Authors: Jan Stanstrup [aut] (ORCID: https://orcid.org/0000-0003-0541-7369),
Johannes Rainer [aut, cre] (ORCID:
https://orcid.org/0000-0002-6977-7147),
Josep M. Badia [ctb] (ORCID: https://orcid.org/0000-0002-5704-1124),
Roger Gine [aut] (ORCID: https://orcid.org/0000-0003-0288-9619),
Andrea Vicini [aut] (ORCID: https://orcid.org/0000-0001-9438-6909),
Prateek Arora [ctb] (ORCID: https://orcid.org/0000-0003-0822-9240)
Last modified: 2025-04-15 15:18:16.961849
Compiled: Thu May 22 18:28:28 2025
The CompoundDb package provides the functionality to create
chemical compound databases from a variety of sources and to use such
annotation databases (CompDb) (Rainer et al. 2022). A detailed description
on the creation of annotation resources is given in the Creating CompoundDb
annotation resources vignette. This vignette focuses on how annotations can be
search for and retrieved.
The package (including dependencies) can be installed with the code below:
install.packages("BiocManager")
BiocManager::install("CompoundDb")In this vignette we use a small CompDb database containing annotations for a
small number of metabolites build using
MassBank release 2020.09. The respective
CompDb database which is loaded below contains in addition to general compound
annotations also MS/MS spectra for these compounds.
library(CompoundDb)
cdb <- CompDb(system.file("sql/CompDb.MassBank.sql", package = "CompoundDb"))
cdb## class: CompDb
## data source: MassBank
## version: 2020.09
## organism: NA
## compound count: 70
## MS/MS spectra count: 70General information about the database can be accessed with the metadata
function.
metadata(cdb)## name value
## 1 source MassBank
## 2 url https://massbank.eu/MassBank/
## 3 source_version 2020.09
## 4 source_date 1603272565
## 5 organism <NA>
## 6 db_creation_date Thu Oct 22 08:45:31 2020
## 7 supporting_package CompoundDb
## 8 supporting_object CompDbThe CompoundDb package is designed to provide annotation resources for small
molecules, such as metabolites, that are characterized by an exact mass and
additional information such as their IUPAC International Chemical Identifier
InChI or
their chemical formula. The available annotations (variables) for compounds
can differ between databases. The compoundVariables() function can be used to
retrieve a list of all available compound annotations for a specific CompDb
database.
compoundVariables(cdb)## [1] "formula" "exactmass" "smiles" "inchi" "inchikey" "cas"
## [7] "pubchem" "name"The actual compound annotations can then be extracted with the compounds()
function which returns by default all columns listed by
compoundVariables(). We can also define specific columns we want to extract
with the columns parameter.
head(compounds(cdb, columns = c("name", "formula", "exactmass")))## formula exactmass name
## 1 C10H10O3 178.0630 Mellein
## 2 C25H47NO9 505.3251 AAL toxin TB
## 3 C17H12O6 312.0634 Aflatoxin B1
## 4 C17H14O6 314.0790 Aflatoxin B2
## 5 C17H12O7 328.0583 Aflatoxin G1
## 6 C17H14O7 330.0739 Aflatoxin G2As a technical detail, CompDb databases follow a very simple database layout
with only few constraints to allow data import and representation for a variety
of sources (e.g. MassBank, HMDB, MoNa, ChEBI). For the present database, which
is based on MassBank, the mapping between entries in the ms_compound database
table and MS/MS spectra is for example 1:1 and the ms_compound table contains
thus highly redundant information. Thus, if we would include the column
"compound_id" in the query we would end up with redundant values:
head(compounds(cdb, columns = c("compound_id", "name", "formula")))## compound_id formula name
## 1 1 C10H10O3 Mellein
## 2 2 C10H10O3 Mellein
## 3 3 C10H10O3 Mellein
## 4 4 C10H10O3 Mellein
## 5 5 C10H10O3 Mellein
## 6 6 C25H47NO9 AAL toxin TBBy default, compounds() extracts the data for all compounds stored in the
database. The function supports however also filters to get values for
specific entries only. These can be defined as filter expressions which are
similar to the way how e.g. a data.frame would be subsetted in R. In the
example below we extract the compound ID, name and chemical formula for a
compound Mellein.
