Skip to content
Statistical testing for overlap between expression quantitative trait loci and transcription factor location data (R code)
Branch: master
Clone or download

Latest commit

Fetching latest commit…
Cannot retrieve the latest commit at this time.


Type Name Latest commit message Commit time
Failed to load latest commit information.

eqtlchiptest: Software for statistical testing of the overlap between expression quantitative trait loci (eQTLs) and genome-wide transcription factor location data. This software accompanies the manuscript "Identifying cell type-specific transcription factors by integrating ChIP-seq and eQTL data--application to monocyte gene regulation", by Mudra Choudhury and Stephen Ramsey, which has been submitted to the Journal of Bioinformatics and Computational Biology.

Author: Mudra Choudhury, Oregon State University

From the lab of: Stephen Ramsey, Oregon State University (

Date: August 1, 2016

This software is distributed under the Apache Software License 2.0. Please see the file LICENSE for details on the software licensing agreement.

Usage notes: There are two R scripts in the subdirectory "R", that comprise this software release. The scripts load tab-delimited text datafiles, examples of which are given in the subdirectory "data". Each of the example datafiles contains the first ten lines of the actual datafile used in the analysis. The example datafiles have Unix line termination. The two R scripts are used in this order:

(1) Randomization.R (2) Calculate_Overlap.R

The "Randomization.R" script generates a file (UCSC BED format) of eQTLs that are randomly placed in the genome, within a set of allowed regions defined by the file "Background_Model.txt" (UCSC BED format). The sizes of the randomly placed eQTL regions are defined by the input file "Monocyte_LD_Blocks.bed".

The "Calculate_Overlap.R" script compares an input file of eQTL regions (in UCSC BED format) with an input file of ChIP-seq peak regions (in UCSC BED format) to compute the number of ChIP-seq peaks whose centers are within any of the eQTL regions. The sex chromosomes X and Y are excluded from the analysis (this exclusion is because when the authors used the SNAP tool to map eSNPs to proxy SNPs in order to define the eQTLs, proxy SNPs on the sex chromosomes were not returned by SNAP (see the above-referenced manuscript for more details).

You can’t perform that action at this time.