# Creating a new field for entries.

Pierrick Roger

#### 1 May 2024

#### Abstract

This vignettte explains the process to create a new biodb generic entry field, and how to parse its value for a particular database connector.

#### Package

biodb 1.12.0

# 1 Introduction

In this vignette we will focus on creating a new biodb field to be used inside an existing connector. biodb fields are defined for all database connectors. They are definitions of what types of data may be set inside biodb entry objects. Since they are shared by all connectors, they need to be defined without any reference to a particular database. However many of them are linked to a particular science or technological domain (genetics, metabolomics, mass spectrometry, …).

An entry field is like a type definition. The definition is done at the top-level of biodb, and thus it not related to any particular connector. The definition includes: a name, a description, a class (integer, double, character, logical), a cardinality (single value or vector), a list of allowed values, a class (to group similar fields like “mass”), etc.

For a particular connector, when an entry object is created in memory, a file containing the values is obtained from the database and a parsing is run in order to extract those values and affect them to associated biodb entry fields inside the biodb entry object. Thus the parsing of the value of a biodb entry field is different for each connector, while the biodb entry field is used by several different connectors.

No biodb connector use all available biodb entry fields. However it can happen that a connector does not implement the parsing of some available data inside a database. The reason is that, in most cases, the amount of available data, and the diversity of it, inside a single entry would require an excessive amount of coding. As a consequence, we often restrict our development onto a subset of the available data, in which we are interested.

When one particular data from the database is not present inside the entries of the corresponding biodb connector, this means that no parsing has been written for it inside the connector. Moreover it could also mean that no biodb entry field is defined to handled this particular type of data. Fortunately, biodb offers you a way to correct dynamically, inside your code, this shortage, creating a new biodb entry field if necessary and creating the corresponding parsing of the data for the connector.

Follow the subsequent explanations in order to learn how to define a new parsing of a value for a connector and assign it to an existing entry field, and how to define a new entry field.

First we instantiate the package:

    mybiodb <- biodb::BiodbMain$new()

    ## INFO  [17:24:26.769] Loading definitions from package biodb version 1.12.0.

## 1.1 Defining a new parsing of a field

Before going with the creation of a new field, we will look at different ways of parsing a value for an existing biodb field that is not handled by a connector. Two connector cases will be used as examples: the `ChebiExConn` connector defined for the Creating a new connector. vignette and the `CompCsvFileConn` connector from the biodb package.

### 1.1.1 Defining a parsing expression for a remote database connector (ChebiExConn)

The `ChebiExConn` class implements an example connector to the ChEBI (Hastings et al. 2012) remote database. See vignette Creating a new connector. for the creation of this connector.

We load dynamically the definition of this connector inside biodb as explained in the Creating a new connector. vignette:

    chebiexDefFile <- system.file("extdata", "chebi_ex.yml", package='biodb')
    connClass <- system.file("extdata", "ChebiExConn.R", package='biodb')
    entryClass <- system.file("extdata", "ChebiExEntry.R", package='biodb')
    source(connClass)
    source(entryClass)
    mybiodb$loadDefinitions(chebiexDefFile)

For our demonstration we will suppose this connector has been created by somebody else, and we have no access to the implementation code.

We create a connector to this database:

    conn <- mybiodb$getFactory()$createConn('chebi.ex')

And get one entry:

    entryIds <- c('17001', '40304', '64679')
    entriesDf <- mybiodb$entriesToDataframe(conn$getEntry(entryIds))

That you can see in table 1.

