Package: MsBackendMassIVE
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: 2026-06-03 13:46:20.527703
Compiled: Wed Jun 3 13:47:55 2026
Metabolomics experiments and results including mass spectrometry (MS)
data can be deposited in several public repositories, such as MassIVE
(Mass Spectrometry Interactive Virtual Environment). MassIVE is a
community resource developed by the NIH-funded Center for Computational
Mass Spectrometry at UC San Diego to promote the global, free exchange
of mass spectrometry data. MassIVE supports deposition of both
proteomics and metabolomics experiments and is a full member of the ProteomeXchange consortium.
While data is available, manual lookup and download is cumbersome
hampering the re-analysis of public data and replication of results. The
MsBackendMassIVE package closes this gap by providing
functionality to query, retrieve and cache MS data from MassIVE directly
from R hence enabling a direct and seamless integration of MS data from
MassIVE into R-based analysis workflows. MsBackendMassIVE
leverages on Bioconductor’s r Biocpkg("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 MassIVE is identified by a unique accession starting with MSV followed by a number. While the MassIVE web page allows only a manual, non programmatic, lookup of data and experiments, a separate, central registry of data files and experiments is hosted on GNPS2. This datasetcache registry allows programmatic access and is used by MsBackendMassIVE to query information on MassIVE experiments.
Below we list all files from the MassIVE data set with the ID MSV000080547.
library(MsBackendMassIVE)
#' List files of a MassIVE data set
all_files <- massive_list_files("MSV000080547")
head(all_files)## [1] "ccms_parameters/params.xml"
## [2] "peak/Quant_assesment_QE/AG_spiked_sample1.mzXML"
## [3] "peak/Quant_assesment_QE/AG_spiked_sample10.mzXML"
## [4] "peak/Quant_assesment_QE/AG_spiked_sample11.mzXML"
## [5] "peak/Quant_assesment_QE/AG_spiked_sample12.mzXML"
## [6] "peak/Quant_assesment_QE/AG_spiked_sample13.mzXML"These files are accessible through the FTP path associated with the
MassIVE data set. Below we use the massive_ftp_path()
function to return the FTP path for our test data set.
## [1] "ftp://massive-ftp.ucsd.edu/v01/MSV000080547"MS data files in supported formats (mzML, CDF, mzXML) can be directly
loaded using the MsBackendMassIVE backend into R as a
Spectra object (MsBackendMassIVE directly
extends Spectra’s MsBackendMzR backend and
therefore supports import of MS data files in these formats). 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("MSV000080547", filePattern = "1.mzML$",
source = MsBackendMassIVE())
s## MSn data (Spectra) with 4322 spectra in a MsBackendMassIVE backend:
## msLevel rtime scanIndex
## <integer> <numeric> <integer>
## 1 1 0.497 1
## 2 1 0.829 2
## 3 1 1.159 3
## 4 1 1.488 4
## 5 1 1.818 5
## ... ... ... ...
## 4318 1 718.439 2157
## 4319 1 718.769 2158
## 4320 1 719.099 2159
## 4321 1 719.429 2160
## 4322 1 719.759 2161
## ... 36 more variables/columns.
##
## file(s):
## AG_spiked_sample1.mzML
## AG_spiked_sample11.mzMLThis call downloaded 2 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 MassIVE
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 MassIVE 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" "massive_id"
## [37] "data_file"The MassIVE-specific variables are "massive_id" and
"data_file" providing the MassIVE ID of the data set and
the original data file name in the MassIVE FTP server for each
individual spectrum.
## DataFrame with 4322 rows and 2 columns
## massive_id data_file
## <character> <character>
## 1 MSV000080547 peak/Quant_assesment..
## 2 MSV000080547 peak/Quant_assesment..
## 3 MSV000080547 peak/Quant_assesment..
## 4 MSV000080547 peak/Quant_assesment..
## 5 MSV000080547 peak/Quant_assesment..
## ... ... ...
## 4318 MSV000080547 peak/Quant_assesment..
## 4319 MSV000080547 peak/Quant_assesment..
## 4320 MSV000080547 peak/Quant_assesment..
## 4321 MSV000080547 peak/Quant_assesment..
## 4322 MSV000080547 peak/Quant_assesment..## [1] "AG_spiked_sample1.mzML" "AG_spiked_sample1.mzML" "AG_spiked_sample1.mzML"
## [4] "AG_spiked_sample1.mzML" "AG_spiked_sample1.mzML" "AG_spiked_sample1.mzML"The massive_sync() function can be used to
synchronize the local content of a
MsBackendMassIVE 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.