compounds(cdb, columns = c("compound_id", "name", "formula"),
filter = ~ name == "Mellein")## compound_id formula name
## 1 1 C10H10O3 Mellein
## 2 2 C10H10O3 Mellein
## 3 3 C10H10O3 Mellein
## 4 4 C10H10O3 Mellein
## 5 5 C10H10O3 MelleinNote that a filter expression always has to start with ~ followed by the
variable on which the data should be subsetted and the condition to select the
entries of interest. An overview of available filters for a CompDb can be
retrieved with the supportedFilter() function which returns the name of the
filter and the database column on which the filter selects the values:
supportedFilters(cdb)## filter field
## 1 CompoundIdFilter compound_id
## 2 ExactmassFilter exactmass
## 3 FormulaFilter formula
## 4 InchiFilter inchi
## 5 InchikeyFilter inchikey
## 8 MsmsMzRangeMaxFilter msms_mz_range_max
## 7 MsmsMzRangeMinFilter msms_mz_range_min
## 6 NameFilter name
## 9 SpectrumIdFilter spectrum_idAlso, filters can be combined to create more specific filters in the same manner
this would be done in R, i.e. using & for and, | for or and ! for
not. To illustrate this we extract below all compound entries from the table
for compounds with the name Mellein and that have a "compound_id" which is
either 1 or 5.
compounds(cdb, columns = c("compound_id", "name", "formula"),
filter = ~ name == "Mellein" & compound_id %in% c(1, 5))## compound_id formula name
## 1 1 C10H10O3 Mellein
## 2 5 C10H10O3 MelleinSimilarly, we can define a filter expression to retrieve compounds with an exact mass between 310 and 320.
compounds(cdb, columns = c("name", "exactmass"),
filter = ~ exactmass > 310 & exactmass < 320)## exactmass name
## 1 312.0634 Aflatoxin B1
## 2 314.0790 Aflatoxin B2In addition to filter expressions, we can also define and combine filters
using the actual filter classes. This provides additional conditions that would
not be possible with regular filter expressions. Below we fetch for examples
only compounds from the database that contain a H14 in their formula. To this
end we use a FormulaFilter with the condition "contains". Note that all
filters that base on character matching (i.e. FormulaFilter, InchiFilter,
InchikeyFilter, NameFilter) support as conditions also "contains",
"startsWith" and "endsWith" in addition to "=" and "!=".
compounds(cdb, columns = c("name", "formula", "exactmass"),
filter = FormulaFilter("H14", "contains"))## formula exactmass name
## 1 C17H14O6 314.0790 Aflatoxin B2
## 2 C17H14O7 330.0739 Aflatoxin G2It is also possible to combine filters if they are defined that way, even if it
is a little less straight forward than with the filter expressions. Below we
combine the FormulaFilter with the ExactmassFilter to retrieve only
compounds with an "H14" in their formula and an exact mass between 310 and
320.
filters <- AnnotationFilterList(
FormulaFilter("H14", "contains"),
ExactmassFilter(310, ">"),
ExactmassFilter(320, "<"),
logicOp = c("&", "&"))
compounds(cdb, columns = c("name", "formula", "exactmass"),
filter = filters)## formula exactmass name
## 1 C17H14O6 314.079 Aflatoxin B2CompDb databasesCompoundDb defines additional functions to work with CompDb databases. One
of them is the mass2mz() function that allows to directly calculate ion
(adduct) m/z values for exact (monoisotopic) masses of compounds in a
database. Below we use this function to calculate [M+H]+ and [M+Na]+ ions
for all unique chemical formulas in our example CompDb database.
mass2mz(cdb, adduct = c("[M+H]+", "[M+Na]+"))## [M+H]+ [M+Na]+
## C10H10O3 179.0703 201.0522
## C25H47NO9 506.3324 528.3143
## C17H12O6 313.0706 335.0526
## C17H14O6 315.0863 337.0682
## C17H12O7 329.0656 351.0475
## C17H14O7 331.0812 353.0632
## C20H20N2O3 337.1547 359.1366
## C15H16O6 293.1020 315.0839
## C14H10O5 259.0601 281.0420
## C15H12O5 273.0757 295.0577
## C16H16O8 337.0918 359.0737To get a matrix with adduct m/z values for discrete compounds (identified
by their InChIKey) we specify name = "inchikey".