Table 1: Some entries from ChebiEx database
Their charge value is not visible become it was not parsed from database file.
accession | formula | inchi | inchikey | molecular.mass | monoisotopic.mass | name | smiles | chebi.ex.id
--- | --- | --- | --- | --- | --- | --- | --- | ---
17001 | C9H13N5O4 | InChI=1S/C9H13N5O4/c10-9-13-7-5(8(18)14-9)12-3(1-11-7)6(17)4(16)2-15/h4,6,15-17H,1-2H2,(H4,10,11,13,14,18)/t4-,6+/m1/s1 | YQIFAMYNGGOTFB-XINAWCOVSA-N | 255.2308 | 255.0967 | 7,8-dihydroneopterin | `Nc1nc2NCC(=Nc2c(=O)[nH]1)[C@H](O)[C@H](O)CO` | 17001
40304 | C10H13N5O5 | InChI=1S/C10H13N5O5/c11-9-13-7-6(8(18)14-9)12-10(19)15(7)5-1-3(17)4(2-16)20-5/h3-5,16-17H,1-2H2,(H,12,19)(H3,11,13,14,18)/t3-,4+,5+/m0/s1 | HCAJQHYUCKICQH-VPENINKCSA-N | 283.2407 | 283.0917 | 8-hydroxy-2’-deoxyguanosine | `Nc1nc2n([C@H]3C[C@H](O)[C@@H](CO)O3)c(O)nc2c(=O)[nH]1` | 40304
64679 | C9H18NO11P | InChI=1S/C9H18NO11P/c10-3(8(15)16)2-19-22(17,18)21-9-7(14)6(13)5(12)4(1-11)20-9/h3-7,9,11-14H,1-2,10H2,(H,15,16)(H,17,18)/t3-,4+,5+,6-,7-,9+/m0/s1 | JTBRVTASGISCGJ-RHNOWPELSA-N | 347.2131 | 347.0618 | O-(alpha-D-mannose-1-phosphoryl)-L-serine | `N[C@@H](COP(O)(=O)O[C@H]1O[C@H](CO)[C@@H](O)[C@H](O)[C@@H]1O)C(O)=O` | 64679

You will notice that no electrical charge is mentioned for the molecules in the table, while it is present inside ChEBI database. Let us choose one of the entries:

    id <- entryIds[[1]]
    id

    ## [1] "17001"

And get the ChEBI web page of this entry:

    conn$getEntryPageUrl(id)

    ##                                                   17001
    ## "https://www.ebi.ac.uk/chebi/searchId.do?chebiId=17001"

Go on this page ( https://www.ebi.ac.uk/chebi/searchId.do?chebiId=17001 ) to check that the electrical charge information is indeed given by ChEBI (`Net Charge 0`). To integrate this data inside the biodb entry, we need to extract it from the file returned by ChEBI. When asked for an entry on its web service interface, ChEBI returns an XML file that biodb stores in its cache. By calling the following method on your connector, you can get the path to the biodb cache file:

    conn$getCacheFile(id)

    ## [1] "C:\\Users\\biocbuild\\AppData\\Local/R/cache/R/biodb/chebi.ex-0c5076ac2a43d16dbce503a44b09f649/17001.xml"

If you take a look to this file with your favourite editor, you will see the following text:

    <charge>0</charge>

This the XML tag that stores the value of the electrical charge. To extract values from XML, biodb uses the XPath query language. In XPath language, the expression `//chebi:charge` means to get the value inside the `charge` tag wherever it is (`//`) inside the tree structure of the XML. See XPath Tutorial for an introduction to XPath. We need to give this XPath expression to the biodb instance, and explain to which entry field the extracted value must be affected. This is done by defining a small YAML file:

    chargeParsingDefFile <- system.file("extdata", "chebi_ex_charge_parsing.yml", package='biodb')

Whose content is as follow:

    databases:
      chebi.ex:
        parsing.expr:
          charge: //chebi:charge

In this file we define a new parsing expression inside the `parsing.expr` section for the `chebi.ex` database connector. The definition of the parsing expression consists of two values: the targeted biodb entry field (`charge`) and the XPath expression (`//chebi:charge`).

Now we just have to load this new definition:

    mybiodb$loadDefinitions(chargeParsingDefFile)

Delete the existing connector:

    mybiodb$getFactory()$deleteConn(conn)

    ## INFO  [17:24:29.970] Connector "chebi.ex" deleted.

Recreate the connector and reload the same entries:

    conn <- mybiodb$getFactory()$createConn('chebi.ex')
    entriesDf <- mybiodb$entriesToDataframe(conn$getEntry(entryIds))

You can see in 2 that the electrical charge is now indicated for each entry.

