Authors: Johannes Rainer
Modified: 2022-05-30 14:08:00
Compiled: Sun Sep 18 16:08:12 2022

1 Introduction

Chemical compound annotation and information can be retrieved from a variety of sources including HMDB, LipidMaps or ChEBI. The CompoundDb package provides functionality to extract data relevant for (chromatographic) peak annotations in metabolomics/lipidomics experiments from these sources and to store it into a common format (i.e. an CompDb object/database). This vignette describes how such CompDb objects can be created exemplified with package-internal test files that represent data subsets from some annotation resources.

The R object to represent the compound annotation is the CompDb object. Each object (respectively its database) is supposed to contain and provide annotations from a single source (e.g. HMDB or LipidMaps) but it is also possible to create cross-source databases too.

2 Creating CompDb databases

CompDb databases can be created from existing data resources such as the Human Metabolome Database (HMDB) by importing all of their data or can alternatively be build by sequentially adding data and information to the database. This section explains first how data can be loaded from existing resources to create a CompDb database and in the last subsection how an empty CompDb can be sequentially and manually filled with annotation data 2.4.

The CompoundDb package provides the compound_tbl_sdf and the compound_tbl_lipidblast functions to extract relevant compound annotation from files in SDF (structure-data file) format or in the json files from LipidBlast (http://mona.fiehnlab.ucdavis.edu/downloads). CompoundDb allows to process SDF files from:

Note however that it is also possible to define such a table manually and use that to create the database. As simple example for this is provided in the section CompDb from custom input 2.2 below or the help page of createCompDb for more details on that.

2.1 CompDb from HMDB data

Below we use the compound_tbl_sdf to extract compound annotations from a SDF file representing a very small subset of the HMDB database. To generate a database for the full HMDB we would have to download the structures.sdf file containing all metabolites and load that file instead.

library(CompoundDb)

## Locate the file
hmdb_file <- system.file("sdf/HMDB_sub.sdf.gz", package = "CompoundDb")
## Extract the data
cmps <- compound_tbl_sdf(hmdb_file)

The function returns by default a (data.frame-equivalent) tibble (from the tidyverse’s tibble package).

cmps
## # A tibble: 9 × 8
##   compound_id name                  inchi inchi…¹ formula exact…² synon…³ smiles
##   <chr>       <chr>                 <chr> <chr>   <chr>     <dbl> <named> <chr> 
## 1 HMDB0000001 1-Methylhistidine     InCh… BRMWTN… C7H11N…   169.  <chr>   "CN1C…
## 2 HMDB0000002 1,3-Diaminopropane    InCh… XFNJVJ… C3H10N2    74.1 <chr>   "NCCC…
## 3 HMDB0000005 2-Ketobutyric acid    InCh… TYEYBO… C4H6O3    102.  <chr>   "CCC(…
## 4 HMDB0000008 2-Hydroxybutyric acid InCh… AFENDN… C4H8O3    104.  <chr>   "CCC(…
## 5 HMDB0000010 2-Methoxyestrone      InCh… WHEUWN… C19H24…   300.  <chr>   "[H][…
## 6 HMDB0000011 (R)-3-Hydroxybutyric… InCh… WHBMMW… C4H8O3    104.  <chr>   "C[C@…
## 7 HMDB0000012 Deoxyuridine          InCh… MXHRCP… C9H12N…   228.  <chr>   "OC[C…
## 8 HMDB0004370 N-Methyltryptamine    InCh… NCIKQJ… C11H14…   174.  <chr>   "CNCC…
## 9 HMDB0006719 5,6-trans-Vitamin D3  InCh… QYSXJU… C27H44O   384.  <chr>   "CC(C…
## # … with abbreviated variable names ¹​inchikey, ²​exactmass, ³​synonyms

The tibble contains columns

  • compound_id: the resource-specific ID of the compound. Can be an integer or a character.
  • name: the name of the compound, mostly a generic or common name.
  • inchi: the compound’s inchi.
  • inchikey: the INCHI key.
  • formula: the chemical formula of the compound.
  • exactmass: the compounds (monoisotopic) mass.
  • synonyms: a list of aliases/synonyms for the compound.
  • smiles: the SMILES of the compound.

