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  • What does genome-wide coverage look like for good MeDIP/ChIP enrichment?

    I have a MeDIP-seq experiment with inputs and enriched samples for several replicates. I'm not sure if we have good enrichment, so I want to look at the coverage genome-wide in the MeDIP-enriched samples versus the inputs. Looking at the alignments in IGV isn't helpful at the genome-wide scale, so I want to look at a histogram of the number of bases across the genome with coverage 1x, 2x, 3x, ... 100x .. etc. I got this information by using bedtools genomeCoverageBed.

    I would imagine such a plot would look like this, where enriched (MeDIP) samples have many regions of the genome with zero coverage, but more reads with very high coverage.



    When I actually look at this data, here's what I get (log scale on both axes):



    Zooming into the first part of the graph between 1x-100x shows a very slight difference in the distribution. Most of the "lower" curves are inputs, while most of the "upper" curves are enriched samples.



    My question is: I'm not sure if I have good enrichment or not - what should this look like for an experiment with good enrichment?

    I would have expected a much more noticeable difference between the genome-wide coverage of MeDIP'd samples and inputs. Has anyone ever produced this kind of plot for MeDIP-seq or ChIP-seq data?

    If you have aligned data, these plots can be produced easily:

    Code:
    # Data prep with Bedtools
    genomeCoverageBed -ibam sample1.bam -g hg19.fa.fai | grep genome > sample1.coverage.txt
    genomeCoverageBed -ibam sample2.bam -g hg19.fa.fai | grep genome > sample2.coverage.txt
    # ...etc
    
    # Visualization with R:
    # Read in all "*coverage.txt" files into a list
    # Collapse them into a data.frame
    bigdataframe <- do.call(rbind,  lapply(datalist, data.frame, stringsAsFactors=T))
    
    # plot
    library(ggplot2)
    qplot(V2,V5, colour=sample)

  • #2
    You could try enrichmentPlot in Repitools - an example using MBDCap-seq (similar to MeDIP-seq) is in section 3 of the manual.

    Comment


    • #3
      a good chipseq expt looks like this using your code

      Comment


      • #4
        @mudshark - thanks very much! it looks a bit different than my own. is this public data or is this your own? what factor were you ChIP'ing?

        Thanks.

        Comment


        • #5
          its a transcription factor (unpublished).

          i was thinking.. did you filter multiple hit reads? if not, these would explain the high coverages in input and MeDiP sample kind of obscuring the 'true' enrichment.

          Comment

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