dgrapov/ctsgetr repository overview
R interface to the Chemical Translation Service (CTS)
CTSgetR provides a consitent interface to translation of chemical names and over 200 database identifiers including InChIKey, HMDB, KEGG and PubChem. Translation of chemical names is hard. Use CTSgetR to robustly translate chemical names to other identifiers through 1) conversion to InChIKey 2) biological or popularity scoring and 3) translation to over 200 biological database identifiers. CTSgetR uses a sqlite database to cache and speed all of your routine translations.
This package supports metabolite identifier translation:
using R
Installation
Make sure CTS API is available
View some of the possible translation options between > 200 databases
## [1] "BioCyc" "CAS"
## [3] "ChEBI" "Chemical Name"
## [5] "Human Metabolome Database" "InChIKey"
## [7] "KEGG" "LMSD"
## [9] "LipidMAPS" "PubChem CID"
Find a database of interest
## [1] "PubChem CID"
Initialize a local database to speed up routine queries
## [1] "Creating a new database"
## [[1]]
## translation n
## 1 Chemical Name <--> InChIKey 10
## 2 Human Metabolome Database <--> InChIKey 1
## 3 InChIKey <--> KEGG 6
## 4 InChIKey <--> PubChem CID 4
## 5 KEGG <--> PubChem CID 22
##
## $total
## [1] 43
Translation examples
Chemical Name to InChIKey
## id from to key
## 1 alanine Chemical Name InChIKey QNAYBMKLOCPYGJ-REOHCLBHSA-N
## 2 lactic acid Chemical Name InChIKey JVTAAEKCZFNVCJ-UHFFFAOYSA-N
One identifier to many
The example below shows the alternative data.frame input format for more complex queries.
## id from to
## 1 alanine Chemical Name PubChem CID
## 2 lactic acid Chemical Name PubChem CID
## 3 alanine Chemical Name KEGG
## 4 lactic acid Chemical Name KEGG
## 5 alanine Chemical Name Human Metabolome Database
## 6 lactic acid Chemical Name Human Metabolome Database
## id from to key
## 1 alanine Chemical Name Human Metabolome Database HMDB0000161
## 2 lactic acid Chemical Name Human Metabolome Database HMDB0144295
## 3 alanine Chemical Name KEGG C00041
## 4 lactic acid Chemical Name KEGG C01432
## 5 alanine Chemical Name PubChem CID 5950
## 6 lactic acid Chemical Name PubChem CID 19789253
Many identifiers to one
Build up complex queries by combingn data frames of id, from to to values.
## id from to
## 1 alanine Chemical Name PubChem CID
## 2 lactic acid Chemical Name PubChem CID
## 3 alanine Chemical Name KEGG
## 4 lactic acid Chemical Name KEGG
## 5 alanine Chemical Name Human Metabolome Database
## 6 lactic acid Chemical Name Human Metabolome Database
## 7 HMDB0000161 Human Metabolome Database KEGG
## 8 HMDB0000161 Human Metabolome Database PubChem CID
## id from to key
## 1 alanine Chemical Name Human Metabolome Database HMDB0000161
## 2 lactic acid Chemical Name Human Metabolome Database HMDB0144295
## 3 alanine Chemical Name KEGG C00041
## 4 lactic acid Chemical Name KEGG C01432
## 5 alanine Chemical Name PubChem CID 5950
## 6 lactic acid Chemical Name PubChem CID 19789253
## 7 HMDB0000161 Human Metabolome Database KEGG C00041
## 8 HMDB0000161 Human Metabolome Database PubChem CID 5950
Deploy
CTSgetRas adockerizedAPI
The following docker image and docker-compose commands can be used to build and run the `CTSg
etRpackage as an [opencpu](https://hub.docker.com/r/opencpu/ubuntu-18.04) basedAPI`.
-
CTSgetR image
-
ocpuclient: client library for accessign
CTSgetRAPI
The CTSgetR image contains an opencpu and Rstudio server
-
localhost/ocpu/: opencpu-server -
localhost/rstudio/: rstudio server (use user: opencpu and password: )
Build docker image
build
export rstudio_pass=mypassword # rstudio server password for user opencpu
docker-compose -f docker-compose.yml build --force-rm
Launch API
#mount to persist internal sqlite DB between updates
export ctsgetr_db_mount=<local path to save database e.g. /mypath>
docker-compose -f docker-compose.yml up -d
Test API endpoints
bash
curl http://localhost/ocpu/library/CTSgetR/R/heartbeat
R
heartbeat
library(ocpuclient)
base_url<-'http://localhost/ocpu/'
endpoint<-'library/CTSgetR/R/heartbeat'
url<-paste0(base_url,endpoint)
post_ocpu(url=url)
translation
#translate
endpoint<-'library/CTSgetR/R/CTSgetR'
url<-paste0(base_url,endpoint)
id <-
c("C15973",
"C00026")
from <- "KEGG"
to <- "PubChem CID"
body<-list(id=id,from=from,to=to,db_name=db_name)
post_ocpu(url=url,body=body)
Launch
shinyUI using asynchronousopencpuAPI
The following example shows a how to use a shiny module combined with futures and promises R packages to connect to an opencpu API.
library(shiny)
library(tippy)
library(CTSgetR) # local calls
library(ocpuclient) # CTSgetR opencpu API calls
Specify local database or API options
#one of local
Sys.setenv('ctsgetr_DB'='inst/ctsgetr.sqlite') #see section `in R` showing how to initialize a local databse
#or API
Sys.setenv('ctsgetr_DB'='/ctsgetr/inst/ctsgetr.sqlite') # in API docker for mount
Sys.setenv('CTSgetR_API'='http://localhost/ocpu/library/CTSgetR/R/CTSgetR') # url of API endpoint
User input translations
library(promises)
library(future)
plan(multisession)
#module
ui <- fluidPage(
sidebarLayout(position = "left",
sidebarPanel(tagList(mod_CTSgetR_ui("translate"))),
mainPanel(verbatimTextOutput("main_out")))
)
server <- function(input, output, session) {
translation <- mod_CTSgetR_server('translate')
output$main_out <- renderPrint({
translation() %...>% print(.)
})
}
shinyApp(ui, server)
Connect to other shiny components
library(promises)
library(future)
plan(multisession)
example<-data.frame('chemical_name' = c('alanine','DMT'))
#module
ui <- fluidPage(
sidebarLayout(position = "left",
sidebarPanel(tagList(mod_CTSgetR_ui("translate"))),
mainPanel(verbatimTextOutput("main_out")))
)
server <- function(input, output, session) {
#make `example` a reactive returning a data frame to update dynamically
translation <- mod_CTSgetR_server('translate',data=example)
output$main_out <- renderPrint({
translation() %...>% print(.)
})
}
shinyApp(ui, server)
Tag summary
Content type
Image
Digest
Size
810.1 MB
Last updated
about 6 years ago
docker pull dgrapov/ctsgetr