Requires: MACS2, pysam, R, python 3+.
Find the footprints within called peaks.
Requires peak positions in bed file format. MACS calls peaks in a bed file.
To include the summit positions use xls_to_bed.py to convert the MACS peaks xls file into a bed file.
Create a reads file with extract_reads.py.
extract_reads.py -p [peak position BED files] -a [sam/bam alignment files].
Set -s option if summit positions are included in bed file.
Set -o with a filename to write output to file
If you wish to extract reads from a custom window width set using -w option (100).
The reads file and peaks bed file will be the input to load_reads.R.
load_reads.R -r [reads file] -b [peaks BED file] Set -s if the BED file includes summit positions. Use -c [int n] to cut the data to the first n peaks.
load_reads.R creates data structures which are loaded by footprint.R.
footprint.R run an algorithm to find footprints.
Footprint is defined as the minimum position between 2 summits. Summits are defined as a local maximum that is above a threshold and that rises far enough above surrounding local minimums (prominence).
Can specify: Minimum number of reads for the peak to be considered. Summit threshold relative to max. Absolute summit threshold. Minimum prominence.
A footprints bed file wil be produced with the position of each footprint. The R data structures are also saved to allow you to explore your results.
Using Homer to find motifs which are over-represented in footprints.
Note: Requires Homer, bedtools, python 3+, R.
Choose a window from the centre to search. Can either be a fraction 0.0 to 1.0 or an integer > 1. Integers will give a fixed window size. A fraction will search that proportion of the footprint to neighbouring peak.
Run the search_motifs.sh script with the following arguments:
- The footprints bed file
- Your selected window size
- The reference genome in fasta format
- The set of known motifs to use <vertebrates|insects|worms|plants|yeast|all>
- Number of cpu cores to utilise
This will produce an output folder where the motifs can be browsed.
To investigate a motif, save the motif matrix *.motif file from homer.
Run the script find_motif.sh with the following arguments:
- The motif matrix file from homer
- The reference genome in fasta format
- The footprints bed file
- The number of cpu cores to utilise
This script will out put a number of images showing the relationship between motif and the footprints that it occupies. A _footprints.bed file is also output.
To investigte interactions between motifs run the compare_motifs.R script with the 2 _footprints.bed files as arguments. This will again output a number of images showing the relationship between the 2 motifs.
To investigate whether a motif is repeated run the repeated_motif.R with the _footprints.bed file as the argument.
