Tweet
Hello all,
I’m fairly new to the business of NGS data analysis. I was wondering whether anybody might have some advice or could suggest some resources for plotting average ChIP-seq profiles relative to genomic features such as transcription start sites and, particularly, enhancers. In my case, I’m looking to use such a plot to compare the H3K4me1 ChIP profiles between control and knockdown cells as they may occur relative to TSSs or enhancers.
Precisely what kind of files are required? A file for the total list of TSSs or gene transcript genomic regions found throughout the genome? A BED file corresponding to the H3K4me1 reads in control and knockdown cells? I’ve also noticed that with such read profiles (at least for certain histone mod reads plotted relative to TSSs) that some people group their reads together in 100bp windows and plot these relative to the TSS. How might I do something like this? Some people use average read density while others use cumulative reads occurring within such windows – Is using either the average read density or cumulative number of reads more advisable?
Also, how is it possible to make a BED file for something like enhancers detailing their genomic positions, or do we have to use surrogates like p300 binding sites and assume that these regions are likely to be associated with enhancers?
Thanks!
I’m fairly new to the business of NGS data analysis. I was wondering whether anybody might have some advice or could suggest some resources for plotting average ChIP-seq profiles relative to genomic features such as transcription start sites and, particularly, enhancers. In my case, I’m looking to use such a plot to compare the H3K4me1 ChIP profiles between control and knockdown cells as they may occur relative to TSSs or enhancers.
Precisely what kind of files are required? A file for the total list of TSSs or gene transcript genomic regions found throughout the genome? A BED file corresponding to the H3K4me1 reads in control and knockdown cells? I’ve also noticed that with such read profiles (at least for certain histone mod reads plotted relative to TSSs) that some people group their reads together in 100bp windows and plot these relative to the TSS. How might I do something like this? Some people use average read density while others use cumulative reads occurring within such windows – Is using either the average read density or cumulative number of reads more advisable?
Also, how is it possible to make a BED file for something like enhancers detailing their genomic positions, or do we have to use surrogates like p300 binding sites and assume that these regions are likely to be associated with enhancers?
Thanks!
Comment