ChIPSeqCompare is a script that compares two ChIPSeq experiments, calculates overlapping regions using bedtools and provides distributions of normalized fold change (in a relative scale 0-100) for overlapping binding sites. It outputs a normalized fold change correlation plot indicating synergistic or antagonistic binding of two factors.
To run the script download the .sh file
wget https://raw.githubusercontent.com/milospjanic/ChIPSeqCompare/master/ChIPSeqCompare.sh chmod 755 ChIPSeqCompare.sh
Run the script providing the two ChIPSeq file names and the two TF name that will be plotted on the graph.
cat file.1 chr1 234413 234875 11.10171 chr1 437737 438028 5.28653 chr1 464787 465135 12.97743 chr1 531621 531932 4.16333 chr1 564665 566826 3.79419 chr1 567243 567748 6.21398 chr1 568692 569022 8.21949 chr1 569159 570390 16.70131 chr1 726773 727043 6.08584 chr1 755430 755639 4.75787 cat file.2 chr1 9987 10299 53.33 chr1 10335 10456 13.93 chr1 26041 26273 7.14 chr1 31792 32271 7.69 chr1 34189 34641 9.14 chr1 34678 35111 11.61 chr1 38548 38825 6.41 chr1 143851 144131 8.63 chr1 173502 173882 15.25 chr1 253268 253448 11.57 ./ChIPSeqCompare.sh file.1 file.2 TFX TFY
Example of a strong correlation between the binding of two factors indicating either synergistic binding to DNA of two factors (one factor helping the other one binds) or the presence of the two factors in a same protein complex. Third option is that these are members of the same TF family and that they stably interchange in the binding complex, hence at each bining site a stable equilibrium is achieved.
Second example is shows the absence of correlation, with points equally distribited in all areas of the graph.
Third example shows anticorrelation with binding sites appearing to be strongly anticorrelated (in red/green areas of the graph)
Two following examples show two possible artefacts of the ChIPSeq method. First one shows a full corelation for the two factors that are in the same binding complex (JUN, FOS). Fold changes could appear higher in one set when one antibody is stronger then the other one (or due to some other technical factor).
Finally when one dataset is saturated (sequenced to the maximum), each binding site will have consistently higher fold changes than the other data set. Hence, a systemic artefact represented as almost a vertical shape of the correlation.