Table 2: Some entries from ChebiEx database
Their charge value is now visible, since the parsing expression has been added to the connector.
accession | formula | inchi | inchikey | molecular.mass | monoisotopic.mass | name | smiles | charge | chebi.ex.id
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
17001 | C9H13N5O4 | InChI=1S/C9H13N5O4/c10-9-13-7-5(8(18)14-9)12-3(1-11-7)6(17)4(16)2-15/h4,6,15-17H,1-2H2,(H4,10,11,13,14,18)/t4-,6+/m1/s1 | YQIFAMYNGGOTFB-XINAWCOVSA-N | 255.2308 | 255.0967 | 7,8-dihydroneopterin | `Nc1nc2NCC(=Nc2c(=O)[nH]1)[C@H](O)[C@H](O)CO` | 0 | 17001
40304 | C10H13N5O5 | InChI=1S/C10H13N5O5/c11-9-13-7-6(8(18)14-9)12-10(19)15(7)5-1-3(17)4(2-16)20-5/h3-5,16-17H,1-2H2,(H,12,19)(H3,11,13,14,18)/t3-,4+,5+/m0/s1 | HCAJQHYUCKICQH-VPENINKCSA-N | 283.2407 | 283.0917 | 8-hydroxy-2’-deoxyguanosine | `Nc1nc2n([C@H]3C[C@H](O)[C@@H](CO)O3)c(O)nc2c(=O)[nH]1` | 0 | 40304
64679 | C9H18NO11P | InChI=1S/C9H18NO11P/c10-3(8(15)16)2-19-22(17,18)21-9-7(14)6(13)5(12)4(1-11)20-9/h3-7,9,11-14H,1-2,10H2,(H,15,16)(H,17,18)/t3-,4+,5+,6-,7-,9+/m0/s1 | JTBRVTASGISCGJ-RHNOWPELSA-N | 347.2131 | 347.0618 | O-(alpha-D-mannose-1-phosphoryl)-L-serine | `N[C@@H](COP(O)(=O)O[C@H]1O[C@H](CO)[C@@H](O)[C@H](O)[C@@H]1O)C(O)=O` | 0 | 64679

### 1.1.2 Defining a parsing expression for a local database connector (CompCsvFileConn)

The `CompCsvFileConn` class implements a connector to a local CSV file database of chemical compounds, as explained inside vignette

. For a database stored inside a CSV file, the data parsing is very simple. It consists in associating each biodb entry field with a column name. By default biodb will define associations for each entry field whose name is used for a column. The columns whose names are not the names of existing biodb entry fields are not associated and thus you cannot access their values from biodb.

If you want to access those values, you have the define manually the associations, using the `setField()` method.

For our example we use an extract from ChEBI database as the input CSV database file:

    fileUrl <- system.file("extdata", "chebi_extract_with_unknown_column.tsv", package='biodb')

See table 3 for the content of this file.

Table 3: First lines of the compound database file.
accession | elecCharge | formula | monoisotopic.mass | molecular.mass | kegg.compound.id | name | smiles
--- | --- | --- | --- | --- | --- | --- | ---
1018 | 0 | C2H8AsNO3 | 168.97201 | 169.012 | C07279 | 2-Aminoethylarsonate | `NCC[As](O)(O)=O`
1390 | 0 | C8H8O2 | 136.05243 | 136.148 | C06224 | 3,4-Dihydroxystyrene | `Oc1ccc(C=C)cc1O`
1456 | 0 | C3H9NO2 | 91.06333 | 91.109 | C06057 | 3-aminopropane-1,2-diol | `NC[C@H](O)CO`
1549 | 0 | C3H5O3R | 89.02387 | 89.070 | C03834 | 3-hydroxymonocarboxylic acid | `OC([*])CC(O)=O`
1894 | 0 | C5H11NO | 101.08406 | 101.147 | C10974 | 4-Methylaminobutanal | `CNCCCC=O`
1932 | 0 | C6H6NR | 92.05002 | 92.119 | C03084 | 4-Substituted aniline | `Nc1ccc([*])cc1`

In this file, the column name `elecCharge` will not be associated to any biodb entry field. Indeed, the biodb entry field the electrical charge of a molecule is `charge`, not `elecCharge`. Let us verify that.

We first create the connector to this CSV file:

    conn <- mybiodb$getFactory()$createConn('comp.csv.file', url=fileUrl)

And get the content of some of the entries:

    entriesDf <- mybiodb$entriesToDataframe(conn$getEntry(conn$getEntryIds()))

    ## INFO  [17:24:30.936] Loading file database "F:/biocbuild/bbs-3.19-bioc/tmpdir/RtmpUZeQDS/Rinst3c581577b32/biodb/extdata/chebi_extract_with_unknown_column.tsv".
    ## WARN  [17:24:30.948] Column "elecCharge" does not match any biodb field.

    ## Warning in warn("Column \"%s\" does not match any biodb field.", colname):
    ## Column "elecCharge" does not match any biodb field.

See table 4 for the content of this entry. As you can see, no `charge` field is listed.

