dbparser

Overview

Drugs databases vary too much in their formats and structures which making related data analysis not a very easy job and requires a lot of efforts to work on only two databases together such as DrugBank, OnSIDES, and TWOSIDES.

Hence, dbparser package aims to parse different public drugs databases into a single and unified format R object called dvobject (stands for drugverse object).

With recent updates, dbparser has evolved into an integration engine, allowing you to merge mechanistic data (DrugBank) with real-world phenotypic data (OnSIDES) and drug-drug interaction risks (TWOSIDES).

That should help in:

• working with single data object and not multiple databases in different formats,
• using R analysis capabilities easily on drugs data,
• ease of transferring data between researchers after performing required data analysis or dvobject and storing results in the same object in a very easy manner.

dvobject Structure

dvobject introduces a unified and compressed format of drugs data. It is an R list object.

For a single database (e.g., DrugBank): It contains one or more of the following sub-lists:

• drugs: list of data.frames that contain drugs information (i.e. synonyms, classifications, …) and it is the only mandatory list
• salts: data.frame contains drugs salts information
• products: data.frame of commercially available drugs products in the world
• references: data.frame of articles, links and textbooks about drugs or CETT data
• cett: list of data.frames contain targets, enzymes, carriers and transporters information

For a merged database (Integrated Pharmacovigilance): When databases are merged using merge_drugbank_onsides or merge_drugbank_twosides, the dvobject becomes a nested structure containing:

• drugbank: The mechanistic hub.
• onsides: The side-effect data (from FDA labels).
• twosides: The drug-drug interaction data.
• integrated_data: Enriched tables that bridge the databases (e.g., linking DrugBank IDs to OnSIDES adverse events).
• metadata: Detailed provenance for all contained datasets.

Drug Databases

Parsers are available for the following databases (it is in progress list)

1. DrugBank

DrugBank database is a comprehensive, freely accessible, online database containing information on drugs and drug targets. As both a bioinformatics and a cheminformatics resource, DrugBank combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. sequence, structure, and pathway) information. More information about DrugBank can be found here.

In its raw form, the DrugBank database is a single XML file. Users must create an account with DrugBank and request permission to download the database. Note that this may take a couple of days.

The dbparser package parses the DrugBank XML database into R tibbles that can be explored and analyzed by the user, check this tutorial for more details.

If you are waiting for access to the DrugBank database, or do not intend to do a deep dive with the data, you may wish to use the dbdataset package, which contains the DrugBank database already parsed into dvobject. Note that this is a large package that exceeds the limit set by CRAN. It is only available on GitHub.

dbparser is tested against DrugBank versions 5.1.0 through 5.1.12 successfully. If you find errors with these versions or any other version please submit an issue here.

2. OnSIDES (The Phenotype)

OnSIDES provides adverse drug events extracted from thousands of FDA drug labels using machine learning. * Parser: parseOnSIDES() * Input: Directory containing OnSIDES CSV files.

3. TWOSIDES (Polypharmacy)

TWOSIDES provides data on drug-drug interactions and the adverse events that arise when two drugs are taken together. * Parser: parseTWOSIDES() * Input: The TWOSIDES.csv.gz file.

Quick Start: Integration Pipeline

The power of dbparser lies in its ability to chain parsers and mergers together. Here is how you can build a complete pharmacovigilance dataset:

library(dbparser)
library(dplyr)

# 1. Parse the raw databases
drugbank_db <- parseDrugBank("data/drugbank.xml")
onsides_db  <- parseOnSIDES("data/onsides/")
twosides_db <- parseTWOSIDES("data/TWOSIDES.csv.gz")

# 2. Build the Integrated Knowledge Graph
#    DrugBank serves as the hub. We chain the merges.
final_db <- drugbank_db %>% 
  merge_drugbank_onsides(onsides_db) %>% 
  merge_drugbank_twosides(twosides_db)

# 3. Analyze Results
#    Example: Accessing the enriched drug-drug interaction table
head(final_db$integrated_data$drug_drug_interactions)

For a detailed case study, please refer to the Integrated Pharmacovigilance Vignette.

Installation

You can install the released version of dbparser from CRAN with:

install.packages("dbparser")

or you can install the latest updates directly from the repo

library(devtools)
devtools::install_github("ropensci/dbparser")

Code of Conduct

Please note that the ‘dbparser’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Contributing Guide

👍🎉 First off, thanks for taking the time to contribute! 🎉👍 Please review our Contributing Guide.

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Using dbparser for a paper you are writing? Consider citing it

citation("dbparser")
#> To cite dbparser in publications use:
#> 
#>   Mohammed Ali, Ali Ezzat ().  dbparser: DrugBank Database XML Parser.
#>   R package version 2.2.0.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {DrugBank Database XML Parser},
#>     author = {Mohammed Ali and Ali Ezzat},
#>     organization = {Interstellar for Consultinc inc.},
#>     note = {R package version 2.2.0},
#>     url = {https://CRAN.R-project.org/package=dbparser},
#>   }