechanisms of defense against ferroptosis (an iron-dependent form of cell death induced by lipid peroxidation) in cellular organelles remain poorly understood, hindering our ability to target ferroptosis in disease treatment. In this study, metabolomic analyses revealed that treatment of cancer cells with glutathione peroxidase 4 (GPX4) inhibitors results in intracellular glycerol 3-phosphate (G3P) depletion. We further showed that supplementation of cancer cells with G3P attenuates ferroptosis induced by GPX4 inhibitors in a G3P dehydrogenase 2 (GPD2)-dependent manner; GPD2 deletion sensitizes cancer cells to GPX4 inhibition-induced mitochondrial lipid peroxidation and ferroptosis, and combined deletion of GPX4 and GPD2 synergistically suppresses tumor growth by inducing ferroptosis in vivo. Mechanistically, inner mitochondrial membrane-localized GPD2 couples G3P oxidation with ubiquinone reduction to ubiquinol, which acts as a radical-trapping antioxidant to suppress ferroptosis in mitochondria. Taken together, these results reveal that GPD2 participates in ferroptosis defense in mitochondria by generating ubiquinol.
## INSTITUTE LAST_NAME FIRST_NAME
## 1 University of Texas MD Anderson Cancer Center Gan Boyi
## 2 University of Texas MD Anderson Cancer Center Gan Boyi
## ADDRESS EMAIL PHONE
## 1 6565 MD Anderson Blvd, Houston, TX 77030 [email protected] 713-792-8653
## 2 6565 MD Anderson Blvd, Houston, TX 77030 [email protected] 713-792-8653
## DOI
## 1 http://dx.doi.org/10.21228/M8HD8Q
## 2 http://dx.doi.org/10.21228/M8HD8Q
## STUDY_TITLE
## 1 LC-MS analysis of metabolic changes induced by GPX4 inhibitor treatment in cultured HT1080 cells
## 2 LC-MS analysis of metabolic changes induced by GPX4 inhibitor treatment in cultured HT1080 cells
## STUDY_SUMMARY
## 1 HT1080 cells were treated with vehicle (DMSO), RSL3 (10 micromolar), ML210 (10 micromolar), or ML162 (10 micromolar) for 2 hours. Cellular metabolites were then extracted and analyzed by LC-MS.
## 2 HT1080 cells were treated with vehicle (DMSO), RSL3 (10 micromolar), ML210 (10 micromolar), or ML162 (10 micromolar) for 2 hours. Cellular metabolites were then extracted and analyzed by LC-MS.
## SUBMIT_DATE SUBJECT_TYPE SUBJECT_SPECIES TAXONOMY_ID CELL_STRAIN_DETAILS
## 1 2022-03-02 Cultured cells Homo sapiens 9606 HT1080
## 2 2022-03-02 Cultured cells Homo sapiens 9606 HT1080
## COLLECTION_SUMMARY
## 1 Metabolites were extracted from cells in 35 mm culture plates by rapidly aspirating the culture medium and incubating the plates with 0.6 ml of an 80% methanol: 20% water mixture on a cold block on dry ice for 15 min. Next, the cell material was scraped into Eppendorf tubes pre-chilled on ice. After centrifugation at 13,000 RCF for 5 min at 4 °C, the supernatant was collected into a fresh tube and stored on dry ice until analysis.
## 2 Metabolites were extracted from cells in 35 mm culture plates by rapidly aspirating the culture medium and incubating the plates with 0.6 ml of an 80% methanol: 20% water mixture on a cold block on dry ice for 15 min. Next, the cell material was scraped into Eppendorf tubes pre-chilled on ice. After centrifugation at 13,000 RCF for 5 min at 4 °C, the supernatant was collected into a fresh tube and stored on dry ice until analysis.
## SAMPLE_TYPE STORAGE_CONDITIONS
## 1 Cultured cells -80?
## 2 Cultured cells -80?
## TREATMENT_SUMMARY
## 1 Cells were seeded in 35-mm culture plates. When the cell confluence reached 70-80%, cells were treated with RSL3, ML210, or ML162 for 2 hours.