To create a simple compound database, we could pass this tibble along with additional required metadata information to the createCompDb function. In the present example we want to add however also MS/MS spectrum data to the database. We thus load below the MS/MS spectra for some of the compounds from the respective xml files downloaded from HMDB. To this end we pass the path to the folder in which the files are located to the msms_spectra_hmdb function. The function identifies the xml files containing MS/MS spectra based on their their file name and loads the respective spectrum data. The folder can therefore also contain other files, but the xml files from HMDB should not be renamed or the function will not recognice them. Note also that at present only MS/MS spectrum xml files from HMDB are supported (one xml file per spectrum); these could be downloaded from HMDB with the hmdb_all_spectra.zip file.

## Locate the folder with the xml files
xml_path <- system.file("xml", package = "CompoundDb")
spctra <- msms_spectra_hmdb(xml_path)

Also here, spectra information can be manually provided by adhering to the expected structure of the data.frame (see ?createCompDb for details).

At last we have to create the metadata for the resource. The metadata information for a CompDb resource is crucial as it defines the origin of the annotations and its version. This information should thus be carefully defined by the user. Below we use the make_metadata helper function to create a data.frame in the expected format. The organism should be provided in the format e.g. "Hsapiens" for human or "Mmusculus" for mouse, i.e. capital first letter followed by lower case characters without whitespaces.

metad <- make_metadata(source = "HMDB", url = "http://www.hmdb.ca",
                       source_version = "4.0", source_date = "2017-09",
                       organism = "Hsapiens")

With all the required data ready we create the SQLite database for the HMDB subset. With path we specify the path to the directory in which we want to save the database. This defaults to the current working directory, but for this example we save the database into a temporary folder.

db_file <- createCompDb(cmps, metadata = metad, msms_spectra = spctra,
                        path = tempdir())

The variable db_file is now the file name of the SQLite database. We can pass this file name to the CompDb function to get the CompDb objects acting as the interface to the database.

cmpdb <- CompDb(db_file)
cmpdb
## class: CompDb 
##  data source: HMDB 
##  version: 4.0 
##  organism: Hsapiens 
##  compound count: 9 
##  MS/MS spectra count: 4

To extract all compounds from the database we can use the compounds function. The parameter columns allows to choose the database columns to return. Any columns for any of the database tables are supported. To get an overview of available database tables and their columns, the tables function can be used:

tables(cmpdb)
## $ms_compound
## [1] "compound_id" "name"        "inchi"       "inchikey"    "formula"    
## [6] "exactmass"   "smiles"     
## 
## $msms_spectrum
##  [1] "original_spectrum_id" "compound_id"          "polarity"            
##  [4] "collision_energy"     "predicted"            "splash"              
##  [7] "instrument_type"      "instrument"           "precursor_mz"        
## [10] "spectrum_id"          "msms_mz_range_min"    "msms_mz_range_max"   
## 
## $msms_spectrum_peak
## [1] "spectrum_id" "mz"          "intensity"   "peak_id"    
## 
## $synonym
## [1] "compound_id" "synonym"

Below we extract only selected columns from the compounds table.

compounds(cmpdb, columns = c("name", "formula", "exactmass"))
##                        name   formula exactmass
## 1 (R)-3-Hydroxybutyric acid    C4H8O3  104.0473
## 2        1,3-Diaminopropane   C3H10N2   74.0844
## 3         1-Methylhistidine C7H11N3O2  169.0851
## 4     2-Hydroxybutyric acid    C4H8O3  104.0473
## 5        2-Ketobutyric acid    C4H6O3  102.0317
## 6          2-Methoxyestrone  C19H24O3  300.1725
## 7      5,6-trans-Vitamin D3   C27H44O  384.3392
## 8              Deoxyuridine C9H12N2O5  228.0746
## 9        N-Methyltryptamine  C11H14N2  174.1157

Analogously we can use the Spectra function to extract spectrum data from the database. The function returns by default a Spectra object from the Spectra package with all spectra metadata available as spectra variables.

library(Spectra)
sps <- Spectra(cmpdb)
sps
## MSn data (Spectra) with 4 spectra in a MsBackendCompDb backend:
##     msLevel precursorMz  polarity
##   <integer>   <numeric> <integer>
## 1        NA          NA         1
## 2        NA          NA         1
## 3        NA          NA         1
## 4        NA          NA         0
##  ... 32 more variables/columns.
##  Use  'spectraVariables' to list all of them.
##  data source: HMDB 
##  version: 4.0 
##  organism: Hsapiens

The available spectra variables for the Spectra object can be retrieved with spectraVariables:

