
DrugBank Database XML Parser
Mohammed Ali, Ali Ezzat
2023-08-29
Source:vignettes/dbparser.Rmd
dbparser.Rmd
Introduction
The main purpose of the dbparser
package is to parse the DrugBank database which is downloadable in XML format from this link. The parsed data can then be explored and analyzed as desired by the user. In this tutorial, we will see how to use dbparser
along with dplyr
and ggplot2
along with other libraries to do simple drug analysis
Loading and Parsing the Data
Before starting the code we are assuming the following:
- user already downloaded DrugBank xml database file based on the Read Me instructions or the above note,
- user saved the downloaded database in working directory as
C:\
. - user named the downloaded xml file drugbank.xml.
Now we can loads the drugs
info, drug groups
info and drug targets
actions info.
## load dbparser package
library(dbparser)
library(dplyr)
library(ggplot2)
library(XML)
## parse data from XML and save it to memory
<- parseDrugBank(db_path = "C:\drugbank.xml",
dvobj drug_options = drug_node_options(),
parse_salts = TRUE,
parse_products = TRUE,
references_options = references_node_options(),
cett_options = cett_nodes_options())
## load drugs data
<- dvobj$drugs$general_information
drugs
## load drug groups data
<- dvobj$drugs$groups
drug_groups
## load drug targets actions data
<- dvobj$cett$targets$actions drug_targets_actions
Exploring the data
Following is an example involving a quick look at a few aspects of the parsed data. First we look at the proportions of biotech
and small-molecule
drugs in the data.
## view proportions of the different drug types (biotech vs. small molecule)
drugs %>%
select(type) %>%
ggplot(aes(x = type, fill = type)) +
geom_bar() +
guides(fill = FALSE) ## removes legend for the bar colors
Below, we view the different drug_groups
in the data and how prevalent they are.
## view proportions of the different drug types for each drug group
drugs %>%
full_join(drug_groups, by = c('primary_key' = 'drugbank_id')) %>%
select(type, group) %>%
ggplot(aes(x = group, fill = type)) +
geom_bar() +
theme(legend.position = 'bottom') +
labs(x = 'Drug Group',
y = 'Quantity',
title = "Drug Type Distribution per Drug Group",
caption = "created by ggplot") +
coord_flip()
Finally, we look at the drug_targets_actions
to observe their proportions as well.
## get counts of the different target actions in the data
targetActionCounts <-
drug_targets_actions %>%
group_by(action) %>%
summarise(count = n()) %>%
arrange(desc(count))
## get bar chart of the 10 most occurring target actions in the data
p <-
ggplot(targetActionCounts[1:10,],
aes(x = reorder(action,count), y = count, fill = letters[1:10])) +
geom_bar(stat = 'identity') +
labs(fill = 'action',
x = 'Target Action',
y = 'Quantity',
title = 'Target Actions Distribution',
subtitle = 'Distribution of Target Actions in the Data',
caption = 'created by ggplot') +
guides(fill = FALSE) + ## removes legend for the bar colors
coord_flip() ## switches the X and Y axes
## display plot
p