Hi I have a question, I have Drosophila genome spike-in in a chip-seq experiment. Does anybody have experience on the best way to normalised and process those samples to generate bw and count matrix? ( I am using deeptools) 

I am following the instruction of the vendor: 

map reads to the sample genome (human, mouse, etc.)
map reads to the DM genome
count unique DM sequence tags. Identify sample with least amount of tags
Count DM tags from other samples. Create a normalization factor = lowest_sample/sample_of_interest

then I use the normalisation factor ${SPIKE} in deeptools as follow:

bamCoverage -b ${SAMPLE}/${SAMPLE}.nodup.norm.bam -o /scratch/${PBS_JOBID}/${SAMPLE}/${SAMPLE}.bw \
			-p 1 --normalizeTo1x ${genomesize} \
			--scaleFactor ${SPIKE} \
			--ignoreForNormalization chrX chrY \
			-bl ${ENCODEBED} \
			--smoothLength 40 \
			--binSize 10 \
			--centerReads --extendReads 150

Since I am getting a modest increase, and the vendor results are a modest decrease of signal I am wondering if I am doing something wrong with bamCoverage --scaleFactor argument or if the discrepancy is due to the different processing of the bw between my pipeline (deeptools based) and the vendor one (custom made).

Thanks a lot for your support  and advice
 Did the vendor instructions mention that they also normalized to 1x? That could be the problem...

It's possible that you should only be applying the scaleFactor alone and not also normalizing to 1x. If you apply only the scaleFactor (i.e. run that command without the --normalizeto1x argument), would you see a modest decrease of signal as the vendor does?

The [deepTools google group][1] is another great resource for questions like this one, and they may be able to help more.


  [1]: https://groups.google.com/forum/#!forum/deeptools