The tool is designed for the identification of transcriptional regulatory modules (ETRMs) enriched within differentially methylated regions (DMRs). Given a set of DMRs and background regions, it uses HOMER’s known motif enrichment functionality to recursively identify TF motifs significantly enriched. The user has the option to cluster the DMRs into different subsets first or directly apply recursive ETRM identification on the entire set of DMRs. Clustering is based on WGCNA (Weighted Gene Correlation Network Analysis) algorithm but other clustering algorithms such as K-means may be used as well.
The user may use an existing motif database
OR motifs compiled in this study: copy and unzip the file "motifs.zip" to HOMER library/data/knownTFs/motifs folder
OR append the HOMER motif file together with the motif files provided:
For e.g., user can append the methylation-related motifs to HOMER's known motif files for vertebrates
(/home/bsharmi6/HOMER_custom/data/knownTFs/vertebrates/known.motifs)
The appended motif files are provided under Motif_db/all.motifs and Motif_db/known.motifs for convenience. The user can replace the known vertebrates motif files in HOMER with these two motif files.
An snapshot of the motif database containing a few methylated motifs in addition to HOMER's known motifs (e.g. path = /home/bsharmi6/HOMER_custom/data/knownTFs/motifs/) is given below -
"dplyr", "WGCNA", "pheatmap", "igraph", "gdata"
- Code/Shell_script/shell_script_local: to run the commands on a local system
- Code/Shell_script/shell_script_server: for users who wish to run the tool on a server
- Code/R_script: R scripts needed by the tool
- Examples: sample data and output provided as demo
- Motif database: Motif libraries compiled from HOMER and other public databases (for details see the paper)
Please download a genome fa file (e.g. mm10.fa). It is a large file and has not been provided here. Clone the Github repository to a local system or a server
./run_DMR_clustering_script.sh -R –o -d <3 column BED format DMR file> -x -db <3 column BED format DMR background file>
Example on demo data:
cd Code/Shell_script/shell_script_local/
./run_DMR_clustering_script.sh -R Code/R_script/DMR_clustering_general.R -o Examples/Output/ -d Examples/DMR.txt -x Examples/Methylation_matrix.txt -b Examples/DMR_background.txt
Explanation of command:
-o: path to store the output
-d: tab delimited three column BED format DMR file with three columns: chromosome, start, end
-x: methylome matrix. One column indicates methylome values for DMR location, one column indicates methylome values for one sample. Number of rows of x must match with the number of rows in the DMR file
-b: tab delimited three column BED format DMR background file to create background regions for each DMR cluster
-R: R script used for clustering
-h: explains required parameters
The command will cluster DMRs, and create a folder for each cluster name containing the tab delimited DMRs files and the corresponding background files
Since WGCNA can be slow on large DMR matrices, it might be efficient to run the scripts by submitting as jobs on a high performance computing system. The script below shows how to run the scripts on server -
sbatch --export=Rpath=Code/R_script/DMR_clustering_general.R,outpath=Examples/Output/,x=Examples/Methylation_matrix.txt,dms=Examples/DMR.txt,dms_background=Examples/DMR_background.txt R_clustering_general.sbatch
./recursive_motif_identification_noclustering.sh –i -H -r -target -background -s -R
Example on demo data:
cd Code/Shell_script/shell_script_local/
./recursive_motif_identification_noclustering.sh -i Examples/ -H /home/bsharmi6/HOMER_custom/ -r mm10.fa -t DMR.txt -b DMR_background.txt -s Code/Perl_script/sequence_extractor.pl -R Code/R_script/
Explanation of command:
-i: path containing the DMR file
-H: path to HOMER database
-s: sequence extractor Perl script
-R: path to R script files
-t: DMR matrix
-b: DMR background matrix
-r: path containing the reference fasta file; examples are mm10.fa, hg19.fa etc.
-h: explains required parameters \
sbatch --export=idir=Examples/,Hpath=/home/bsharmi6/HOMER_custom/,refpath=/home/bsharmi6/mm10bowtie2/mm10.fa,seqextractpath=Code/Perl_script/sequence_extractor.pl,Rpath=Code/R_script/,target=DMR.txt,background=DMR_background.txt recursive_motif_identification_noclustering.sbatch
./recursive_motif_identification_clustering.sh –i -H -r -s -R
Example on demo data:
cd Code/Shell_script/shell_script_local/
./recursive_motif_identification_clustering.sh -i Examples/ -H /home/bsharmi6/HOMER_custom/ -r mm10.fa -s Code/Perl_script/sequence_extractor.pl -R Code/R_script/
Explanation of command:
-i: path containing the DMR clusters
-H: path to HOMER database
-s: sequence extractor Perl script
-R: path to R script files
-r: path containing the reference fasta file; examples are mm10.fa, hg19.fa etc
-h: explains required parameters
sbatch --export=idir=Examples/,Hpath=/home/bsharmi6/HOMER_custom/,refpath=/home/bsharmi6/mm10bowtie2/mm10.fa,seqextractpath=Code/Perl_script/sequence_extractor.pl,Rpath=Code/R_script/ recursive_motif_identification_clustering.sbatch
Contact: bsharmi6@vt.edu