1 Introduction

1.1 The Comparative Toxicogenomics Database

The Comparative Toxicogenomics Database (CTDbase; is a public resource for toxicogenomic information manually curated from the peer-reviewed scientific literature, providing key information about the interactions of environmental chemicals with gene products and their effect on human disease [1][2]. CTDbase is offered to public by using a web-based interface that includes basic and advanced query options to access data for sequences, references, and toxic agents, and a platform for analysing sequences.

1.2 CTDquerier R package

CTDquerier is an R package that allows to R users to download basic data from CTDbase about genes, chemicals and diseases. Once the user’s input is validated allows to query CTDbase to download the information of the given input from the other modules.

CTDquerier can be installed using devtools. To install CTDquerier run the following command in an R session:

if (!requireNamespace("BiocManager", quietly=TRUE))

Once installed, CTDquerier should be loaded running the following command:

library( CTDquerier )

The main function of CTDquerier are three depending of the input: genes, chemicals or diseases. Table 1 indicates the proper function to be used to query CTDbase depending on the input.

Table 1: Main functions of CTDquerier, designed to accept a specific input
Input Function
Genes query_ctd_gene
Chemicals query_ctd_chem
Diseases query_ctd_dise

The function to query CTDbase relies on a set of function that download the specific vocabulary of each input. Table 2 shows the different functions that are used to download the specific vocabulary and to load it into R. This process is transparent to user since it is encapsulated into each one of the query functions.

Table 2: Functions used to download and load specific vocabulary from CTDbase
Input Load Function Download Function
Genes load_ctd_gene download_ctd_genes
Chemicals load_ctd_chem download_ctd_chem
Diseases load_ctd_dise download_ctd_dise

The three main functions of CTDquerier returns CTDdata objects. These objects can be used to plot the information available in CTDbase by using plot. Moreover, the informatin from CTDbase can be extracted as data.frames using the method get_table. Both plot and extract methods needs an argument index_name that indicates the table to be ploted or extarcted. Table 3 shows the relation between the possible options for index_name depeting of the query performed. Also the pssible representation of each table.

Table 3: Relation of the accessors and representation of each table in a CTDdata object depending of the input
Accessor Genes Chemicals Diseases
gene interactions heat-map/network heat-map
chemical interactions heat-map heat-map
diseases heat-map heat-map
gene-gene interactions heat-map/network
kegg pathways network heat-map network
go terms network heat-map

2 Querying CTDbase

2.1 … by gene

To query CTDbase for a given gene or set of genes, we use the function query_ctd_gene:

args( query_ctd_gene )
## function (terms, verbose = FALSE) 

The argument terms is the one that must be filled with the list of genes of interest. The argument filename is filled with the name that will receive the table with the specific vocabulary from CTDbase for genes. The function checks if this file already exists, if is the case it used the local version. The argument mode is used to download the vocabulary file (for more info., check download.file from module utils). Finally, the argument verbose will show relevant messages about the querying process if is set to TRUE.

A typical gene-query follows:

ctd_genes <- query_ctd_gene( 
    terms = c( "APOE", "APOEB", "APOE2", "APOE3" , "APOE4", "APOA1", "APOA5" ) )
## Warning in .get_cache(): /home/biocbuild/.cache/CTDQuery
## Using temporary cache /tmp/RtmpRHEOpp/BiocFileCache
## Warning in .get_cache(): /home/biocbuild/.cache/CTDQuery
## Using temporary cache /tmp/RtmpRHEOpp/BiocFileCache
## 1/tmp/RtmpRHEOpp/BiocFileCache/592453443d9_CTD_genes.tsv.gz
## Warning in load_ctd_gene(): 1/tmp/RtmpRHEOpp/BiocFileCache/
## 592453443d9_CTD_genes.tsv.gz
## 1/tmp/RtmpRHEOpp/BiocFileCache/592453443d9_CTD_genes.tsv.gz
## Warning in load_ctd_gene(): 1/tmp/RtmpRHEOpp/BiocFileCache/
## 592453443d9_CTD_genes.tsv.gz
## Warning in query_ctd_gene(terms = c("APOE", "APOEB", "APOE2", "APOE3",
## "APOE4", : 2/7 terms were dropped.
## Object of class 'CTDdata'
## -------------------------
##  . Type: GENE 
##  . Length: 5 
##  . Items: APOE, ..., APOA5 
##  . Diseases: 2215 ( 5089 / 5715 )
##  . Gene-gene interactions: 192 ( 232 )
##  . Gene-chemical interactions: 607 ( 1539 )
##  . KEGG pathways: 59 ( 59 )
##  . GO terms: 353 ( 355 )