## 2 Cells were seeded in 35-mm culture plates. When the cell confluence reached 70-80%, cells were treated with RSL3, ML210, or ML162 for 2 hours.
## SAMPLEPREP_SUMMARY
## 1 For analysis by reverse phase chromatography, just before analysis, 500 µL of extract was dried under a nitrogen gas flow and then resuspended in 100 µL of water. For analysis by HILIC chromatography, the extracts were analyzed directly.
## 2 For analysis by reverse phase chromatography, just before analysis, 500 µL of extract was dried under a nitrogen gas flow and then resuspended in 100 µL of water. For analysis by HILIC chromatography, the extracts were analyzed directly.
## PROCESSING_STORAGE_CONDITIONS EXTRACT_STORAGE
## 1 4? 4?
## 2 4? 4?
## CHROMATOGRAPHY_SUMMARY
## 1 The gradient was 0 min, 0% B; 2.5 min, 0% B; 5 min, 20% B; 7.5 min, 20% B; 13 min, 55% B; 15.5 min, 95% B; 18.5 min, 95% B; 19 min, 0% B; and 25 min, 0% B. Solvent A was 10 mM tributylamine and 15 mm acetic acid in water; Solvent B was methanol. The injection volume was 10 µL.
## 2 The gradient was 0 min, 85% B; 2 min, 85% B; 3 min, 80% B; 5 min, 80% B; 6 min, 75% B; 7 min, 75% B; 8 min, 70% B; 9 min, 70% B; 10 min, 50% B; 12 min, 50% B; 13 min, 25% B; 16 min, 25% B; 18 min, 0% B; 23 min, 0% B; 24 min, 85% B; 30 min, 85% B. The injection volume was 5 µL.
## INSTRUMENT_NAME COLUMN_NAME
## 1 Thermo Accela 1250 Phenomenex Synergi Hydro RP 100 A (100 x 2mm,2.5um)
## 2 Thermo Accela 1250 Waters XBridge BEH Amide (150 x 2.1mm,2.5um,100A)
## COLUMN_TEMPERATURE FLOW_RATE
## 1 40 200
## 2 40 150
## SOLVENT_A SOLVENT_B
## 1 100% water; 15 mM acetic acid; 10 mM tributylamine 100% methanol
## 2 95% water/5% acetonitrile; 20 mM ammonium acetate, pH 9.4 100% acetonitrile
## CHROMATOGRAPHY_TYPE ANALYSIS_TYPE INSTRUMENT_TYPE MS_TYPE
## 1 Reversed phase MS Orbitrap ESI
## 2 HILIC MS Orbitrap ESI
## MS_COMMENTS
## 1 The scan range was 80-1000 m/z. Raw data files were converted to mzXML format using msconvert (ProteoWizard). Data was analyzed in the MAVEN software suite and metabolite assignments were made using a previously generated list of retention times derived from pure standard solutions.
## 2 The scan range was 80-1000 m/z. Raw data files were converted to mzXML format using msconvert (ProteoWizard). Data was analyzed in the MAVEN software suite and metabolite assignments were made using a previously generated list of retention times derived from pure standard solutions.
