Data from multiple ChIP-Seq studies for a particular transcription factor have been submitted to the GEO dataset in the wiggle format.
How does one go about correlating data from all these datasets? I would like to see if there are any regions conserved among all these datasets and by extension if they overlap with my ChIP-Seq data.
So far, I have used wig2bed (bedops) to convert wig files to bed and then intersect (Bedtools) them. However, I am doubtful if it is the right thing to do as the scores reported are in different formats for different studies. Some have probability scores, some tag numbers, some normalised reads per million.
In addition, I tried wigCorrelate (UCSC) among files but it does not give information about conserved regions. I'm not sure whether my approach here is correct.
Is there a way to identify peaks using wig files? Or is there a way to decide the threshhold for the scores?
Any other pointers to address the issue?
You do have to be mindful of what the scores in the wiggle files represent and whether or not read normalization has been performed. The original data should be available in GEO, so you might need to backtrack to alignment phase if the methods of data processing and wig file generation are not crystal clear, or not comparable across datasets.
For comparable wigs, use WiggleTools; see the following link, https://github.com/Ensembl/Wiggletools and attached pdf. You can use this tools to compute Pearson's correlation co-efficient, Wilcoxan's rank sum test, and quickly generate heatmaps to look at relationships across multiple ChIP-seq tracks. Overall, less cumbersome than coverting back to bed files. Also, intersections have limited utility as the output is binary (overlap or doesn't overlap) rather than testing the correlation of strength of binding.
Have a look at deeptools, it does exactly what you want in an elegant way:
http://deeptools.ie-freiburg.mpg.de/
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