1 Introduction

biodb provides access to chemical, biological and mass spectra databases, and offers a development framework that facilitates the writing of new connectors.

Numerous public databases are available for scientific research, but few are easily accessible from a programming environment, making it hard for most of researchers to use their content. Developing a code to access databases and keep it up to date with the evolutions of these databases are two time consuming tasks. It is thus greatly preferable to use an already developed package.

In R, packages with public database connectors, most often propose to connect to one single database with a specific API, and do not offer a development framework (Szöcs et al. 2020; Guha 2016; Tenenbaum and Volkening 2020; Carey 2020; Soudy et al. 2020; Carlson and Ortutay 2020; Wolf 2019; Stravs et al. 2013; Drost and Paszkowski 2017; Winter, Chamberlain, and Guangchun 2020). When a package does not offer the services the scientific programmer requests, or when no package exists for the targeted database, a homemade solution is implemented. In such a case, the effort spent is often lost and never capitalized for sharing with the community.

biodb has been designed and implemented as a unified API to databases and a development framework. The unified API allows to access the databases in a standardized way, while allowing original database services to be accessed directly. The development framework has for goal to help scientific programmers to capitalize on their effort to improve connection to databases and share it with the community. The framework lowers the effort needed by the developer to improve an existing connector or implement a new one. Most biodb connectors are distributed inside separated packages, that are automatically recognized by the main package. This system of extensions gives more independence for developing new connectors and distributing them, since developers do not need to request any modification inside the main package code.

The database services provided by the unified API of biodb: retrieval of entries, chemical and biological compound search by mass and name, mass spectra annotation, MSMS matching, read and write of in-house local databases. Alongside the unified API, connectors to public databases furnishes also access to specific web services through dedicated methods. See table 1 for a list of available features.

Table 1: biodb main features
These are generic features (i.e.: present at top-level of architecture or present in at least a group of connectors), unless specified otherwise.
Features Description
Getting entries Retrieval of entries by accession number, and search for entries.
Merging entries Merging entries from different databases.
Exporting entries Extracting values of entries into data frames.
In-house db reading Connection to a local in-house database (CSV file or SQLite database file).
In-house db writing Writing entries into an in-house database.
LCMS annotation Annotating an LCMS spectra using a spectra database.
MSMS matching Search for matching MSMS spectra into a database.
Framework Development framework for easy implementation of biodb extension packages.
Pathways Search for biological pathways with KEGG (see biodbKegg extension).

In this vignette we will introduce you to the basic features of biodb, allowing you to be quickly productive. Pointers toward other documents are included along the way, for going into details or learning advanced features.

For a complete list of featuresSee vignette Details on biodb for a more more information of biodb with other packages.

2 Installation

Install using Bioconductor:

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install('biodb')

3 Initialization

The first step in using biodb, is to create an instance of the main class BiodbMain. This is done by calling the constructor of the class:

mybiodb <- biodb::newInst()

During this step the configuration is set up, the cache system is initialized and extension packages are loaded.

We will see at the end of this vignette that the biodb instance needs to be terminated with a call to the terminate() method.

4 Connecting to a database

In biodb the connection to a database is handled by a connector instance that you can get from the factory. Here we create a connector to a CSV file database (see 2 for content) of chemical compounds:

compUrl <- system.file("extdata", "chebi_extract.tsv", package='biodb')
compdb <- mybiodb$getFactory()$createConn('comp.csv.file', url=compUrl)
## Loading required package: biodb

The two parameters passed to the createConn() are the identifier of the Compound CSV File connector class and the URL (i.e.: the path) of the TSV file. With this connector instance you are now able to get entries and search for them by either name or mass. By default biodb will use the TAB character as separator for the CSV file, and the standard biodb entry field names for the column names of the file. To load a CSV file with a different separator and custom column names, you have to define them inside the connector instance. Please see vignette

for learning how to define the character separator and the column names of your file inside the CSV database connector.

To get a list of all connector classes available with their names, call an instance of BiodbDbsInfo:

mybiodb$getDbsInfo()
## Biodb databases information instance.
## The following databases are defined:
##   comp.csv.file: Compound CSV File connector class.
##   comp.sqlite: Compound SQLite connector class.
##   mass.csv.file: Mass spectra CSV File connector class.
##   mass.sqlite: Mass spectra SQLite connector class.