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] "name"                    "inchi"                  
## [21] "inchikey"                "formula"                
## [23] "exactmass"               "smiles"                 
## [25] "original_spectrum_id"    "predicted"              
## [27] "splash"                  "instrument_type"        
## [29] "instrument"              "spectrum_id"            
## [31] "msms_mz_range_min"       "msms_mz_range_max"      
## [33] "synonym"

Individual spectra variables can be accessed with the $ operator:

sps$collisionEnergy
## [1] 10 25 NA 20

And the actual m/z and intensity values with mz and intensity:

mz(sps)
## NumericList of length 4
## [[1]] 109.2 124.2 124.5 170.16 170.52
## [[2]] 83.1 96.12 97.14 109.14 124.08 125.1 170.16
## [[3]] 44.1 57.9 61.4 71.2 73.8 78.3 78.8 ... 142.9 144.1 157.6 158 175.2 193.2
## [[4]] 111.0815386 249.2587746 273.2587746 ... 367.3006394 383.3319396
## m/z of the 2nd spectrum
mz(sps)[[2]]
## [1]  83.10  96.12  97.14 109.14 124.08 125.10 170.16

Note that it is also possible to retrieve specific spectra, e.g. for a provided compound, or add compound annotations to the Spectra object. Below we use the filter expression ~ compound_id == "HMDB0000001"to get only MS/MS spectra for the specified compound. In addition we ask for the "name" and "inchikey" of the compound.

sps <- Spectra(cmpdb, filter = ~ compound_id == "HMDB0000001",
               columns = c(tables(cmpdb)$msms_spectrum, "name",
                           "inchikey"))
sps
## MSn data (Spectra) with 2 spectra in a MsBackendCompDb backend:
##     msLevel precursorMz  polarity
##   <integer>   <numeric> <integer>
## 1        NA          NA         1
## 2        NA          NA         1
##  ... 32 more variables/columns.
##  Use  'spectraVariables' to list all of them.
##  data source: HMDB 
##  version: 4.0 
##  organism: Hsapiens

The available spectra variables:

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] "name"                    "inchi"                  
## [21] "inchikey"                "formula"                
## [23] "exactmass"               "smiles"                 
## [25] "original_spectrum_id"    "predicted"              
## [27] "splash"                  "instrument_type"        
## [29] "instrument"              "spectrum_id"            
## [31] "msms_mz_range_min"       "msms_mz_range_max"      
## [33] "synonym"

The compound’s name and INCHI key have thus also been added as spectra variables:

sps$inchikey
## [1] "BRMWTNUJHUMWMS-LURJTMIESA-N" "BRMWTNUJHUMWMS-LURJTMIESA-N"

To share or archive the such created CompDb database, we can also create a dedicated R package containing the annotation. To enable reproducible research, each CompDb package should contain the version of the originating data source in its file name (which is by default extracted from the metadata of the resource). Below we create a CompDb package from the generated database file. Required additional information we have to provide to the function are the package creator/maintainer and its version.

createCompDbPackage(
    db_file, version = "0.0.1", author = "J Rainer", path = tempdir(),
    maintainer = "Johannes Rainer <johannes.rainer@eurac.edu>")
## Creating package in /tmp/RtmpeEqdcQ/CompDb.Hsapiens.HMDB.4.0

The function creates a folder (in our case in a temporary directory) that can be build and installed with R CMD build and R CMD INSTALL.

Special care should also be put on the license of the package that can be passed with the license parameter. The license of the package and how and if the package can be distributed will depend also on the license of the originating resource.

2.2 CompDb from custom data

A CompDb database can also be created from custom, manually defined annotations. To illustrate this we create below first a data.frame with some arbitrary compound annotations. According to the ?createCompDb help page, the data frame needs to have columns "compound_id", "name", "inchi", "inchikey", "formula", "exactmass", "synonyms". All columns except "compound_id" can also contain missing values. It is also possible to define additional columns. Below we thus create a data.frame with some compound annotations as well as additional columns. Note that all these annotations in this example are for illustration purposes only and are by no means real. Also, we don’t provide any information for columns "inchi", "inchikey" and "formula" setting all values for these to NA.

cmps <- data.frame(
    compound_id = c("CP_0001", "CP_0002", "CP_0003", "CP_0004"),
    name = c("A", "B", "C", "D"),
    inchi = NA_character_,
    inchikey = NA_character_,
    formula = NA_character_,
    exactmass = c(123.4, 234.5, 345.6, 456.7),
    compound_group = c("G01", "G02", "G01", "G03")
)

