DrugBank Database XML Parser
Mohammed Ali, Ali Ezzat
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
ggplot2 along with other libraries to do simple drug analysis
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
- user named the downloaded xml file drugbank.xml.
Now we can loads the
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$actionsdrug_targets_actions
Following is an example involving a quick look at a few aspects of the parsed data. First we look at the proportions of
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