To get available informations on these database connectors, use the get() method:

mybiodb$getDbsInfo()$get(c('comp.csv.file', 'mass.csv.file'))
## $comp.csv.file
## Compound CSV File class.
##   Class: comp.csv.file.
##   Package: biodb.
##   Description: A connector to handle a compound database stored inside a CSV file. It is possible to choose the separator for the CSV file, as well as match the column names with the biodb entry fields..
##   Entry content type: tsv.
## 
## $mass.csv.file
## Mass spectra CSV File class.
##   Class: mass.csv.file.
##   Package: biodb.
##   Description: A connector to handle a mass spectra database stored inside a CSV file. It is possible to choose the separator for the CSV file, as well as match the column names with the biodb entry fields...
##   Entry content type: tsv.

Here we must stop a moment to explain the use of the $ operator. This operator is the call operator for the object oriented programming (OOP) model R5. This OOP model is different from S4. While in S4 the generic methods and their specialization are defined apart from the classes, in R5 the two are defined together and a method definition is necessarily part of a class. Each method being part of a class, it is also part of each object of the class, hence the use of a call operator ($) on a object. In the code line above, the call mybiodb$getFactory() means to call getFactory() method onto biodb instance. This call will return another object (of class BiodbFactory) on which we call the method createConn(). Note that while in R Studio, you will benefit from the autosuggestion system to find all methods available for an instance. See vignette Details on biodb for explanations about the OOP model chosen for biodb.


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

5 Accessing entries

The main goal of the connector is to retrieve entries. The two main generic ways to retrieve entries with a connector are: getting entries using their identifiers (accession numbers), and searching for entries by name. For compound databases, there is also the possibility to search for entries by mass.

In this section we will show how to get entries, convert them into a data frame and search for entries by name. For advanced features about entries, please see the vignette entries.

5.1 Getting entries

Getting entries is done with the getEntry() methods to which you pass a character vector of one or more identifiers:

entries <- compdb$getEntry(c('1018', '1549', '64679'))
## INFO  [16:47:36.307] Loading file database "/tmp/RtmpmQ64Zk/Rinst19bbd64b4a5ea9/biodb/extdata/chebi_extract.tsv".
entries
## [[1]]
## Biodb Compound CSV File entry instance 1018.
## 
## [[2]]
## Biodb Compound CSV File entry instance 1549.
## 
## [[3]]
## Biodb Compound CSV File entry instance 64679.

The returned objects are instances of BiodbEntry, which means you can call on them all functions available in this class. Here is an example of calling the method getFieldsJson() on the first entry in order to get a JSON representation of the entry values:

entries[[1]]$getFieldsAsJson()
## {
##   "accession": "1018",
##   "formula": "C2H8AsNO3",
##   "monoisotopic.mass": 168.97201,
##   "molecular.mass": 169.012,
##   "kegg.compound.id": "C07279",
##   "name": "2-Aminoethylarsonate",
##   "smiles": "NCC[As](O)(O)=O",
##   "description": "",
##   "comp.csv.file.id": "1018"
## }

5.2 Getting all fields defined inside an entry

To get the list of fields defined (i.e.: with an associated value) in an entry, call the method getFieldNames() on the entry instance:

fields <- entries[[1]]$getFieldNames()
fields
## [1] "accession"         "comp.csv.file.id"  "description"      
## [4] "formula"           "kegg.compound.id"  "molecular.mass"   
## [7] "monoisotopic.mass" "name"              "smiles"

The names returned correspond to all the fields for which a value has been parsed from the content returned by the database. To know the significance of each field you have to call the method get() on the BiodbEntryFields class:

