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What is MONOCLdb?

The MOuse NOnCode Lung database — MONOCLdb — is an integrative and interactive database designed to retrieve and visualize annotations and expression profiles of long-non coding RNAs (lncRNAs) expressed in Collaborative Cross ( founder mice in response to respiratory influenza and SARS infections.

Long non-coding RNAs (lncRNAs) are endogenous cellular RNAs that are mRNA-like in length (> 200 nt) but which lack any positive-strand open-reading frames longer than 30 amino acids. LncRNAs are increasingly implicated in infectious disease, however, only a few have been functionally characterized for their role during viral infection. Using the 8 founder mice of the Collaborative Cross (, we quantified the expression of 20,728 mouse lncRNA genes, 5,329 of which were differentially expressed after influenza A virus (IAV) or severe acute respiratory syndrome coronavirus (SARS-CoV) infection. We annotated these differentially expressed lncRNAs by 2 computational methods (module-based and rank-based enrichments).

On-the-fly generation of expression heatmaps or generation of functional enrichment results (module-based enrichments, rank-based enrichments, associated GO term, associated IMMGEN module, associated REACTOME pathways) are possible using MONOCLdb. Co-expression networks and genomic networks of lncRNAs can also be visualized using MONOCLdb. Association scores between lncRNAs and pathogenicity variables are also provided by MONOCLdb. MONOCLdb also provides a Distributed Annotation System (DAS) service for visualization purposes of the non-coding genes associated with inferred biological functions (associated GO term, IMMGEN module, REACTOME pathways). Furthermore, MONOCLDb offers a web service to automatically retrieve analysis results.

Note : If you make use of the data presented here, please cite the following article in addition to the primary data sources:
Annotation of long non-coding RNAs expressed in Collaborative Cross founder mice in response to respiratory virus infection reveals a new class of interferon-stimulated transcripts. Josset L, Tchitchek N, Gralinski LE, Martin T. Ferris MT, Eisfeld AJ, Green R, Thomas MT, Tisoncik-Go J, Kawaoka Y, Pardo-Manuel de Villena F, Baric RS, Heise MT, Peng X, and Katze MG.

Is it possible to automatically query MONOCLdb?

Yes, you can easily automatically query the MONOCL database using HTTP requests.
Filtering gene signatures and analysis requests have to be sent using HTTP GET requests to the url.

Step 1 - Filtering Selection

First, send your list of non-coding/coding genes with the kind of filtering method you want to apply to the url using the GET atributes. The list of gene identifiers has to be formatted in a form of a string, where each gene id is separated by a coma ','. Please use the variable 'signature' to submit the gene signature and the variable 'filtering' to select the fitering method. A token session ID will be provided to you in the HTML response.
Please refer to the following table for the different available filtering methods.
Filtering method Value of GET['filtering']
NONCODE v3.0 gene identifiersnoncode_id
NONCODE genes associated with specific MONOCLdb modulesmonocldb_modules
NONCODE genes associated with specific GO termsgo_modules
NONCODE genes associated with specific IMMGEN modulesimmgen_modules
NONCODE genes correlating with specific genescorrelating
NONCODE genes in genomic neighborhood of specific genes (within the 200kb)genomic_neighborhood

Step 2 - Analysis Selection

Second, provide your token session ID and select your analysis method to the url, where GET['token'] is your session ID and GET['analysis'] is your analysis of interest.
Please refer to the following table for the different available analysis methods.
Analysis method Value of GET['analysis']
Expression Heatmapexpression_heatmap
Module-based Enrichmentmodule-based_enrichment
Rank-based Enrichmentrank-based_enrichment
Co-Expression Networkco-expression_network
Genomic Networkgenomic_network
Pathogenicity Associationpathogenicity_association

Using this procedure, you will be able to download all the different results.

Is there any example of script for automatically querying MONOCOLdb?

Bellow is a R script example that you can use for automatically querying of the MONOCOL database.

Please notice that you need to have the "RCurl" package installed.
library('RCurl') # load the RCurl package or install it if not present

signature <- c("n280959","n265692","n266006") # define a gene signature
filtering <- "noncode_id" # define the filtering method
analysis  <- "expression_heatmap" # define the analysis method

curl <- getCurlHandle() # create a Curl handler
tmpcookiefile <- tempfile("curl-cookie", fileext = ".txt") # define a temporary file for cookie session
curlSetOpt(cookiejar=tmpcookiefile, useragent = "RCurl", followlocation = TRUE, curl=curl) # define the user agent as "RCurl"

signature <- paste(signature, collapse=",") # string processing

# step 1. Submit the gene signature with the name of the filtering method, and retrieve the token ID
webobj <- getForm("",.params = c(signature=signature,filtering=filtering), curl=curl)
tokenid  <- webobj[1]

# step 2. Submit the session token ID with the name of the analysis method, and retrieve the analysis results
webobj <- getForm("",.params = c(token=tokenid,analysis=analysis), curl=curl)
result <- webobj[1]

# save and display results in a dataframe
tmpfile = tempfile("dataframe", fileext = ".txt")
write(result, file = tmpfile)
resultdf <- read.table(tmpfile, sep = "\t", header = TRUE)

How MONOCLdb has been designed?

The website of MONOCLdb was designed using Drupal (v 7.23), an open source Content Management System (CMS).
The core layer of MONOCLdb was designed using a collection of PHP (v 5.5.5), MySQL (v 5.6.14) and R/Bioconductor suite (v 3.0.2) scripts.
The visualization layer of MONOCLdb was designed using the D3.js - Data-Driven Documents (v 3.3.8) Javascript library.


The MONOCL database was designed and implemented by Laurence Josset and Nicolas Tchitchek in the Lab of Michael Katze ( in the Department of Microbiology at the University of Washington, USA.


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