Table 4: Some entries from the compound database.
accession | formula | monoisotopic.mass | molecular.mass | kegg.compound.id | name | smiles | comp.csv.file.id
--- | --- | --- | --- | --- | --- | --- | ---
1018 | C2H8AsNO3 | 168.97201 | 169.012 | C07279 | 2-Aminoethylarsonate | `NCC[As](O)(O)=O` | 1018
1390 | C8H8O2 | 136.05243 | 136.148 | C06224 | 3,4-Dihydroxystyrene | `Oc1ccc(C=C)cc1O` | 1390
1456 | C3H9NO2 | 91.06333 | 91.109 | C06057 | 3-aminopropane-1,2-diol | `NC[C@H](O)CO` | 1456
1549 | C3H5O3R | 89.02387 | 89.070 | C03834 | 3-hydroxymonocarboxylic acid | `OC([*])CC(O)=O` | 1549
1894 | C5H11NO | 101.08406 | 101.147 | C10974 | 4-Methylaminobutanal | `CNCCCC=O` | 1894
1932 | C6H6NR | 92.05002 | 92.119 | C03084 | 4-Substituted aniline | `Nc1ccc([*])cc1` | 1932

Now we call the method to define the new association:

    conn$setField('charge', 'elecCharge')

The first parameter is the name of the biodb entry field, the second the name of the column inside the CSV file

The new column will now be parsed when getting the entry. But before we must remove all entries from memory:

    conn$deleteAllEntriesFromVolatileCache()

And then reload the same entries again:

    entries2Df <- mybiodb$entriesToDataframe(conn$getEntry(conn$getEntryIds()))

See table 5 for the content of this entry. A new data frame column is present, named `charge`.

Table 5: Some entries from the compound database
They now show the newly parsed “charge” field.
accession | formula | monoisotopic.mass | molecular.mass | kegg.compound.id | name | smiles | charge | comp.csv.file.id
--- | --- | --- | --- | --- | --- | --- | --- | ---
1018 | C2H8AsNO3 | 168.97201 | 169.012 | C07279 | 2-Aminoethylarsonate | `NCC[As](O)(O)=O` | 0 | 1018
1390 | C8H8O2 | 136.05243 | 136.148 | C06224 | 3,4-Dihydroxystyrene | `Oc1ccc(C=C)cc1O` | 0 | 1390
1456 | C3H9NO2 | 91.06333 | 91.109 | C06057 | 3-aminopropane-1,2-diol | `NC[C@H](O)CO` | 0 | 1456
1549 | C3H5O3R | 89.02387 | 89.070 | C03834 | 3-hydroxymonocarboxylic acid | `OC([*])CC(O)=O` | 0 | 1549
1894 | C5H11NO | 101.08406 | 101.147 | C10974 | 4-Methylaminobutanal | `CNCCCC=O` | 0 | 1894
1932 | C6H6NR | 92.05002 | 92.119 | C03084 | 4-Substituted aniline | `Nc1ccc([*])cc1` | 0 | 1932

## 1.2 Creating a new field and parsing its value

Sometimes you just do not need to parse some value for setting an existing biodb field, but you need to get a value that does not correspond to any defined biodb field. In this case, you need to define a new field alongside defining your parsing.

For this demonstration we will use again the `ChebiExConn` connector example from the Creating a new connector. vignette.

In the ChEBI database, each entry (i.e.: molecule) gets a score (a number of stars) reflecting its curation status. This field is not present inside the current `ChebiExConn` connector example. Let us see that by displaying the content of some entries:

    conn <- mybiodb$getFactory()$getConn('chebi.ex')
    entryIds <- c('17001', '40304', '64679')
    entriesDf <- mybiodb$entriesToDataframe(conn$getEntry(entryIds))

See table 6.