## MsBackendMassIVE with 4322 spectra
## msLevel rtime scanIndex
## <integer> <numeric> <integer>
## 1 1 0.497 1
## 2 1 0.829 2
## 3 1 1.159 3
## 4 1 1.488 4
## 5 1 1.818 5
## ... ... ... ...
## 4318 1 718.439 2157
## 4319 1 718.769 2158
## 4320 1 719.099 2159
## 4321 1 719.429 2160
## 4322 1 719.759 2161
## ... 36 more variables/columns.
##
## file(s):
## AG_spiked_sample1.mzML
## AG_spiked_sample11.mzMLIn addition, it is also possible to manually cache and
download selected files from MassIVE using the
massive_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
MassIVE data set MSV000080547:
## rid massive_id data_file
## 1 BFC2 MSV000080547 peak/Quant_assesment_QQQ/AG_spiked_sample11.mzML
## rpath
## 1 /github/home/.cache/R/BiocFileCache/AG_spiked_sample11.mzMLThe massive_cached_data_files() function can be used to
inspect and list all locally cached MassIVE 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 massive_id data_file
## 2 BFC2 MSV000080547 peak/Quant_assesment_QQQ/AG_spiked_sample11.mzML
## rpath
## 2 /github/home/.cache/R/BiocFileCache/AG_spiked_sample11.mzMLLocally cached files for a MassIVE data set can be removed using the
massive_delete_cache() function providing the ID of the
MassIVE data set for which local data files should be removed.
Next to the MsBackendMassIVE backend for
Spectra objects, the MsBackendMassIVE package
provides also various utility functions to query and retrieve
information from MassIVE or GNPS2’s datasetcache.
The massive_param_file() function reads the parameter
file from a MassIVE data set that provides general, experiment-specific
information. These are retrieved as a two-column data.frame
with the first column containing the names of the data set properties,
and the second their values.
## ParameterName
## 1 dataset.comments
## 2 dataset.instrument
## 3 dataset.keywords
## 4 dataset.modification
## 5 dataset.password
## 6 dataset.pi
## Value
## 1 Quantity assessment experiment for one sample two metabolomics workflow
## 2 MS:1001911;MS:1000644
## 3 Standards;quantity assesment
## 4 PRIDE:0000398
## 5 a
## 6 P Dorrestein|||The massive_number_files() function returns the number
of files in a MassIVE data set. By default, only MS data files (mzML,
CDF, mzXML) are counted, but this can be changed by providing a
different pattern to the pattern parameter.
## [1] 40The massive_download_file() function allows to download
any file of an experiment (directly, i.e., without caching). As an
example we download below a docx file to a temporary folder.
## [1] "ccms_parameters/params.xml"
## [2] "ccms_statistics/statistics.tsv"
## [3] "methods/README_Histones_P108_VS3.docx"
## [4] "other/Table 1 SAINT3788_TripleTOF.xlsx"
## [5] "other/Table 2 SAINT3788_TripleTOF.xlsx"
## [6] "other/Table 3 SAINT3788_TripleTOF.xlsx"MsBackendMassIVE provides also two utility functions to
query the GNPS2 datasetcache, gnps2_query() and
gnps2_usi_download_link().
Below we use gnps2_query() to retrieve all information
for a MassIVE data set from the datasetcache.
