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p-value for peak-calling using MACS

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  • p-value for peak-calling using MACS

    Hi I am trying to do peak-calling using MACS for some of my H3K4me3 ChIP-seq data. Using the default p-value of 1e-5 I only obtained 500+ peaks. But if I change the p-value to 1e-4 I got 1500+ peaks and by increasing it more to 1e-3 I got 7000+ peaks! My question is how do I know which is the best p-value to use? I would like to get more peaks but I also don't want any false positive peaks. Thanks for your help!

  • #2
    I am also very interested in knowing this because I am using MACS to do the peak calling too

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    • #3
      Depends how many false positives you're willing to handle. The default is 1E-5 and if your enrichment is good enough you should have many thousand peaks at that level.

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      • #4
        There is no best number and it completely depends on your data set. Load the data onto a genome visualizer and look at the peaks it is calling. Use your best judgement and validate by qPCR. Fold enrichment is another important and easy to understand parameter to consider.
        --------------
        Ethan

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        • #5
          How do you mean? Are you talking about the BAM file from Bowtie or the BED file from MACS? I find that the output from MACS is just a bar. On the other hand the BAM files are quite big and time out on me when I try to visualize them.

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          • #6
            dgrayson, if you use the -w option when running MACS you will generate a wig file which can be uploaded and visualized on a genome browser. I like to use the -w option with the -S option so that I only generate a single wig file instead of one for each chromosome.

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            • #7
              Thanks- I've tried that. I'm running MACS in a local Galaxy server which does not produce wig output files (MACS 1.4 in Galaxy). In addition this version of Galaxy does not allow us to view our output in UCSC browser.

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              • #8
                Maybe you can try using the web-based Galaxy? It allows you to generate a wig file, which is, in my opinion, really important for visualization of your chip datasets.

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                • #9
                  igv bam bai bed files

                  you can use IGV viewer. view bam ( samtools index to get bai file) of experiment control and the bed file to see an overall view.. bed will be bars of the peaks but bam with index will show tags and enrichment.

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