Table 6: Some entries from ChebiEx database
There is no field that indicates the number of stars of an entry.
accession | formula | inchi | inchikey | molecular.mass | monoisotopic.mass | name | smiles | charge | chebi.ex.id
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
17001 | C9H13N5O4 | InChI=1S/C9H13N5O4/c10-9-13-7-5(8(18)14-9)12-3(1-11-7)6(17)4(16)2-15/h4,6,15-17H,1-2H2,(H4,10,11,13,14,18)/t4-,6+/m1/s1 | YQIFAMYNGGOTFB-XINAWCOVSA-N | 255.2308 | 255.0967 | 7,8-dihydroneopterin | `Nc1nc2NCC(=Nc2c(=O)[nH]1)[C@H](O)[C@H](O)CO` | 0 | 17001
40304 | C10H13N5O5 | InChI=1S/C10H13N5O5/c11-9-13-7-6(8(18)14-9)12-10(19)15(7)5-1-3(17)4(2-16)20-5/h3-5,16-17H,1-2H2,(H,12,19)(H3,11,13,14,18)/t3-,4+,5+/m0/s1 | HCAJQHYUCKICQH-VPENINKCSA-N | 283.2407 | 283.0917 | 8-hydroxy-2’-deoxyguanosine | `Nc1nc2n([C@H]3C[C@H](O)[C@@H](CO)O3)c(O)nc2c(=O)[nH]1` | 0 | 40304
64679 | C9H18NO11P | InChI=1S/C9H18NO11P/c10-3(8(15)16)2-19-22(17,18)21-9-7(14)6(13)5(12)4(1-11)20-9/h3-7,9,11-14H,1-2,10H2,(H,15,16)(H,17,18)/t3-,4+,5+,6-,7-,9+/m0/s1 | JTBRVTASGISCGJ-RHNOWPELSA-N | 347.2131 | 347.0618 | O-(alpha-D-mannose-1-phosphoryl)-L-serine | `N[C@@H](COP(O)(=O)O[C@H]1O[C@H](CO)[C@@H](O)[C@H](O)[C@@H]1O)C(O)=O` | 0 | 64679

In the XML entry content returned by the ChEBI server, this field is stored inside the `entityStar` element as shown here:

    <entityStar>3</entityStar>

You can check that directly inside the XML content of one of the entries, as explained earlier.

To get this number of stars we define the new field and its parsing expression inside the following YAML file:

    nStarsDefFile <- system.file("extdata", "chebi_ex_stars_field.yml", package='biodb')

Here is its content:

    databases:
      chebi.ex:
        parsing.expr:
          n_stars: //chebi:return/chebi:entityStar

    fields:
      n_stars:
        description: The ChEBI example stars indicator.
        class: integer

You already know how to define the parsing expression inside the YAML file The value of the XPath expression is a bit longer than for the electrical charge, but the principle is the same.

What is new, is the `fields` section, in which we define the new fields. The name of the field (`n_stars`) is used as a key inside the section. Then several keys are used to define the field, see table 7 for a description of those keys.

Table 7: The different keys used to define a field.
Key | Description
--- | ---
alias | Other possible names of the field.
description | A description of the field.
class | The R class. One of `integer`, `character`, `double`, `logical`.
type | A name of a group for related fields. Existing ones are `name` and `mass`, but you can create your owns.
card | The cardinality. Either `one` (single value) or `many` (vector).
case.insensitive | If `true` then the value is case insensitive.
forbids.duplicates | If `true` and the cardinality is `many`, no duplicate values will be accepted.
lower.case | If `true`, the value will be put in lower case.
allowed.values | If this vector is not empty, then only the values listed in this vector will be allowed for this field.

We can now load the new definition:

    mybiodb$loadDefinitions(nStarsDefFile)

Delete the existing connector:

    mybiodb$getFactory()$deleteConn(conn)

    ## INFO  [17:24:35.629] Connector "chebi.ex" deleted.

Recreate the connector and reload the same entries:

    conn <- mybiodb$getFactory()$createConn('chebi.ex')
    entriesDf <- mybiodb$entriesToDataframe(conn$getEntry(entryIds))

See table 8. Now a column named `n_stars` indicates the number of stars for each entry in the data frame.

