Hello all :)
Some histone marks are referred to as generating 'broad peaks', whilst others 'narrow peaks'. Others, like Pol-II, might be somewhere in between. Personally, I have had a really hard time really getting it clear in my head which marks cause which kinds of peaks, and what that distribution looks like genome-wide.
This got me thinking if there was a way to represent distribution of signal graphically, to illustrate the differences in IP efficiency between two antibodies, two experiments, two chromosomes, etc. Perhaps one experiment produces 'sharper', cleaner peaks than the other (new data compared to previously published stuff, for example).
You can do something like this with a FRiP score (fraction of reads in peaks), but it relies heavily on what is defined by the peak caller as a peak. When comparing two experiments, say in vivo to in vitro, the problem of pre-defined or overlapping peaks can become messy.
These charts are just my first attempt at plotting genome-wide signal distribution - no doubt there is probably some previously existing method/tool to do this which I have not yet come across, in which case i'd be very grateful to hear about how this is supposed to be done!
I couldn't decide between the following two representations of the same data (exact and cumulative), so I thought I would ask here to see what you guys/girls prefer. The data is made by just counting the frequency of signal-depth for every base in the genome. Signal-depth is just like read-depth, except it included the regions between read-pairs, no softclips, duplicates, etc etc - since this is ChIP data.
If you have any suggestions or criticisms, *please please please* let me know :)
UPDATE - fixed some of the text above, and added plots below for each chromosome. Note that the lines for X and Y look weird because they have a significantly lower chance of receiving signal, since all mice were male. MT is significantly small for a contig.
And below are plots for different replicates which used the exact same antibodies/etc.