Next we add also synonyms for each compound. This columns supports multiple values for each row.

cmps$synonyms <- list(
    c("a", "AA", "aaa"),
    c(),
    c("C", "c"),
    ("d")
)

We also need to define the metadata for our database, which we do with the make_metadata function. With this information we can already create a first rudimentary CompDb database that contains only compound annotations. We thus create below our custom CompDb database in a temporary directory. We also manually specify the name of our database with the dbFile parameter - if not provided, the name of the database will be constructed based on information from the metadata parameter. In a real-case scenario, path and dbFile should be changed to something more meaningful.

metad <- make_metadata(source = "manually defined", url = "",
                       source_version = "1.0.0", source_date = "2022-03-01",
                       organism = NA_character_)

db_file <- createCompDb(cmps, metadata = metad, path = tempdir(),
                        dbFile = "CompDb.test.sqlite")

We can now load this toy database using the CompDb function providing the full path to the database file. Note that we load the database in read-write mode by specifying flags = RSQLite::SQLITE_RW - by default CompDb will load databases in read-only mode hence ensuring that the data within the database can not be compromised. In our case we would however like to add more information to this database later and hence we load it in read-write mode.

cdb <- CompDb(db_file, flags = RSQLite::SQLITE_RW)
cdb
## class: CompDb 
##  data source: manually defined 
##  version: 1.0.0 
##  organism: NA 
##  compound count: 4

We can now retrieve annotations from the database with the compound function.

compounds(cdb)
##   name inchi inchikey formula exactmass compound_group
## 1    A  <NA>     <NA>    <NA>     123.4            G01
## 2    B  <NA>     <NA>    <NA>     234.5            G02
## 3    C  <NA>     <NA>    <NA>     345.6            G01
## 4    D  <NA>     <NA>    <NA>     456.7            G03

Or also search and filter the annotations.

compounds(cdb, filter = ~ name %in% c("B", "A"))
##   name inchi inchikey formula exactmass compound_group
## 1    A  <NA>     <NA>    <NA>     123.4            G01
## 2    B  <NA>     <NA>    <NA>     234.5            G02

Next we would like to add also MS2 spectra data to the database. This could be either done directly in the createCompDb call with parameter msms_spectra, or with the insertSpectra function that allows to add MS2 spectra data to an existing CompDb which can be provided as a Spectra object. We thus below manually create a Spectra object with some arbitrary MS2 spectra - alternatively, Spectra can be imported from a variety of input sources, including MGF or MSP files using e.g. the MsBackendMgf or MsBackendMsp packages.

#' Define basic spectra variables
df <- DataFrame(msLevel = 2L, precursorMz = c(124.4, 124.4, 235.5))
#' Add m/z and intensity information for each spectrum
df$mz <- list(
    c(3, 20, 59.1),
    c(2, 10, 30, 59.1),
    c(100, 206, 321.1))
df$intensity <- list(
    c(10, 13, 45),
    c(5, 8, 9, 43),
    c(10, 20, 400))
#' Create the Spectra object
sps <- Spectra(df)

The Spectra object needs also to have a variable (column) called "compound_id" which provides the information with which existing compound in the database the spectrum is associated.

compounds(cdb, "compound_id")
##   compound_id
## 1     CP_0001
## 2     CP_0002
## 3     CP_0003
## 4     CP_0004
sps$compound_id <- c("CP_0001", "CP_0001", "CP_0002")

We can also add additional information to the spectra, such as the instrument.

sps$instrument <- "AB Sciex TripleTOF 5600+"

And we can now add these spectra to our existing toy CompDb. Parameter columns allows to specify which of the spectra variables should be stored into the database.

cdb <- insertSpectra(cdb, spectra = sps,
                     columns = c("compound_id", "msLevel",
                                 "precursorMz", "instrument"))
cdb
## class: CompDb 
##  data source: manually defined 
##  version: 1.0.0 
##  organism: NA 
##  compound count: 4 
##  MS/MS spectra count: 3

We have thus now a CompDb database with compound annotations and 3 MS2 spectra. We could for example also retrieve the MS2 spectra for the compound with the name A from the database with:

