Unconfigured Ad

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts
  • Kleido
    Junior Member
    • Jan 2014
    • 4

    What "high duplication rate" means

    Hi all,
    Could someone define "high duplication rate" in ChIP seq data analysis?
    Thks
  • dpryan
    Devon Ryan
    • Jul 2011
    • 3478

    #2
    Am I correct in guessing that you ran FastQC on your reads and it showed a really high duplication rate? If so, I would typically consider that normal unless whatever you're pulling down is rather non-specific in where it's located/binds.

    Comment

    • Kleido
      Junior Member
      • Jan 2014
      • 4

      #3
      Actually the bioinformatician who ran the analysis said that, so I'm just trying to understand since he couldn't explain it to me

      Comment

      • SNPsaurus
        Registered Vendor
        • May 2013
        • 525

        #4
        That probably refers to PCR duplicates... that is, even though you may have 90 reads at a location, they are likely to be 90 copies of the same original DNA fragment and so should not be considered independent binding events of your protein to DNA. This happens when low-complexity libraries are heavily amplified, which is common for ChIP-Seq.

        You can determine if duplicates are a problem because you would expect that reads start at multiple locations across a genomic region where your protein was cross-linked, because the DNA was sheared randomly. If the reads start at only a few locations and there are multiple reads at each of the starts, then those are going to be duplicates.

        As dpryan mentioned, with ChIP-Seq perfectly good data can look highly duplicated, since there might be very high coverage of reads in a constrained space. So it takes some actual examination of how the coverage looks to tell if it is just stacked high or duplicated.
        Last edited by SNPsaurus; 01-30-2014, 11:23 PM.
        Providing nextRAD genotyping and PacBio sequencing services. http://snpsaurus.com

        Comment

        • dpryan
          Devon Ryan
          • Jul 2011
          • 3478

          #5
          Just to add to SNPsaurus' response, keep in mind that gauging duplication rate is difficult if you have single-end reads. Then, the maximum coverage of a single position after removing what appear to be PCR duplicates is twice whatever your read-length is. Of course, for Chip-seq, this is unrealistic, so unless you have paired-end reads you're probably better off ignoring PCR duplicates.

          Comment

          • Kleido
            Junior Member
            • Jan 2014
            • 4

            #6
            Thank you dpryan and SNPsaurus, that's very helpful.

            Comment

            Latest Articles

            Collapse

            • 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.
              ...
              Today, 11:10 AM
            • SEQadmin2
              Cancer Drug Resistance: The Lingering Barrier to Rising Survival
              by SEQadmin2



              Cancer survival rates have significantly increased in the last few decades in the United States, reaching a combined 70% 5-year survival rate by 2021. Behind this number, there are years of research to find new therapies, drug targets, and early detection methods. But there is one core challenge that keeps slowing down these advances, and it’s about drug resistance.

              There is no single reason why many patients don’t respond to treatment as expected. Cancer is...
              Yesterday, 05:17 AM
            • GATTACAT
              Reply to Nine Things a Sample Prep Scientist Thinks About Before Sequencing
              by GATTACAT
              Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.
              07-01-2026, 11:43 AM

            ad_right_rmr

            Collapse

            News

            Collapse

            Topics Statistics Last Post
            Started by SEQadmin2, Today, 10:04 AM
            0 responses
            7 views
            0 reactions
            Last Post SEQadmin2  
            Started by SEQadmin2, Yesterday, 10:08 AM
            0 responses
            6 views
            0 reactions
            Last Post SEQadmin2  
            Started by SEQadmin2, 07-07-2026, 11:05 AM
            0 responses
            9 views
            0 reactions
            Last Post SEQadmin2  
            Started by SEQadmin2, 07-02-2026, 11:08 AM
            0 responses
            31 views
            0 reactions
            Last Post SEQadmin2  
            Working...