mybiodb$getEntryFields()$get(fields)
## $accession
## Entry field "accession".
##   Description: The accession number of the entry.
##   Class: character.
##   Case: sensitive.
##   Cardinality: one.
##   Aliases: NA.
## 
## $comp.csv.file.id
## Entry field "comp.csv.file.id".
##   Description: Compound CSV File ID
##   Class: character.
##   Case: insensitive.
##   Type: id.
##   Cardinality: many.
##   Duplicates: forbidden.
##   Aliases: NA.
## 
## $description
## Entry field "description".
##   Description: The decription of the entry.
##   Class: character.
##   Case: sensitive.
##   Cardinality: one.
##   Aliases: protdesc.
## 
## $formula
## Entry field "formula".
##   Description: Empirical chemical formula of a compound.
##   Class: character.
##   Case: sensitive.
##   Cardinality: one.
##   Aliases: NA.
## 
## $kegg.compound.id
## Entry field "kegg.compound.id".
##   Description: KEGG Compound ID
##   Class: character.
##   Case: insensitive.
##   Type: id.
##   Cardinality: many.
##   Duplicates: forbidden.
##   Aliases: NA.
## 
## $molecular.mass
## Entry field "molecular.mass".
##   Description: Molecular mass (also called molecular weight), in u (unified atomic mass units) or Da (Dalton). It is computed from the atomic masses of each nuclide present in the molecule, taking into account the various possible isotops of each atom. See https://en.wikipedia.org/wiki/Molecular_mass.
##   Class: double.
##   Type: mass.
##   Cardinality: one.
##   Aliases: mass, molecular.weight, compoundmass.
## 
## $monoisotopic.mass
## Entry field "monoisotopic.mass".
##   Description: Monoisotopic mass, in u (unified atomic mass units) or Da (Dalton). It is computed using the mass of the primary isotope of the elements including the mass defect (mass difference between neutron and proton, and nuclear binding energy). Used with high resolution mass spectrometers. See https://en.wikipedia.org/wiki/Monoisotopic_mass.
##   Class: double.
##   Type: mass.
##   Cardinality: one.
##   Aliases: exact.mass.
## 
## $name
## Entry field "name".
##   Description: The name of the entry.
##   Class: character.
##   Case: insensitive.
##   Type: name.
##   Cardinality: many.
##   Duplicates: forbidden.
##   Aliases: fullnames, synonyms.
## 
## $smiles
## Entry field "smiles".
##   Description: SMILES.
##   Class: character.
##   Case: sensitive.
##   Cardinality: one.
##   Aliases: NA.

The BiodbEntryFields gathers all information about entry fields, the same way the BiodbDbsInfo class gather information about all database connectors.

5.3 Getting field values from an entry

In biodb the definition of fields are global. Thus they are shared between databases, and the same field will have the same name in two entries of two different databases.

getFieldValue() is used to get the value of a field:

entries[[1]]$getFieldValue('formula')
## [1] "C2H8AsNO3"

5.4 Exporting entries into a data frame

Another way to access field values of entries, is to export them as a data frame.

You can export the values of one single entry:

entryDf <- entries[[1]]$getFieldsAsDataframe()

See 3 for the exported data frame.


Table 3: Values of one entry of the compound database.
accession formula monoisotopic.mass molecular.mass kegg.compound.id name smiles description comp.csv.file.id
1018 C2H8AsNO3 168.972 169.012 C07279 2-Aminoethylarsonate NCC[As](O)(O)=O 1018

Or export the values of a set of entries:

entriesDf <- mybiodb$entriesToDataframe(entries)

See 4 for the exported data frame.


Table 4: Values of a set of entries from the compound database.
accession formula monoisotopic.mass molecular.mass kegg.compound.id name smiles description comp.csv.file.id
1018 C2H8AsNO3 168.97201 169.0120 C07279 2-Aminoethylarsonate NCC[As](O)(O)=O 1018
1549 C3H5O3R 89.02387 89.0700 C03834 3-hydroxymonocarboxylic acid OC([*])CC(O)=O 1549
64679 C9H18NO11P 347.06180 347.2131 NA 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 A mannose phosphate in which in which the phosphate group of alpha-D-mannose 1-phosphate is esterified by the alcoholic hydroxy group of L-serine. 64679

5.5 Searching for entries

In biodb each database connector offers the possibility to search entries by their name, although some database servers do not propose this feature in which case an explicit error message will be returned.