mass2mz(cdb, adduct = c("[M+H]+", "[M+Na]+"), name = "inchikey")## [M+H]+ [M+Na]+
## KWILGNNWGSNMPA-UHFFFAOYSA-N 179.0703 201.0522
## CTXQVLLVFBNZKL-YVEDVMJTSA-N 506.3324 528.3143
## OQIQSTLJSLGHID-WNWIJWBNSA-N 313.0706 335.0526
## WWSYXEZEXMQWHT-WNWIJWBNSA-N 315.0863 337.0682
## XWIYFDMXXLINPU-WNWIJWBNSA-N 329.0656 351.0475
## WPCVRWVBBXIRMA-WNWIJWBNSA-N 331.0812 353.0632
## MJBWDEQAUQTVKK-IAGOWNOFSA-N 329.0656 351.0475
## SZINUGQCTHLQAZ-DQYPLSBCSA-N 337.1547 359.1366
## MMHTXEATDNFMMY-WBIUFABUSA-N 293.1020 315.0839
## CEBXXEKPIIDJHL-UHFFFAOYSA-N 259.0601 281.0420
## LCSDQFNUYFTXMT-UHFFFAOYSA-N 273.0757 295.0577
## VSMBLBOUQJNJIL-JJXSEGSLSA-N 337.0918 359.0737Alternatively we could also use name = "compound_id" to get a value for each
row in the compound database table, but for this example database this would
result in highly redundant information.
mass2mz() bases on the MetaboCoreUtils::mass2mz function and thus supports
all pre-defined adducts from that function. These are (for positive polarity):
MetaboCoreUtils::adductNames()## [1] "[M+3H]3+" "[M+2H+Na]3+" "[M+H+Na2]3+"
## [4] "[M+Na3]3+" "[M+2H]2+" "[M+H+NH4]2+"
## [7] "[M+H+K]2+" "[M+H+Na]2+" "[M+C2H3N+2H]2+"
## [10] "[M+2Na]2+" "[M+C4H6N2+2H]2+" "[M+C6H9N3+2H]2+"
## [13] "[M+H]+" "[M+Li]+" "[M+2Li-H]+"
## [16] "[M+NH4]+" "[M+H2O+H]+" "[M+Na]+"
## [19] "[M+CH4O+H]+" "[M+K]+" "[M+C2H3N+H]+"
## [22] "[M+2Na-H]+" "[M+C3H8O+H]+" "[M+C2H3N+Na]+"
## [25] "[M+2K-H]+" "[M+C2H6OS+H]+" "[M+C4H6N2+H]+"
## [28] "[2M+H]+" "[2M+NH4]+" "[2M+Na]+"
## [31] "[2M+K]+" "[2M+C2H3N+H]+" "[2M+C2H3N+Na]+"
## [34] "[3M+H]+" "[M+H-NH3]+" "[M+H-H2O]+"
## [37] "[M+H-Hexose-H2O]+" "[M+H-H4O2]+" "[M+H-CH2O2]+"
## [40] "[M]+"and for negative polarity:
MetaboCoreUtils::adductNames(polarity = "negative")## [1] "[M-3H]3-" "[M-2H]2-" "[M-H]-" "[M+Na-2H]-"
## [5] "[M+Cl]-" "[M+K-2H]-" "[M+C2H3N-H]-" "[M+CHO2]-"
## [9] "[M+C2H3O2]-" "[M+Br]-" "[M+C2F3O2]-" "[2M-H]-"
## [13] "[2M+CHO2]-" "[2M+C2H3O2]-" "[3M-H]-" "[M-H+HCOONa]-"
## [17] "[M]-"In addition, user-supplied adduct definitions are also supported (see the help
of mass2mz() in the MetaboCoreUtils package for details).
CompDb database can also store and provide MS/MS spectral data. These can be
accessed via a Spectra object from the Spectra
Bioconductor. Such a Spectra object for a CompDb can be created with the
Spectra() function as in the example below.
sps <- Spectra(cdb)
sps## MSn data (Spectra) with 70 spectra in a MsBackendCompDb backend:
## msLevel precursorMz polarity
## <integer> <numeric> <integer>
## 1 2 179.07 1
## 2 2 179.07 1
## 3 2 179.07 1
## 4 2 179.07 1
## 5 2 179.07 1
## ... ... ... ...
## 66 2 337.091 1
## 67 2 337.091 1
## 68 2 337.091 1
## 69 2 337.091 1
## 70 2 337.091 1
## ... 46 more variables/columns.
## Use 'spectraVariables' to list all of them.
## data source: MassBank
## version: 2020.09
## organism: NAThis Spectra object uses a MsBackendCompDb to represent the MS data of the
CompDb database. In fact, only the compound identifiers and the precursor m/z
values from all spectra are stored in memory while all other data is retrieved
on-the-fly from the database when needed.
