Also the genome size parameter. It needs to be the number of uniquely mappable locations on the mitochondria.
I have a data set of chipseq and want to look for mitochondrial TF. During experiment mitochondria fractions were enriched.The data is SE 50 bp from Hiseq. I was wondering should I be using any regular paek calling like MACS, Find Peaks etc or there is a separate analysis flow / recommended analysis work flow for analyzing mitochondrial targets from Chipseq data. Thanks
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Looking from the other side I usually view mito (chrM) mapping reads as contamination and remove them as they cause errors in MACS statistical analyses, such as FDR calculation. So I am guessing that genomic DNA will be a contaminant for your analysis, in which case I would first map reads to the entire sequence set (mito and nuclear) and then remove nuclear mapping reads. You should then be able to use MACS etc. If you use MACS you would need to alter the lambda model parameters as it looks at genomic regions 1kb and 10kb away from candidate regions.
I hope this is of some help.
How we decide about lamda? Is there a rule of thumb? I mean it has to be more or less?
use tag autocorrelation to estimate lambda: http://biowhat.ucsd.edu/homer/chipseq/qc.html
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