Unconfigured Ad

Collapse
X
 
  • Time
  • Show
Clear All
new posts
  • lwhitmore
    Member
    • Aug 2013
    • 70

    #1

    MACS Chip Seq Biological Replicates

    Hey All,
    I have 2 biological replicates for a treatment chip seq experiment. When I analyze these 2 replicates independently (using MACS 1.4.2) I get about 3000 peaks identified for both replicates however when I merge them together I only get about 2000. How exactly does merging the replicates together change how MACS identifies peaks?
    Any help would be appreciated
    Leanne
  • jennyal
    Junior Member
    • Dec 2014
    • 3

    #2
    The same 'problem'

    Hi!
    I wonder if you got an answer to your problem with 'loosing' peaks when merging files?
    We are experiencing the same thing.

    Thanks!
    Jenny

    Comment

    • lwhitmore
      Member
      • Aug 2013
      • 70

      #3
      Hi,
      Unfortunately I did not ever get an answer to that question.

      Sorry, If you end up finding out why you should let me know

      Leanne

      Comment

      • rory
        Member
        • Aug 2008
        • 28

        #4
        I'll ChIP in on this one...

        In general, it is not a good idea to combine the reads from biological replicates. One reason is that you lose the ability to distinguish the biological and experimental variability inherent in ChIP experiments. This is similar to the reason you wouldn't combine all the reads from biological replicates in an RNA-seq experiment. Remember we expect ChIP experiments to exhibit even greater variability than RNA!

        The other reason is that it tends to break peak calling, as you discovered. Peak calling involves separating signal (peaks) from noise (background). In a ChIP, the majority of reads are background. So when you combine replicate data, the background levels all get higher and/or more filled in, which makes it in general more difficult to distinguish real peaks. If the replicates lined up cleanly -- for example technical replicates -- the peaks would fill in as well, but the biological and experimental variance means that the peaks just tend to either be a) relatively "lower"/less dense or b) less well-formed (messier "peaks"). The good aspect of this is that any peaks that are able to be identified (your reduced set of 2000) should have a lower false discovery rate.

        For a two-replicate experiment, you can follow the ENCODE guidelines to call peaks separately and combine them into a low-FDR set using the IDR calculation. I'd expect this to give even less than 2000 peaks as it can be quite a stringent test. It may also help to look at the overlap of the two peaksets to see how similar the 3,000 peaks from each replicate are.

        Cheers-
        Rory

        Comment

        • jennyal
          Junior Member
          • Dec 2014
          • 3

          #5
          Ok, at least I know now. Of course I'll let you know if I come across the answer!

          BW

          Jenny

          Comment

          • jennyal
            Junior Member
            • Dec 2014
            • 3

            #6
            Oh, Rory, I only saw you reply after I posted mine.

            Thanks for the info :-)

            BW

            Jenny

            Comment

            • Irem
              Junior Member
              • Sep 2014
              • 1

              #7
              Hi Rory,

              Could you please tell me how to combine the peaks from replicates into a low-FDR set using the IDR calculation?

              Thanks,

              Irem

              Comment

              • rory
                Member
                • Aug 2008
                • 28

                #8
                This page, "ENCODE: TF ChIP-seq peak calling using the Irreproducibility Discovery Rate (IDR) framework" , contains the instructions referenced in the "Software Tools Used to Create the ENCODE Resource" page on the UCSC browser...

                -R

                Comment

                • lwhitmore
                  Member
                  • Aug 2013
                  • 70

                  #9
                  Rory,
                  I looked at the page you mentioned and that program specifically says if you used macs14 that you should not use it to calculate the IDR. Do you know of any programs that can accurately calculate the IDR for peaks found by mac14?

                  Thanks
                  Leanne

                  Comment

                  Latest Articles

                  Collapse

                  • SEQadmin2
                    Beyond CRISPR/Cas9: Understand, Choose, and Use the Right Genome Editing Tool
                    by SEQadmin2



                    CRISPR/Cas9 sparked the gene editing revolution for both research and therapeutics.1 But this system still showed severe issues that limited its applications. The most prominent were the heavy reliance on PAM sequences, delivery limitations, double-stranded breaks that prompt unintended edits and cell death, and editing inefficiency (both in targeting and in knock-in reliability).

                    Despite this, “CRISPR helped turn genome editing from a specialized technique into
                    ...
                    07-31-2026, 11:01 AM
                  • SEQadmin2
                    Proteomic Platforms: How to Choose the Right Analytical Strategy to Improve Detection and Clinical Applications
                    by SEQadmin2


                    Proteomics platforms are evolving rapidly, with advances in mass spectrometry and affinity-based approaches expanding what researchers can detect and at what scale. As the field moves toward deeper proteome coverage and clinical applications, scientists face an increasingly complex landscape of tools. This article will explore how researchers are navigating these choices to find the right platform for their work.

                    The systematic characterization of the human proteome has
                    ...
                    07-20-2026, 11:48 AM
                  • SEQadmin2
                    Advanced Sequencing Platforms Tackle Neuroscience’s Toughest Genomics Problems
                    by SEQadmin2



                    Genomics studies in neuroscience face a special challenge due to the brain’s complexity and scarcity of samples. Mapping changes in cell type and state using conventional next-generation sequencing methods remains challenging. Advances in technologies like single-cell sequencing, spatial transcriptomics, and long-read sequencing have opened the door to deeper studies of the brain and diseases like Alzheimer’s, amyotrophic lateral sclerosis (ALS), and schizophrenia.
                    ...
                    07-09-2026, 11:10 AM

                  ad_right_rmr

                  Collapse

                  News

                  Collapse

                  Topics Statistics Last Post
                  Started by SEQadmin2, Today, 07:41 AM
                  0 responses
                  9 views
                  0 reactions
                  Last Post SEQadmin2  
                  Started by SEQadmin2, 08-03-2026, 10:13 AM
                  0 responses
                  23 views
                  0 reactions
                  Last Post SEQadmin2  
                  Started by SEQadmin2, 07-31-2026, 02:55 AM
                  0 responses
                  36 views
                  0 reactions
                  Last Post SEQadmin2  
                  Started by SEQadmin2, 07-24-2026, 12:17 PM
                  0 responses
                  25 views
                  0 reactions
                  Last Post SEQadmin2  
                  Working...