The spectraVariables() function lists all available annotations for a spectrum
from the database, which includes also annotations of the associated compounds.
spectraVariables(sps)## [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" "compound_id"
## [19] "formula" "exactmass"
## [21] "smiles" "inchi"
## [23] "inchikey" "cas"
## [25] "pubchem" "name"
## [27] "accession" "spectrum_name"
## [29] "date" "authors"
## [31] "license" "copyright"
## [33] "publication" "splash"
## [35] "adduct" "ionization"
## [37] "ionization_voltage" "fragmentation_mode"
## [39] "collisionEnergy_text" "instrument"
## [41] "instrument_type" "precursorMz_text"
## [43] "spectrum_id" "predicted"
## [45] "msms_mz_range_min" "msms_mz_range_max"
## [47] "synonym"Individual variables can then be accessed with $ and the variable name:
head(sps$adduct)## [1] "[M+H]+" "[M+H]+" "[M+H]+" "[M+H]+" "[M+H]+" "[M+H]+"For more information on how to use Spectra objects in your analysis have also
a look at the package
vignette
or a tutorial on how to perform
MS/MS spectra matching with Spectra.
Similar to the compounds() function, a call to Spectra() will give access to
all spectra in the database. Using the same filtering framework it is
however also possible to extract only specific spectra from the
database. Below we are for example accessing only the MS/MS spectra of the
compound Mellein. Using the filter in the Spectra() call can be
substantially faster than first initializing a Spectra with the full data and
then subsetting that to selected spectra.
mellein <- Spectra(cdb, filter = ~ name == "Mellein")
mellein## MSn data (Spectra) with 5 spectra in a MsBackendCompDb backend:
## msLevel precursorMz polarity
## <integer> <numeric> <integer>
## 1 2 179.07 1
## 2 2 179.07 1
## 3 2 179.07 1
## 4 2 179.07 1
## 5 2 179.07 1
## ... 46 more variables/columns.
## Use 'spectraVariables' to list all of them.
## data source: MassBank
## version: 2020.09
## organism: NAInstead of all spectra we extracted now only a subset of 5 spectra from the database.
As a simple toy example we perform next pairwise spectra comparison between the 5 spectra from Mellein with all the MS/MS spectra in the database.
library(Spectra)
cormat <- compareSpectra(mellein, sps, ppm = 40)Note that the MsBackendCompDb does not support parallel processing, thus,
while compareSpectra() would in general support parallel processing, it gets
automatically be disabled if a Spectra with a MsBackendCompDb is used.
cormat <- compareSpectra(mellein, sps, ppm = 40, BPPARAM = MulticoreParam(2))## Warning in MulticoreParam(2): MulticoreParam() not supported on Windows, use
## SnowParam()The CompDb database layout is designed to provide compound annotations, but in
mass spectrometry (MS) ions are measured. These ions are generated e.g. by
electro spray ionization (ESI) from the original compounds in a sample. They are
characterized by their specific mass-to-charge ratio (m/z) which is measured by
the MS instrument. Eventually, also a retention time is available. Also, for the
same compound several different ions (adducts) can be formed and measured, all
with a different m/z. This type of data can be represented by an IonDb
database, which extends the CompDb and hence inherits all of its properties
but adds additional database tables to support also ion annotations. Also,
IonDb objects provide functionality to add new ion annotations to an existing
database. Thus, this type of database can be used to build lab-internal
annotation resources containing ions, m/z and retention times for pure standards
measured on a specific e.g. LC-MS setup.
