Unconfigured Ad

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts
  • MLog
    Member
    • Jan 2010
    • 36

    Contamination between samples run on the same lane

    Hi colleagues,
    I found a strange effect when sequencing several samples on Miseq. The reads from each sample contain about ~0.1% of reads from other samples run on the same flowcell. This does not seem to be caused by improper recognition of barcodes - we did demultiplexing without allowing mismatches, and the indices are very different one from another (e.g. TAGCTT and AGTTCC).
    And this is certainly not contamination during library preparation - because I had one of the samples sequenced twice, with different "neighbors" and the contaminating reads were different.
    Have you ever seen something like that? What can be done to minimize this?
  • GenoMax
    Senior Member
    • Feb 2008
    • 7142

    #2
    See if your observation is the same as recently mentioned here: http://seqanswers.com/forums/showthread.php?t=38697

    Comment

    • MLog
      Member
      • Jan 2010
      • 36

      #3
      No, this is not a contamination between runs, if you mean this.

      Comment

      • GenoMax
        Senior Member
        • Feb 2008
        • 7142

        #4
        Check the posts by vl80 and JackieBadger in the thread I linked above. They are about cross contamination between samples in the same run. That is what you are referring to right?

        Comment

        • mcnelson.phd
          Senior Member
          • Jul 2011
          • 162

          #5
          Yeah, it looks like you're seeing what I've been calling index misassignment. It apparently stems from image alignment and signal bleed issues during index sequencing that causes a cluster to be assigned the wrong index sequence.

          Unfortunately, nothing can really be done about it, although dual indexing will cut down on the misassignment issue.

          Comment

          Latest Articles

          Collapse

          • 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
          • SEQadmin2
            Nine Things a Sample Prep Scientist Thinks About Before Sequencing
            by SEQadmin2


            I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.

            Here are nine questions we think about, in roughly the order they matter, before...
            06-18-2026, 07:11 AM

          ad_right_rmr

          Collapse

          News

          Collapse

          Topics Statistics Last Post
          Started by SEQadmin2, Yesterday, 11:08 AM
          0 responses
          7 views
          0 reactions
          Last Post SEQadmin2  
          Started by SEQadmin2, 06-30-2026, 05:37 AM
          0 responses
          11 views
          0 reactions
          Last Post SEQadmin2  
          Started by SEQadmin2, 06-26-2026, 11:10 AM
          0 responses
          19 views
          0 reactions
          Last Post SEQadmin2  
          Started by SEQadmin2, 06-17-2026, 06:09 AM
          0 responses
          53 views
          0 reactions
          Last Post SEQadmin2  
          Working...