How to correlate the uniquely mapped reads from two MNase-seq data sample. I have tried few methods. In one of them I have determined the per-base coverage in whole genome of MNase-seq read sample, then I normalized each position by dividing the total number of uniquely mapped reads, eventually use the cor and pairs functions of R (taking so much time).
I have also used deeptools for this comparison. Can any suggest more efficient way, how to correlate two MNase-seq samples.
Thanks in advance
You should use Danpos2. It's a Python2 based toolset developed specifically for MNase-seq (although it's said to work with ChIP-seq data also). Check out their website: https://sites.google.com/site/danposdoc/
Bear in mind that you can compare statistically your samples in just one step, getting p-values for occupancy, position shifts and fuzzines for each identified nucleosome position. Moreover, the script will automatically produce normalized .wig files, which can be easilly navigated in genome browsers.
One final remark: the results obtained from MNase treatments differ much, even between technical replicates. You should make replicates or otherwise you'll never be sure whether the differences you see originate from biological conditions or from technical variability of digestion.
Best regards,
Maciej
You may take a look at the following link, which is an online tool that generates nucleosome occupation maps from sequence reads generated by Chromatin digestion with micrococcal nuclease (MNase-seq).
http://nucleosome.usal.es/nucwave/
The following paper may also come to work.
http://www.biomedcentral.com/1471-2199/13/15
Best
Dear Vladimir Teif,
I am very familiar with the link you provided, and I think DANPOS2 has the parameters to compares two replicates (which I have not tried yet).
Here, I am attaching the heatmap which has the Pearson correlation values (determined by my method). But, unfortunately still not sure how to determine correlation in best possible way. Although, this method is somehow better than Deeptool software.
Best.
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