CompDb databases, such as the cdb from this example, are however by default
read-only, thus, we below create a new database connection, copy the content
of the cdb to that database and convert the CompDb to an IonDb.
library(RSQLite)
## Create a temporary database
con <- dbConnect(SQLite(), tempfile())
## Create an IonDb copying the content of cdb to the new database
idb <- IonDb(con, cdb)
idb## class: IonDb
## data source: MassBank
## version: 2020.09
## organism: NA
## compound count: 70
## MS/MS spectra count: 70
## ion count: 0The IonDb defines an additional function ions that allows to retrieve ion
information from the database.
ions(idb)## [1] compound_id ion_adduct ion_mz ion_rt
## <0 rows> (or 0-length row.names)The present database does not yet contain any ion information. Below we define a
data frame with ion annotations and add that to the database with the
insertIon() function. The column "compound_id" needs to contain the
identifiers of the compounds to which the ion should be related to. In the
present example we add 2 different ions for the compound with the ID 1
(Mellein). Note that the specified m/z values as well as the retention times
are completely arbitrary.
ion <- data.frame(compound_id = c(1, 1),
ion_adduct = c("[M+H]+", "[M+Na]+"),
ion_mz = c(123.34, 125.34),
ion_rt = c(196, 196))
idb <- insertIon(idb, ion)These ions have now be added to the database.
ions(idb)## compound_id ion_adduct ion_mz ion_rt
## 1 1 [M+H]+ 123.34 196
## 2 1 [M+Na]+ 125.34 196Ions can also be deleted from a database with the deleteIon function (see the
respective help page for more information).
Note that we can also retrieve compound annotation information for the ions. Below we extract the associated compound name and its exact mass.
ions(idb, columns = c("ion_adduct", "name", "exactmass"))## ion_adduct name exactmass
## 1 [M+H]+ Mellein 178.063
## 2 [M+Na]+ Mellein 178.063sessionInfo()## R version 4.5.0 RC (2025-04-04 r88126 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
## LAPACK version 3.12.1
##
## locale:
## [1] LC_COLLATE=C
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] RSQLite_2.3.11 Spectra_1.18.2 BiocParallel_1.42.0
## [4] CompoundDb_1.12.1 S4Vectors_0.46.0 BiocGenerics_0.54.0
## [7] generics_0.1.4 AnnotationFilter_1.32.0 BiocStyle_2.36.0
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 rjson_0.2.23 xfun_0.52
## [4] bslib_0.9.0 ggplot2_3.5.2 htmlwidgets_1.6.4
## [7] Biobase_2.68.0 vctrs_0.6.5 tools_4.5.0
## [10] bitops_1.0-9 parallel_4.5.0 tibble_3.2.1
## [13] blob_1.2.4 cluster_2.1.8.1 pkgconfig_2.0.3
## [16] dbplyr_2.5.0 RColorBrewer_1.1-3 lifecycle_1.0.4
## [19] GenomeInfoDbData_1.2.14 compiler_4.5.0 farver_2.1.2
## [22] codetools_0.2-20 clue_0.3-66 GenomeInfoDb_1.44.0
## [25] htmltools_0.5.8.1 sass_0.4.10 RCurl_1.98-1.17
## [28] yaml_2.3.10 lazyeval_0.2.2 pillar_1.10.2
## [31] jquerylib_0.1.4 MASS_7.3-65 DT_0.33
## [34] cachem_1.1.0 MetaboCoreUtils_1.16.1 tidyselect_1.2.1
## [37] digest_0.6.37 stringi_1.8.7 dplyr_1.1.4
## [40] bookdown_0.43 rsvg_2.6.2 fastmap_1.2.0
## [43] grid_4.5.0 cli_3.6.5 magrittr_2.0.3
## [46] base64enc_0.1-3 dichromat_2.0-0.1 ChemmineR_3.60.0
## [49] scales_1.4.0 UCSC.utils_1.4.0 bit64_4.6.0-1
## [52] rmarkdown_2.29 XVector_0.48.0 httr_1.4.7
## [55] bit_4.6.0 gridExtra_2.3 png_0.1-8
## [58] memoise_2.0.1 evaluate_1.0.3 knitr_1.50
## [61] GenomicRanges_1.60.0 IRanges_2.42.0 rlang_1.1.6
## [64] Rcpp_1.0.14 glue_1.8.0 DBI_1.2.3
## [67] xml2_1.3.8 BiocManager_1.30.25 jsonlite_2.0.0
## [70] R6_2.6.1 fs_1.6.6 ProtGenerics_1.40.0
## [73] MsCoreUtils_1.20.0Rainer, Johannes, Andrea Vicini, Liesa Salzer, Jan Stanstrup, Josep M. Badia, Steffen Neumann, Michael A. Stravs, et al. 2022. “A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R.” Metabolites 12 (2): 173. https://doi.org/10.3390/metabo12020173.