As can be seen, query_ctd_gene informs about the number of terms used in the query and the number of terms lost in the process. To know the exact terms that were found in CTDbase and the ones that were lost, we use the method get_terms.

get_terms( ctd_genes )
## $found
## [1] "APOE"  "APOEB" "APOE2" "APOA1" "APOA5"
## $lost
## [1] "APOE3" "APOE4"

2.1.1 Extract Tables

Now that the information about the genes of interest was download from CTDbase we can access to it using the method get_table. Method extract allows to access to different tables according to the origin of the object. For a created from genes the accessible tables are:

Table Available Accessors
Gene Interactions NO "gene interactions"
Chemicals Interactions YES "chemical interactions"
Diseases YES "diseases"
Gene-Gene Interactions YES "gene-gene interactions"
Pathways (KEGG) YES "kegg pathways"
GO (Gene Ontology Terms) YES "go terms"

Example of how to extract one of this tables follows:

get_table( ctd_genes , index_name = "diseases" )[ 1:2, 1:3 ]
## DataFrame with 2 rows and 3 columns
##                             Disease.Name   Disease.ID  Direct.Evidence
##                              <character>  <character>      <character>
## 1 Chemical and Drug Induced Liver Injury MESH:D056486 marker/mechanism
## 2                        Atherosclerosis MESH:D050197 marker/mechanism

The information stored in each table can be see in the following code, were the names of the columns of each table is shown:

colnames( get_table( ctd_genes, index_name = "chemical interactions" ) )
## [1] "Chemical.Name"       "Chemical.ID"         "CAS.RN"             
## [4] "Interaction"         "Interaction.Actions" "Reference.Count"    
## [7] "Organism.Count"      "GeneSymbol"          "GeneID"
colnames( get_table( ctd_genes, index_name = "diseases" ) )
## [1] "Disease.Name"      "Disease.ID"        "Direct.Evidence"  
## [4] "Inference.Network" "Inference.Score"   "Reference.Count"  
## [7] "GeneSymbol"        "GeneID"
colnames( get_table( ctd_genes, index_name = "gene-gene interactions" ) )
##  [1] "Source.Gene.Symbol" "Source.Gene.ID"     "Target.Gene.Symbol"
##  [4] "Target.Gene.ID"     "Source.Organism"    "Target.Organism"   
##  [7] "Assay"              "Interaction.Type"   "Throughput"        
## [10] "Reference.Authors"  "Reference.Citation" "PubMed.ID"         
## [13] "GeneSymbol"         "GeneID"
colnames( get_table( ctd_genes, index_name = "kegg pathways" ) )
## [1] "Pathway"    "Pathway.ID" "GeneSymbol" "GeneID"
colnames( get_table( ctd_genes, index_name = "go terms" ) )
## [1] "Ontology"             "Qualifiers"           "GO.Term.Name"        
## [4] "GO.Term.ID"           "Organisms..Evidence." "GeneSymbol"          
## [7] "GeneID"

2.1.2 Plotting Gene Created CTDdata Objects

The generic plot function has the same mechanism that get_table. Using the argument index_name we select the table to plot. Then, the arguments subset.gene and subset.* (being * chemicals, diseases, pathways and go) allows to filter the X-axis and Y-axis. Depending the table to be plotted, the argument field.score can be used to select the field to plotted (that can takes "Inference" or "Reference" values). Then argument filter.score can be used to filter entries of the table. Finally, the argument max.length is in charge to reduce the characters of the labels.

The following plot shows the number of reference that cites the association between the APOE-like genes and chemicals.

plot( ctd_genes, index_name = "chemical interactions", filter.score = 3 )

Then, next plot shows shows the inference score that associates the APOE-like genes with diseases according to CTDbase.

plot( ctd_genes, index_name = "disease", filter.score = 115 )

The plot to explore the gene-gene interactions is based in a network representation. The genes from the original set are dark-coloured, while the other genes are light-coloured.

plot( ctd_genes, index_name = "gene-gene interactions", 
    representation = "network", main = "APOE-like gene-gene interactions" )