Table 8: Some entries from ChebiEx database
Now there is a n_stars no field that indicates the number of stars of an entry.
accession | formula | inchi | inchikey | molecular.mass | monoisotopic.mass | name | smiles | charge | n.stars | chebi.ex.id
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
17001 | C9H13N5O4 | InChI=1S/C9H13N5O4/c10-9-13-7-5(8(18)14-9)12-3(1-11-7)6(17)4(16)2-15/h4,6,15-17H,1-2H2,(H4,10,11,13,14,18)/t4-,6+/m1/s1 | YQIFAMYNGGOTFB-XINAWCOVSA-N | 255.2308 | 255.0967 | 7,8-dihydroneopterin | `Nc1nc2NCC(=Nc2c(=O)[nH]1)[C@H](O)[C@H](O)CO` | 0 | 3 | 17001
40304 | C10H13N5O5 | InChI=1S/C10H13N5O5/c11-9-13-7-6(8(18)14-9)12-10(19)15(7)5-1-3(17)4(2-16)20-5/h3-5,16-17H,1-2H2,(H,12,19)(H3,11,13,14,18)/t3-,4+,5+/m0/s1 | HCAJQHYUCKICQH-VPENINKCSA-N | 283.2407 | 283.0917 | 8-hydroxy-2’-deoxyguanosine | `Nc1nc2n([C@H]3C[C@H](O)[C@@H](CO)O3)c(O)nc2c(=O)[nH]1` | 0 | 3 | 40304
64679 | C9H18NO11P | InChI=1S/C9H18NO11P/c10-3(8(15)16)2-19-22(17,18)21-9-7(14)6(13)5(12)4(1-11)20-9/h3-7,9,11-14H,1-2,10H2,(H,15,16)(H,17,18)/t3-,4+,5+,6-,7-,9+/m0/s1 | JTBRVTASGISCGJ-RHNOWPELSA-N | 347.2131 | 347.0618 | O-(alpha-D-mannose-1-phosphoryl)-L-serine | `N[C@@H](COP(O)(=O)O[C@H]1O[C@H](CO)[C@@H](O)[C@H](O)[C@@H]1O)C(O)=O` | 0 | 3 | 64679

# 2 Closing biodb instance

Do not forget to terminate your biodb instance once you are done with it:

    mybiodb$terminate()

    ## INFO  [17:24:36.296] Closing BiodbMain instance...
    ## INFO  [17:24:36.298] Connector "comp.csv.file" deleted.
    ## INFO  [17:24:36.300] Connector "chebi.ex" deleted.

# 3 Session information

    sessionInfo()

    ## R version 4.4.0 beta (2024-04-15 r86425 ucrt)
    ## Platform: x86_64-w64-mingw32/x64
    ## Running under: Windows Server 2022 x64 (build 20348)
    ##
    ## Matrix products: default
    ##
    ##
    ## locale:
    ## [1] LC_COLLATE=en_US.UTF-8  LC_CTYPE=en_US.UTF-8    LC_MONETARY=en_US.UTF-8
    ## [4] LC_NUMERIC=C            LC_TIME=en_US.UTF-8
    ##
    ## time zone: America/New_York
    ## tzcode source: internal
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] biodb_1.12.0     BiocStyle_2.32.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] rappdirs_0.3.3       sass_0.4.9           utf8_1.2.4
    ##  [4] generics_0.1.3       bitops_1.0-7         stringi_1.8.3
    ##  [7] RSQLite_2.3.6        hms_1.1.3            digest_0.6.35
    ## [10] magrittr_2.0.3       evaluate_0.23        bookdown_0.39
    ## [13] fastmap_1.1.1        blob_1.2.4           plyr_1.8.9
    ## [16] jsonlite_1.8.8       progress_1.2.3       DBI_1.2.2
    ## [19] BiocManager_1.30.22  httr_1.4.7           fansi_1.0.6
    ## [22] XML_3.99-0.16.1      jquerylib_0.1.4      cli_3.6.2
    ## [25] rlang_1.1.3          chk_0.9.1            crayon_1.5.2
    ## [28] dbplyr_2.5.0         bit64_4.0.5          withr_3.0.0
    ## [31] cachem_1.0.8         yaml_2.3.8           tools_4.4.0
    ## [34] memoise_2.0.1        dplyr_1.1.4          filelock_1.0.3
    ## [37] curl_5.2.1           vctrs_0.6.5          R6_2.5.1
    ## [40] BiocFileCache_2.12.0 lifecycle_1.0.4      stringr_1.5.1
    ## [43] bit_4.0.5            pkgconfig_2.0.3      pillar_1.9.0
    ## [46] bslib_0.7.0          glue_1.7.0           Rcpp_1.0.12
    ## [49] lgr_0.4.4            xfun_0.43            tibble_3.2.1
    ## [52] tidyselect_1.2.1     knitr_1.46           htmltools_0.5.8.1
    ## [55] rmarkdown_2.26       compiler_4.4.0       prettyunits_1.2.0
    ## [58] askpass_1.2.0        RCurl_1.98-1.14      openssl_2.1.2

# References

Hastings, Janna, Paula de Matos, Adriano Dekker, Marcus Ennis, Bhavana Harsha, Namrata Kale, Venkatesh Muthukrishnan, et al. 2012. “The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013.” Nucleic Acids Research 41 (D1): D456–D463. https://doi.org/10.1093/nar/gks1146.