Usage of Annotation Resources with the CompoundDb Package
Source:vignettes/CompoundDb-usage.Rmd
CompoundDb-usage.RmdAuthors: Jan Stanstrup [aut] (https://orcid.org/0000-0003-0541-7369), Johannes Rainer
[aut, cre] (https://orcid.org/0000-0002-6977-7147), Josep M. Badia
[ctb] (https://orcid.org/0000-0002-5704-1124), Roger Gine [aut]
(https://orcid.org/0000-0003-0288-9619), Andrea Vicini
[aut] (https://orcid.org/0000-0001-9438-6909), Prateek Arora
[ctb] (https://orcid.org/0000-0003-0822-9240)
Last modified: 2024-09-27 12:02:39.449229
Compiled: Fri Sep 27 12:10:07 2024
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 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 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 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.
## 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:
## 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.
## [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".
## [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 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))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
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.
## ion_adduct name exactmass
## 1 [M+H]+ Mellein 178.063
## 2 [M+Na]+ Mellein 178.063Session information
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 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-r0.3.20.so; LAPACK version 3.10.0
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## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
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## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
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## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
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## time zone: UTC
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##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] RSQLite_2.3.7 Spectra_1.15.10 BiocParallel_1.39.0
## [4] CompoundDb_1.9.5 S4Vectors_0.43.2 BiocGenerics_0.51.1
## [7] AnnotationFilter_1.29.0 BiocStyle_2.33.1
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.5 rjson_0.2.23 xfun_0.47
## [4] bslib_0.8.0 ggplot2_3.5.1 htmlwidgets_1.6.4
## [7] Biobase_2.65.1 generics_0.1.3 vctrs_0.6.5
## [10] tools_4.4.1 bitops_1.0-8 parallel_4.4.1
## [13] tibble_3.2.1 fansi_1.0.6 blob_1.2.4
## [16] cluster_2.1.6 pkgconfig_2.0.3 dbplyr_2.5.0
## [19] desc_1.4.3 lifecycle_1.0.4 GenomeInfoDbData_1.2.12
## [22] compiler_4.4.1 textshaping_0.4.0 munsell_0.5.1
## [25] codetools_0.2-20 clue_0.3-65 GenomeInfoDb_1.41.1
## [28] htmltools_0.5.8.1 sass_0.4.9 RCurl_1.98-1.16
## [31] yaml_2.3.10 lazyeval_0.2.2 pkgdown_2.1.1.9000
## [34] pillar_1.9.0 jquerylib_0.1.4 MASS_7.3-61
## [37] DT_0.33 cachem_1.1.0 MetaboCoreUtils_1.13.0
## [40] tidyselect_1.2.1 digest_0.6.37 dplyr_1.1.4
## [43] bookdown_0.40 rsvg_2.6.1 fastmap_1.2.0
## [46] grid_4.4.1 colorspace_2.1-1 cli_3.6.3
## [49] magrittr_2.0.3 base64enc_0.1-3 utf8_1.2.4
## [52] ChemmineR_3.57.0 UCSC.utils_1.1.0 scales_1.3.0
## [55] bit64_4.5.2 rmarkdown_2.28 XVector_0.45.0
## [58] httr_1.4.7 bit_4.5.0 gridExtra_2.3
## [61] png_0.1-8 ragg_1.3.3 memoise_2.0.1
## [64] evaluate_1.0.0 knitr_1.48 GenomicRanges_1.57.1
## [67] IRanges_2.39.2 rlang_1.1.4 Rcpp_1.0.13
## [70] glue_1.7.0 DBI_1.2.3 xml2_1.3.6
## [73] BiocManager_1.30.25 jsonlite_1.8.9 R6_2.5.1
## [76] ProtGenerics_1.37.1 MsCoreUtils_1.17.2 systemfonts_1.1.0
## [79] fs_1.6.4 zlibbioc_1.51.1