Ugo Borello <ugo.borello at="" ...=""> writes:
> Dear Rory,
> I have few basic questions about ChiPQC.
> - Could I use ChiPQC with paired-end data?
> - I would like to use my mouse blacklist
> MOUSE (mm9):
> I noticed that the formatting of this mm9-blacklist.bed is different
> your blacklist_hg19.bed. For example your file has an header, not
> Is it correct then to provide only a filename to my blacklist, when
> ChIPQC(), or is it better to import the bed file as Granges object
> - If I understand correctly, while constructing a ChIPQCexperiment
> and setting consensus=TRUE, a consensus peak set is generated. This
> consensus peak set consists of the peaks overlapping at least two of
> replicate samples, correct?
> Is it possible to access this consensus peak set?
> Thank you for your help and for this very useful tool,
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> Bioconductor at ...
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Sorry for the delay in reply.
Supplying a GRanges object should work well and typically what I do.
ChIPQC expects there to be a header when reading from a file so your
file should be good.
You could use paired-end data with ChIPQC but for now this would
require you to filter your Bam file for only "first read in pair" in
to avoid double counting and biasing the cross-coverage scores plots.
This should be straightforward to do using something like samtools
In the future I do want to include paired-end data analysis for ChIPQC
to accommodate MNAse-seq. I'm not sure how Diffbind handles paired
end data, perhaps Rory has an idea?
To retrieve consensus sets you could take the DBA object from
ChIPQCexperiment and work from there using the Diffbind package.
dbaobject = ChIPQCexperimentObject at DBA
Head of Bioinformatics, MRC Clinical Sciences Centre, Faculty of
Imperial College London,Hammersmith Hospital Campus, Du Cane