Spectra(cdb, filter = ~ name == "A")
## MSn data (Spectra) with 2 spectra in a MsBackendCompDb backend:
##     msLevel precursorMz  polarity
##   <integer>   <numeric> <integer>
## 1         2       124.4        NA
## 2         2       124.4        NA
##  ... 31 more variables/columns.
##  Use  'spectraVariables' to list all of them.
##  data source: manually defined 
##  version: 1.0.0 
##  organism: NA

2.3 CompDb from MoNA data

MoNa (Massbank of North America) provides a large SDF file with all spectra which can be used to create a CompDb object with compound information and MS/MS spectra. Note however that MoNa is organized by spectra and the annotation of the compounds is not consistent and normalized. Spectra from the same compound can have their own compound identified and data that e.g. can differ in their chemical formula, precision of their exact mass or other fields.

Similar to the example above, compound annotations can be imported with the compound_tbl_sdf function while spectrum data can be imported with msms_spectra_mona. In the example below we use however the import_mona_sdf that wraps both functions to reads both compound and spectrum data from a SDF file without having to import the file twice. As an example we use a small subset from a MoNa SDF file that contains only 7 spectra.

mona_sub <- system.file("sdf/MoNa_export-All_Spectra_sub.sdf.gz",
                        package = "CompoundDb")
mona_data <- import_mona_sdf(mona_sub)
## Warning: MoNa data can currently not be normalized and the compound table
## contains thus highly redundant data.

As a result we get a list with a data.frame each for compound and spectrum information. These can be passed along to the createCompDb function to create the database (see below).

metad <- make_metadata(source = "MoNa",
                       url = "http://mona.fiehnlab.ucdavis.edu/",
                       source_version = "2018.11", source_date = "2018-11",
                       organism = "Unspecified")
mona_db_file <- createCompDb(mona_data$compound, metadata = metad,
                             msms_spectra = mona_data$msms_spectrum,
                             path = tempdir())

We can now load and use this database, e.g. by extracting all compounds as shown below.

mona <- CompDb(mona_db_file)
compounds(mona)
##                   name
## 1 Sulfachlorpyridazine
## 2         Sulfaclozine
## 3        Sulfadimidine
## 4       Sulfamethazine
## 5       Sulfamethazine
##                                                                                                      inchi
## 1             InChI=1S/C10H9ClN4O2S/c11-9-5-6-10(14-13-9)15-18(16,17)8-3-1-7(12)2-4-8/h1-6H,12H2,(H,14,15)
## 2             InChI=1S/C10H9ClN4O2S/c11-9-5-13-6-10(14-9)15-18(16,17)8-3-1-7(12)2-4-8/h1-6H,12H2,(H,14,15)
## 3 InChI=1S/C12H14N4O2S/c1-8-7-9(2)15-12(14-8)16-19(17,18)11-5-3-10(13)4-6-11/h3-7H,13H2,1-2H3,(H,14,15,16)
## 4 InChI=1S/C12H14N4O2S/c1-8-7-9(2)15-12(14-8)16-19(17,18)11-5-3-10(13)4-6-11/h3-7H,13H2,1-2H3,(H,14,15,16)
## 5 InChI=1S/C12H14N4O2S/c1-8-7-9(2)15-12(14-8)16-19(17,18)11-5-3-10(13)4-6-11/h3-7H,13H2,1-2H3,(H,14,15,16)
##                      inchikey      formula exactmass smiles
## 1 XOXHILFPRYWFOD-UHFFFAOYSA-N C10H9ClN4O2S  284.0135   <NA>
## 2 QKLPUVXBJHRFQZ-UHFFFAOYSA-N C10H9ClN4O2S  284.0135   <NA>
## 3 ASWVTGNCAZCNNR-UHFFFAOYSA-N  C12H14N4O2S  278.0837   <NA>
## 4 ASWVTGNCAZCNNR-UHFFFAOYSA-N  C12H14N4O2S  278.0837   <NA>
## 5 ASWVTGNCAZCNNR-UHFFFAOYSA-N  C12H14N4O2S  278.0837   <NA>

As stated in the introduction of this section the compound information contains redundant information and the table has essentially one row per spectrum. Feedback on how to reduce the redundancy in the ms_compound table is highly appreciated.