## usi
## 1 mzspec:MSV000083058:ccms_parameters/params.xml
## 2 mzspec:MSV000083058:ccms_statistics/statistics.tsv
## 3 mzspec:MSV000083058:methods/README_Histones_P108_VS3.docx
## 4 mzspec:MSV000083058:other/Table 1 SAINT3788_TripleTOF.xlsx
## 5 mzspec:MSV000083058:other/Table 2 SAINT3788_TripleTOF.xlsx
## 6 mzspec:MSV000083058:other/Table 3 SAINT3788_TripleTOF.xlsx
## filepath dataset collection is_update
## 1 ccms_parameters/params.xml MSV000083058 0
## 2 ccms_statistics/statistics.tsv MSV000083058 0
## 3 methods/README_Histones_P108_VS3.docx MSV000083058 0
## 4 other/Table 1 SAINT3788_TripleTOF.xlsx MSV000083058 0
## 5 other/Table 2 SAINT3788_TripleTOF.xlsx MSV000083058 0
## 6 other/Table 3 SAINT3788_TripleTOF.xlsx MSV000083058 0
## update_name create_time size size_mb sample_type spectra_ms1
## 1 NA 2024-04-11 02:34:07.431000 8619 0 MASSIVE 0
## 2 NA 2024-01-23 14:53:43.404000 0 0 MASSIVE 0
## 3 NA 2024-03-28 13:07:48.624000 31495 0 MASSIVE 0
## 4 NA 2024-04-23 08:04:41.282000 10783 0 MASSIVE 0
## 5 NA 2024-04-02 13:03:41.877000 931530 0 MASSIVE 0
## 6 NA 2024-03-28 13:07:48.754000 152547 0 MASSIVE 0
## spectra_ms2 instrument_vendor instrument_model file_processed
## 1 0 NA NA No
## 2 0 NA NA No
## 3 0 NA NA No
## 4 0 NA NA No
## 5 0 NA NA No
## 6 0 NA NA No
## filename
## 1 params.xml
## 2 statistics.tsv
## 3 README_Histones_P108_VS3.docx
## 4 Table 1 SAINT3788_TripleTOF.xlsx
## 5 Table 2 SAINT3788_TripleTOF.xlsx
## 6 Table 3 SAINT3788_TripleTOF.xlsxVia the gnps2_query() function, it is also possible to
compute the total size of data files in a MassIVE data set. To this end,
the filepath_pattern parameter can be used to restrict the
query to specific files.
files_info <- gnps2_query("MSV000083058", filepath_pattern = "mzML$")
size_gb <- round(sum(files_info$size)/(2^30), 2)
message("Total size of mzML files in data set MSV000083058: ", size_gb, " GB")## Total size of mzML files in data set MSV000083058: 3.48 GBThe gnps2_usi_download_link() returns a fully qualified
link to a data file (listed in the GNPS2 datasetcache), based on it’s
USI.
## [1] "https://massiveproxy.gnps2.org/massiveproxy/MSV000083058/other/Table%201%20SAINT3788_TripleTOF.xlsx"## 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] MsBackendMassIVE_0.99.1 Spectra_1.23.2 BiocParallel_1.47.0
## [4] S4Vectors_0.51.3 BiocGenerics_0.59.6 generics_0.1.4
## [7] BiocStyle_2.41.0
##
## loaded via a namespace (and not attached):
## [1] xfun_0.58 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_3.53.1 cluster_2.1.8.2 blob_1.3.0
## [13] pkgconfig_2.0.3 data.table_1.18.4 dbplyr_2.5.2
## [16] lifecycle_1.0.5 compiler_4.6.0 stringr_1.6.0
## [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] pillar_1.11.1 crayon_1.5.3 jquerylib_0.1.4
## [31] MASS_7.3-65 cachem_1.1.0 MetaboCoreUtils_1.21.1
## [34] tidyselect_1.2.1 rvest_1.0.5 digest_0.6.39
## [37] stringi_1.8.7 dplyr_1.2.1 purrr_1.2.2
## [40] maketools_1.3.2 fastmap_1.2.0 cli_3.6.6
## [43] magrittr_2.0.5 withr_3.0.2 prettyunits_1.2.0
## [46] filelock_1.0.3 rappdirs_0.3.4 bit64_4.8.2
## [49] rmarkdown_2.31 httr_1.4.8 bit_4.6.0
## [52] hms_1.1.4 memoise_2.0.1 evaluate_1.0.5
## [55] knitr_1.51 IRanges_2.47.2 BiocFileCache_3.3.0
## [58] rlang_1.2.0 Rcpp_1.1.1-1.1 glue_1.8.1
## [61] DBI_1.3.0 mzR_2.47.0 BiocManager_1.30.27
## [64] xml2_1.5.2 jsonlite_2.0.0 R6_2.6.1
## [67] fs_2.1.0 ProtGenerics_1.39.2 MsCoreUtils_1.25.4