Usage of Annotation Resources with the CompoundDb Package (original) (raw)
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🔞16.961849
Compiled: Tue Apr 15 17:53:01 2025
Introduction
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.
Installation
The package (including dependencies) can be installed with the code below:
install.packages("BiocManager")
BiocManager::install("CompoundDb")General usage
In this vignette we use a small CompDb database containing annotations for a small number of metabolites build usingMassBank release 2020.09. The respectiveCompDb 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 metadatafunction.
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 CompDbQuerying compound annotations
The 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 IdentifierInChI 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 CompDbdatabase.
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 bycompoundVariables(). 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 B2Additional functionality for CompDb databases
CompoundDb 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).
Accessing and using MS/MS data
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 theSpectra() 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 theCompDb 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 packagevignetteor a tutorial on how to perform MS/MS spectra matching with Spectra.
Similar to the compounds() function, a call to Spectra() will give access toall 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))Ion databases
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 IonDbdatabase, 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 theinsertIon() 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 178.063Session information
sessionInfo()## R version 4.5.0 RC (2025-04-04 r88126)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [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: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] RSQLite_2.3.9 Spectra_1.18.0 BiocParallel_1.42.0
## [4] CompoundDb_1.12.0 S4Vectors_0.46.0 BiocGenerics_0.54.0
## [7] generics_0.1.3 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 lifecycle_1.0.4 GenomeInfoDbData_1.2.14
## [19] compiler_4.5.0 munsell_0.5.1 codetools_0.2-20
## [22] clue_0.3-66 GenomeInfoDb_1.44.0 htmltools_0.5.8.1
## [25] sass_0.4.10 RCurl_1.98-1.17 yaml_2.3.10
## [28] lazyeval_0.2.2 pillar_1.10.2 jquerylib_0.1.4
## [31] MASS_7.3-65 DT_0.33 cachem_1.1.0
## [34] MetaboCoreUtils_1.16.0 tidyselect_1.2.1 digest_0.6.37
## [37] stringi_1.8.7 dplyr_1.1.4 bookdown_0.43
## [40] rsvg_2.6.2 fastmap_1.2.0 grid_4.5.0
## [43] colorspace_2.1-1 cli_3.6.4 magrittr_2.0.3
## [46] base64enc_0.1-3 ChemmineR_3.60.0 scales_1.3.0
## [49] UCSC.utils_1.4.0 bit64_4.6.0-1 rmarkdown_2.29
## [52] XVector_0.48.0 httr_1.4.7 bit_4.6.0
## [55] gridExtra_2.3 png_0.1-8 memoise_2.0.1
## [58] evaluate_1.0.3 knitr_1.50 GenomicRanges_1.60.0
## [61] IRanges_2.42.0 rlang_1.1.6 Rcpp_1.0.14
## [64] glue_1.8.0 DBI_1.2.3 xml2_1.3.8
## [67] BiocManager_1.30.25 jsonlite_2.0.0 R6_2.6.1
## [70] fs_1.6.6 ProtGenerics_1.40.0 MsCoreUtils_1.20.0References
Rainer, 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.