The generic method to search for entries is searchForEntries(), it returns a character vector containing identifiers of matchings entries. Here is a search on the name field:

compdb$searchForEntries(list(name='deoxyguanosine'))
## [1] 40304

If you want to search into a compound database, the connector has certainly implemented the search on mass. With our example database, we can search on the monoisotopic.mass field:

compdb$searchForEntries(list(name='guanosine', monoisotopic.mass=list(value=283.0917, delta=0.1)))
## [1] 16750 40304

When searching by mass, the biodb mass field to use must be selected. To get a list of all biodb mass fields, run:

mybiodb$getEntryFields()$getFieldNames(type='mass')
## [1] "average.mass"      "molecular.mass"    "monoisotopic.mass"
## [4] "nominal.mass"

To get information of any of these fields run:

mybiodb$getEntryFields()$get('nominal.mass')
## Entry field "nominal.mass".
##   Description: Nominal mass, in u (unified atomic mass units) or Da (Dalton). It is computed using the mass number of the most abundant isotope of each atom. Typically used with low resolution mass spectrometers. See https://en.wikipedia.org/wiki/Monoisotopic_mass.
##   Class: integer.
##   Type: mass.
##   Cardinality: one.
##   Aliases: NA.

Then to know if you can run a search on a connector on a particular mass field run:

compdb$isSearchableByField('average.mass')
## [1] FALSE

To get a list of all searchable field for a connector, run:

compdb$getSearchableFields()
## [1] "name"              "monoisotopic.mass" "molecular.mass"

6 Mass spectra

Another feature of biodb is the ability to annotate an LCMS spectra or to search for an MSMS spectra matching. In this section we will see the annotation of LCMS spectra and matching of MSMS spectra.

6.1 Mass spectra annotation with a compound database

Using a compound database it is possible to annotate a mass spectra. You will get a data frame containing your data frame input (with your M/Z values) completed by annotations from the compound database.

Here is an input data frame containing M/Z values in negative mode:

ms.tsv <- system.file("extdata", "ms.tsv", package='biodb')
mzdf <- read.table(ms.tsv, header=TRUE, sep="\t")

See 5 for the content of the input.


Table 5: Input M/Z values.
mz rt
282.0839 334
283.0623 872
346.0546 536
821.3964 740

We know call the annotateMzValues() method to run the annotation:

annotMz <- compdb$annotateMzValues(mzdf, mz.tol=1e-3, ms.mode='neg')

The mz.tol option sets the M/Z tolerance (by default in plain value, thus ±0.1 in our case). The ms.mode option defines the MS mode of your input spectrum, either positive ('pos') or negative ('neg'). See 6 for the content of the input. Note that in the output, columns coming from the database have their name prefixed with the database name.


Table 6: Annotation output.
mz rt comp.csv.file.id
282.0839 334 16750
282.0839 334 35485
282.0839 334 40304
283.0623 872 NA
346.0546 536 64679
821.3964 740 15939

See vignette

for more information on how to annotate a spectrum using a compound database, and in particular how to set the column names of the input file.

6.2 Mass spectra annotation with a mass spectra database

Using a mass spectra database it is as well possible to annotate a simple mass spectrum, but also LCMS data (i.e. including retention times).

First we have to open a connection to the LCMS database (see 7 for content):

massUrl <- system.file("extdata", "massbank_extract_lcms_3.tsv", package='biodb')
massDb <- mybiodb$getFactory()$createConn('mass.csv.file', url=massUrl)

Table 7: Excerpt from LCMS database TSV file.
accession smiles mass ms.mode peak.mztheo peak.intensity chrom.col.id chrom.rt chrom.rt.unit formula name ms.level
PR010001 NCCCN 74.0844 pos 73 999 mycol 78 s C3H10N2 1,3-Diaminopropane 1
PR010001 NCCCN 74.0844 pos 86 407 mycol 78 s C3H10N2 1,3-Diaminopropane 1
PR010001 NCCCN 74.0844 pos 174 481 mycol 78 s C3H10N2 1,3-Diaminopropane 1
PR010002 OCC(O)(C1)OCC(O)(CO)O1 180.0634 pos 73 999 mycol 189 s C6H12O6 1,3-Dihydroxyacetone dimer 1
PR010003 OC(=O)CC(O)(CC(O)=O)C(O)=O 192.0270 pos 73 999 mycol 45 s C6H8O7 Citric acid 1
PR010004 COc(c1)c(O)ccc(C=CC(O)=O)1 194.0579 pos 73 999 mycol 90 s C10H10O4 trans-4-Hydroxy-3-methoxycinnamate 1

Then we create an input data frame containing M/Z and RT (retention time) values:

input <- data.frame(mz=c(73.01, 116.04, 174.2), rt=c(79, 173, 79))

Unit of the retention times will be set when running the annotation.