## ION_MODE
## 1 NEGATIVE
## 2 NEGATIVE## Subject ID Sample ID Factors: Treatment
## 1 - HT1080_DMSO_01 DMSO
## 2 - HT1080_DMSO_02 DMSO
## 3 - HT1080_DMSO_03 DMSO
## 4 - HT1080_ML162_01 ML162
## 5 - HT1080_ML162_02 ML162
## 6 - HT1080_ML162_03 ML162
## 7 - HT1080_ML210_01 ML210
## 8 - HT1080_ML210_02 ML210
## 9 - HT1080_ML210_03 ML210
## 10 - HT1080_RSL3_01 RSL3
## 11 - HT1080_RSL3_02 RSL3
## 12 - HT1080_RSL3_03 RSL3
## Additional sample data: RAW_FILE_NAME
## 1 HT1080_DMSO_01.mzXML
## 2 HT1080_DMSO_02.mzXML
## 3 HT1080_DMSO_03.mzXML
## 4 HT1080_ML162_01.mzXML
## 5 HT1080_ML162_02.mzXML
## 6 HT1080_ML162_03.mzXML
## 7 HT1080_ML210_01.mzXML
## 8 HT1080_ML210_02.mzXML
## 9 HT1080_ML210_03.mzXML
## 10 HT1080_RSL3_01.mzXML
## 11 HT1080_RSL3_02.mzXML
## 12 HT1080_RSL3_03.mzXML
## Additional sample data: RAW_FILE_NAME_2
## 1 HT1080_DMSO_01_HILIC.mzXML
## 2 HT1080_DMSO_02_HILIC.mzXML
## 3 HT1080_DMSO_03_HILIC.mzXML
## 4 HT1080_ML162_01_HILIC.mzXML
## 5 HT1080_ML162_02_HILIC.mzXML
## 6 HT1080_ML162_03_HILIC.mzXML
## 7 HT1080_ML210_01_HILIC.mzXML
## 8 HT1080_ML210_02_HILIC.mzXML
## 9 HT1080_ML210_03_HILIC.mzXML
## 10 HT1080_RSL3_01_HILIC.mzXML
## 11 HT1080_RSL3_02_HILIC.mzXML
## 12 HT1080_RSL3_03_HILIC.mzXML## R version 4.6.0 (2026-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] MsBackendMetabolomicsWorkbench_0.1.7 jsonlite_2.0.0
## [3] Spectra_1.23.3 BiocParallel_1.47.0
## [5] S4Vectors_0.51.3 BiocGenerics_0.59.7
## [7] generics_0.1.4 BiocStyle_2.41.0
##
## loaded via a namespace (and not attached):
## [1] xfun_0.59 bslib_0.11.0 httr2_1.2.2
## [4] Biobase_2.73.1 vctrs_0.7.3 tools_4.6.0
## [7] curl_7.1.0 parallel_4.6.0 tibble_3.3.1
## [10] RSQLite_3.53.2 cluster_2.1.8.2 blob_1.3.0
## [13] pkgconfig_2.0.3 data.table_1.18.4 dbplyr_2.6.0
## [16] lifecycle_1.0.5 stringr_1.6.0 compiler_4.6.0
## [19] progress_1.2.3 codetools_0.2-20 ncdf4_1.24
## [22] clue_0.3-68 htmltools_0.5.9 sys_3.4.3
## [25] buildtools_1.0.0 sass_0.4.10 yaml_2.3.12
## [28] tidyr_1.3.2 pillar_1.11.1 crayon_1.5.3
## [31] jquerylib_0.1.4 MASS_7.3-65 cachem_1.1.0
## [34] MetaboCoreUtils_1.21.1 tidyselect_1.2.1 rvest_1.0.5
## [37] digest_0.6.39 stringi_1.8.7 purrr_1.2.2
## [40] dplyr_1.2.1 maketools_1.3.2 fastmap_1.2.0
## [43] archive_1.1.13 cli_3.6.6 magrittr_2.0.5
## [46] withr_3.0.3 prettyunits_1.2.0 filelock_1.0.3
## [49] rappdirs_0.3.4 bit64_4.8.2 rmarkdown_2.31
## [52] httr_1.4.8 bit_4.6.0 otel_0.2.0
## [55] hms_1.1.4 memoise_2.0.1 evaluate_1.0.5
## [58] knitr_1.51 IRanges_2.47.2 BiocFileCache_3.3.0
## [61] rlang_1.2.0 Rcpp_1.1.1-1.1 glue_1.8.1
## [64] DBI_1.3.0 mzR_2.47.0 selectr_0.5-1
## [67] BiocManager_1.30.27 xml2_1.6.0 R6_2.6.1
## [70] plyr_1.8.9 fs_2.1.0 ProtGenerics_1.39.2
## [73] MsCoreUtils_1.25.4