2.4 CompDb by sequentially filling with data

As an alternative to creating a full database from an existing resource it is also possible to create an empty CompDb database and sequentially filling it with data. This could for example be used to create a laboratory specific annotation library with compound, ion and fragment spectra of pure standards measured on a certain LC-MS setup. Below we create an empty CompDb database providing the file name of the database. In the example we store the database to a temporary file but in a real use case a meaningful file name and file path should be used instead.

dbfile <- tempfile()
mydb <- emptyCompDb(dbfile)
mydb
## class: CompDb 
##  data source: NA 
##  version: NA 
##  organism: NA 
##  compound count: 0

We next define some first compound annotation we want to add to the database. For compound annotations, fields "compound_id" (an arbitrary ID of the compound), "name" (the compound name), "inchi", "inchikey", "formula" (the chemical formula) and "exactmass" (the monoisotopic mass) are expected, but, except of "compound_id", they can also contain missing values or be completely omitted. Below we define a data.frame with annotations for some compounds and add this annotation to the database using the insertCompound function.

cmp <- data.frame(compound_id = c("1", "2"),
                  name = c("Caffeine", "Glucose"),
                  formula = c("C8H10N4O2", "C6H12O6"),
                  exactmass = c(194.080375584, 180.063388116))
mydb <- insertCompound(mydb, cmp)
mydb
## class: CompDb 
##  data source: NA 
##  version: NA 
##  organism: NA 
##  compound count: 2

We next add fragment spectra for the compounds. These could for example represent MS2 spectra measured for the pure standard of the compound and could be extracted for example from xcms result objects or other sources. Below we load some fragment spectra for caffeine from an MGF file distributed with this package. We use the MsBackendMgf package to import that data into a Spectra object.

library(MsBackendMgf)
caf_ms2 <- Spectra(system.file("mgf", "caffeine.mgf", package = "CompoundDb"),
                   source = MsBackendMgf())
caf_ms2
## MSn data (Spectra) with 2 spectra in a MsBackendMgf backend:
##     msLevel     rtime scanIndex
##   <integer> <numeric> <integer>
## 1         2        NA        NA
## 2         2        NA        NA
##  ... 25 more variables/columns.

We can evaluate what spectra variables are available in the imported data.

spectraVariables(caf_ms2)
##  [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"  "TITLE"                  
## [19] "original_spectrum_id"    "compound_id"            
## [21] "collision_energy"        "predicted"              
## [23] "splash"                  "spectrum_id"            
## [25] "msms_mz_range_min"       "msms_mz_range_max"
caf_ms2$rtime
## [1] NA NA

There are many variables available, but most of them, like for example the retention time, or not defined as this information was not provided in the MGF file. In order to associate these fragment spectra to the caffeine compound we just added to the database, we need to assign them the ID of the compound (in our case "1").

caf_ms2$compound_id <- "1"

We can then add the spectra to the database using the insertSpectra function. With parameter columns we specify which of the spectra variables we actually want to store in the database.

mydb <- insertSpectra(mydb, caf_ms2,
                      columns = c("compound_id", "msLevel", "splash",
                                  "precursorMz", "collisionEnergy"))
mydb
## class: CompDb 
##  data source: NA 
##  version: NA 
##  organism: NA 
##  compound count: 2 
##  MS/MS spectra count: 2

We thus have now 2 compounds in the database and 2 fragment spectra:

compounds(mydb)
##       name inchi inchikey   formula exactmass
## 1 Caffeine  <NA>     <NA> C8H10N4O2  194.0804
## 2  Glucose  <NA>     <NA>   C6H12O6  180.0634
sps <- Spectra(mydb)
sps$name
## [1] "Caffeine" "Caffeine"

With insertCommpound and insertSpectra further compounds and fragment spectra could be added to the database. Note that both functions support also to add additional columns (fields or variables) to the database. As an example we define below a compound with an arbitrary additional column and add this to the database using parameter addColumns = TRUE.

cmps <- data.frame(compound_id = "3", name = "X003",
                   formula = "C5H2P3O", extra_field = "artificial compound")
mydb <- insertCompound(mydb, cmps, addColumns = TRUE)
compounds(mydb)
##       name inchi inchikey   formula exactmass         extra_field
## 1 Caffeine  <NA>     <NA> C8H10N4O2  194.0804                <NA>
## 2  Glucose  <NA>     <NA>   C6H12O6  180.0634                <NA>
## 3     X003  <NA>     <NA>   C5H2P3O        NA artificial compound

The additional column is now available in the database. Existing entries in a CompDb can also be deleted using the deleteCompound or deleteSpectra functions. Both require as additional input the IDs of the compound(s) (or spectra) to delete. Below we extract the IDs and names of the compounds from our database.