And finally we call the annotation function searchMsPeaks():

annotMzRt <- massDb$searchMsPeaks(input, mz.tol=0.1, rt.unit='s', rt.tol=10, match.rt=TRUE, prefix='match.')
## INFO  [16:47:38.130] Loading file database "/tmp/RtmpmQ64Zk/Rinst19bbd64b4a5ea9/biodb/extdata/massbank_extract_lcms_3.tsv".

The mz.tol option sets the M/Z tolerance (by default in plain value, thus ±0.1 in our case). The match.rt option enables matching on retention time values, rt.unit sets the unit ("s" for second and "min" for minute) and rt.tol the tolerance. The prefix option specifies a custom prefix to use for naming the database columns inside the output. See 8 for the results.


Table 8: Results of annotation of an M/Z and RT input file with an LCMS database.
mz rt match.accession match.chrom.col.id match.chrom.col.name match.chrom.rt match.chrom.rt.unit match.formula match.mass.csv.file.id match.molecular.mass match.ms.level match.ms.mode match.name match.peak.intensity match.peak.mz match.peak.mztheo match.smiles
73.01 79 PR010001 mycol mycol 78 s C3H10N2 PR010001 74.0844 1 pos 1,3-Diaminopropane 999 73 73 NCCCN
116.04 173 PR010006 mycol mycol 176 s C9H13NO2 PR010006 167.0946 1 pos (R)-(-)-Phenylephrine 999 116 116 CNCC@Hc(c1)cc(O)cc1
174.20 79 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

See vignette

for more details on how to annotate an LCMS spectrum with an LCMS database.

6.3 MS/MS matching

biodb also offers an MS/MS matching service, allowing you to compare your experimental spectrum with an MS/MS database.

First we open a connection to a MS/MS TSV file database:

msmsUrl <- system.file("extdata", "massbank_extract_msms.tsv", package='biodb')
msmsdb <- mybiodb$getFactory()$createConn('mass.csv.file', url=msmsUrl)

See 9 for content.


Table 9: Excerpt from MS/MS database TSV file.
accession formula ms.mode ms.level peak.mztheo peak.intensity peak.relative.intensity peak.formula msprecannot msprecmz peak.attr
AU200951 C7H5F3O neg 2 161.0238 38176 100.000000 C7H4F3O- [M-H]- 161.022 [M-H]-
AU200951 C7H5F3O neg 2 162.0274 1780 4.604605 C6[13]CH4F3O- [M-H]- 161.022 NA
AU200951 C7H5F3O neg 2 141.0167 616 1.601602 C7H3F2O- [M-H]- 161.022 NA
AU200952 C7H5F3O neg 2 161.0246 6180 100.000000 C7H4F3O- [M-H]- 161.022 [M-H]-
AU200952 C7H5F3O neg 2 141.0184 1384 22.322320 C7H3F2O- [M-H]- 161.022 NA
AU200952 C7H5F3O neg 2 121.0113 1180 19.019020 C7H2FO- [M-H]- 161.022 NA

Then we define an input spectrum:

input <- data.frame(mz=c(286.1456, 287.1488, 288.1514), rel.int=c(100, 45, 18))

The rel.int column contains relative intensity in percentage.

Finally we run the matching service by calling the msmsSearch() method:

matchDf <- msmsdb$msmsSearch(input, precursor.mz=286.1438, mz.tol=0.1, mz.tol.unit='plain', ms.mode='pos')
## INFO  [16:47:39.304] Loading file database "/tmp/RtmpmQ64Zk/Rinst19bbd64b4a5ea9/biodb/extdata/massbank_extract_msms.tsv".

The precursor.mz option sets the M/Z value for the precursor of your input spectrum. The mz.tol option defines the M/Z tolerance (by default in plain value, thus ±0.1 in our case). The mz.tol.unit option defines the mode use for the tolerance: either 'plain' or 'ppm'. The ms.mode option defines the MS mode of your input spectrum, either positive ('pos') or negative ('neg').

The results are displayed in 10. Each matching spectrum found in database is listed in the output data frame, along with a score and the number of the matched peak inside the database spectrum (the column names are the peak numbers of the input spectrum).


Table 10: Results of running spectrum matching service on an MS/MS database.
id score peak.1 peak.2 peak.3
AU158001 0.7804225 1 2 3
AU158002 0.7429446 1 2 4

See vignette

For more details on how to search for matching spectra inside an MS/MS database and explanations about the algorithm and the results.

7 Creating and improving connectors

A powerful feature of biodb is its architecture as a development framework. Connectors can be extended dynamically by created new rules to parse field values, or creating new fields. New connectors can also be defined. This feature has been used to create connectors to public databases like: KEGG, ChEBI, HMDB or UniProt.

See the vignettes Creating a new field for entries. and Creating a new connector. for details about connector creation and defining new entry fields.

8 Existing biodb extension packages

Several extension packages for biodb exist today on GitHub. See 11 for a list of those extension and a short description.

For installing them, please first make sure that you have the package devtools installed and run:

devtools::install_github('pkrog/biodbChebi', dependencies=TRUE, build_vignettes=TRUE)

Replace 'pkrog/biodbChebi' by the appropriate repository.

The extensions whose status is marked as Functional are in working order and can be installed and used safely with biodb. They may still need some updates in the documentation or the tests, thus do not hesitate to contact us if you have doubts on the API, the behaviour or if you would like to improve the extension. The extensions whose status is marked as In maintenance are currently non functional due to the refactoring of biodb into a development framework, but will be upgraded as soon as possible. If have the need to re-enable a currently in maintenance extension, do not hesitate to contact us, we may be able to accelerate the upgrade or propose you with our support to upgrade it yourself. If you have the desire to develop a new extension, please contact us, as we will be able accompany you in the process.

Table 11: Available biodb extensions
A list of currently available extensions with their description and their status.
Extension Database Status Description
biodbChebi ChEBI Functional Connector to ChEBI.
biodbKegg KEGG Functional Connectors to KEGG Compound, Enzyme, Genes, Module, Orthology, Pathway and Reaction.
biodbHmdb HMDB Functional Connector to HMDB Metabolites.
biodbUniprot UniProt Functional Connector to UniProt KB.
biodbExpasy ExPASy In maintenance Connector to ExPASy Enzyme.
biodbLipidmaps LIPID MAPS In maintenance Connector to Lipid Maps Structure.
biodbNcbi NCBI In maintenance Connectors to NCBI CCDS, Gene, PubChem Compound and PubChem Substance.
biodbMassbank MassBank In maintenance Connector to MassBank.
biodbNci NCI In maintenance Connector to NCI CACTUS.
biodbChemspider ChemSpider In maintenance Connector to ChemSpider.
biodbMirbase miRBase In maintenance Connector miRBase Mature.
biodbPeakforest PeakForest In maintenance Connectors to PeakForest Compound and PeakForest Mass.

9 Sources of documentation

Several vignettes are available. Among them you will find help for creating a new connector, adding an entry field to an existing connector, searching for compounds by mass or name, merging entries from different databases into a local database, annotation of a mass spectrum, etc. See 12 for a full list of available vignettes.


Table 12: List of biodb available vignettes with their short description.
Vignette Description
An introduction to biodb Introduction to the biodb package.
Details on biodb Details on general biodb usage and principles
Manipulating entry objects Manipulating entry objects
Creating a new connector. Creating a new connector class for accessing a database.
Creating a new field for entries. Creating a new field for entries.

You will also find documentation inside the R manual of the package. All biodb public classes have a help page. On each help page you will find a description of the class as well as a list of all its public methods with a description of their parameters. For instance you can get help on BiodbEntry class with ?BiodbEntry.

10 Closing biodb instance

When done with your biodb instance you have to terminate it, in order to ensure release of resources (file handles, database connection, etc):

mybiodb$terminate()
## INFO  [16:47:39.640] Closing BiodbMain instance... 
## INFO  [16:47:39.641] Connector "comp.csv.file" deleted. 
## INFO  [16:47:39.642] Connector "mass.csv.file" deleted. 
## INFO  [16:47:39.644] Connector "mass.csv.file.1" deleted.