compounds(mydb, columns = c("compound_id", "name"))
##   compound_id     name
## 1           1 Caffeine
## 2           2  Glucose
## 3           3     X003

We can now delete the compound "X003" with deleteCompound and the ID of this compound.

mydb <- deleteCompound(mydb, ids = "3")
compounds(mydb)
##       name inchi inchikey   formula exactmass extra_field
## 1 Caffeine  <NA>     <NA> C8H10N4O2  194.0804        <NA>
## 2  Glucose  <NA>     <NA>   C6H12O6  180.0634        <NA>

Note that deleting a compound with associated spectra (or ions) will result in an error, thus it would not be possible to delete caffeine from the database, because it contains also MS2 spectra for that compound. Using parameter recursive = TRUE in the deleteCompound call would however allow to delete the compound and all associated spectra (and/or ions) along with it. Below we delete thus caffeine and the associated MS2 spectra which leaves us a CompDb with a single compound and no more MS2 spectra.

mydb <- deleteCompound(mydb, ids = "1", recursive = TRUE)
compounds(mydb)
##      name inchi inchikey formula exactmass extra_field
## 1 Glucose  <NA>     <NA> C6H12O6  180.0634        <NA>
Spectra(mydb)
## MSn data (Spectra) with 0 spectra in a MsBackendCompDb backend:

Note that these functions can also be used to add or remove annotations to/from any CompDb database, as long as the database is writeable (i.e. the database is loaded by specifying flags = RSQLite::SQLITE_RW as additional parameter to the CompDb call to load the database).

3 Session information

## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
## 
## 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       
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] MsBackendMgf_1.5.0      RSQLite_2.2.17          Spectra_1.7.2          
##  [4] ProtGenerics_1.29.0     BiocParallel_1.31.12    CompoundDb_1.1.6       
##  [7] S4Vectors_0.35.4        BiocGenerics_0.43.4     AnnotationFilter_1.21.0
## [10] BiocStyle_2.25.0       
## 
## loaded via a namespace (and not attached):
##  [1] Biobase_2.57.1         sass_0.4.2             bit64_4.0.5           
##  [4] jsonlite_1.8.0         bslib_0.4.0            assertthat_0.2.1      
##  [7] MetaboCoreUtils_1.5.2  BiocManager_1.30.18    blob_1.2.3            
## [10] GenomeInfoDbData_1.2.8 yaml_2.3.5             pillar_1.8.1          
## [13] glue_1.6.2             digest_0.6.29          GenomicRanges_1.49.1  
## [16] XVector_0.37.1         colorspace_2.0-3       htmltools_0.5.3       
## [19] pkgconfig_2.0.3        bookdown_0.29          zlibbioc_1.43.0       
## [22] purrr_0.3.4            scales_1.2.1           tibble_3.1.8          
## [25] generics_0.1.3         IRanges_2.31.2         ggplot2_3.3.6         
## [28] DT_0.25                cachem_1.0.6           lazyeval_0.2.2        
## [31] cli_3.4.0              magrittr_2.0.3         memoise_2.0.1         
## [34] evaluate_0.16          fs_1.5.2               fansi_1.0.3           
## [37] MASS_7.3-58.1          xml2_1.3.3             tools_4.2.1           
## [40] lifecycle_1.0.2        stringr_1.4.1          munsell_0.5.0         
## [43] cluster_2.1.4          compiler_4.2.1         jquerylib_0.1.4       
## [46] GenomeInfoDb_1.33.7    rlang_1.0.5            grid_4.2.1            
## [49] RCurl_1.98-1.8         rsvg_2.3.1             rjson_0.2.21          
## [52] MsCoreUtils_1.9.1      htmlwidgets_1.5.4      bitops_1.0-7          
## [55] base64enc_0.1-3        rmarkdown_2.16         gtable_0.3.1          
## [58] codetools_0.2-18       DBI_1.1.3              R6_2.5.1              
## [61] gridExtra_2.3          knitr_1.40             dplyr_1.0.10          
## [64] fastmap_1.1.0          bit_4.0.4              utf8_1.2.2            
## [67] clue_0.3-61            stringi_1.7.8          parallel_4.2.1        
## [70] Rcpp_1.0.9             vctrs_0.4.1            png_0.1-7             
## [73] dbplyr_2.2.1           tidyselect_1.1.2       xfun_0.33             
## [76] ChemmineR_3.49.2