11 Session information

sessionInfo()
## R Under development (unstable) (2021-10-19 r81077)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.15-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] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] biodb_1.3.0      BiocStyle_2.23.0
## 
## loaded via a namespace (and not attached):
##  [1] progress_1.2.2      tidyselect_1.1.1    xfun_0.27          
##  [4] bslib_0.3.1         purrr_0.3.4         vctrs_0.3.8        
##  [7] generics_0.1.1      htmltools_0.5.2     BiocFileCache_2.3.0
## [10] yaml_2.2.1          utf8_1.2.2          blob_1.2.2         
## [13] XML_3.99-0.8        rlang_0.4.12        jquerylib_0.1.4    
## [16] pillar_1.6.4        withr_2.4.2         glue_1.4.2         
## [19] DBI_1.1.1           rappdirs_0.3.3      bit64_4.0.5        
## [22] dbplyr_2.1.1        lifecycle_1.0.1     plyr_1.8.6         
## [25] stringr_1.4.0       memoise_2.0.0       evaluate_0.14      
## [28] knitr_1.36          fastmap_1.1.0       curl_4.3.2         
## [31] fansi_0.5.0         highr_0.9           Rcpp_1.0.7         
## [34] openssl_1.4.5       filelock_1.0.2      BiocManager_1.30.16
## [37] cachem_1.0.6        jsonlite_1.7.2      bit_4.0.4          
## [40] hms_1.1.1           chk_0.7.0           askpass_1.1        
## [43] digest_0.6.28       stringi_1.7.5       bookdown_0.24      
## [46] dplyr_1.0.7         tools_4.2.0         magrittr_2.0.1     
## [49] sass_0.4.0          RSQLite_2.2.8       tibble_3.1.5       
## [52] crayon_1.4.1        pkgconfig_2.0.3     ellipsis_0.3.2     
## [55] prettyunits_1.1.1   assertthat_0.2.1    rmarkdown_2.11     
## [58] httr_1.4.2          lgr_0.4.3           R6_2.5.1           
## [61] compiler_4.2.0

References

Carey, Vince. 2020. “HmdbQuery: Utilities for Exploration of Human Metabolome Database. R Package Version 1.10.0.” https://doi.org/10.18129/B9.bioc.hmdbQuery.

Carlson, Marc, and Csaba Ortutay. 2020. “UniProt.ws: R Interface to Uniprot Web Services. R Package Version 2.30.0.” https://doi.org/10.18129/B9.bioc.UniProt.ws.

Drost, Hajk-Georg, and Jerzy Paszkowski. 2017. “Biomartr: Genomic Data Retrieval with R.” Bioinformatics 33 (8): 1216–7. https://doi.org/10.1093/bioinformatics/btw821.

Guha, Rajarshi. 2016. “Rpubchem: Interface to the Pubchem Collection. R Package Version 1.5.10.” https://CRAN.R-project.org/package=rpubchem.

Soudy, Mohamed, Ali Mostafa Anwar, Eman Ali Ahmed, Aya Osama, Shahd Ezzeldin, Sebaey Mahgoub, and Sameh Magdeldin. 2020. “UniprotR: Retrieving and Visualizing Protein Sequence and Functional Information from Universal Protein Resource (Uniprot Knowledgebase).” Journal of Proteomics 213: 103613. https://doi.org/https://doi.org/10.1016/j.jprot.2019.103613.

Stravs, Michael A., Emma L. Schymanski, Heinz P. Singer, and Juliane Hollender. 2013. “Automatic Recalibration and Processing of Tandem Mass Spectra Using Formula Annotation.” Journal of Mass Spectrometry 48 (1): 89–99. https://doi.org/https://doi.org/10.1002/jms.3131.

Szöcs, Eduard, Tamás Stirling, Eric Scott, Andreas Scharmüller, and Ralf Schäfer. 2020. “Webchem : An R Package to Retrieve Chemical Information from the Web.” Journal of Statistical Software 93 (May). https://doi.org/10.18637/jss.v093.i13.

Tenenbaum, Dan, and Jeremy Volkening. 2020. “KEGGREST: Client-Side Rest Access to the Kyoto Encyclopedia of Genes and Genomes (Kegg). R Package Version 1.30.1.” https://doi.org/10.18129/B9.bioc.KEGGREST.

Winter, David, Scott Chamberlain, and Han Guangchun. 2020. “Rentrez: ’Entrez’ in R.” https://cran.r-project.org/web/packages/rentrez/.

Wolf, Raoul. 2019. “ChemSpider Api R Package.” https://github.com/NIVANorge/chemspiderapi.