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  • Index Read distribution

    Hello,

    I'm looking for advice, help, thoughts, etc on how to achieve more balanced read output when multiplexing. Typically I run all libraries on Agilent Bioanalyzer and pool according to the library peak in equimolar amounts. My last TruSeq mRNA seq project had between 2% and 8% reads for each sample, which varied read output 12 to 48million reads per sample. I see variations also when doing 16s(Schloss barcodes). I just wondering if variation is typical, some barcodes may amplify better than others, etc?? Do most labs see more even read distribution or is it more likely to "do the best you can and hope the outcome is acceptable"?

    I'd be interested in any success stories using normalization protocols, plates, beads etc for library pooling.

    Thanks for any help!
    Last edited by RickC7; 09-27-2017, 07:06 AM.

  • #2
    The most accurate method for library quantification is qPCR.

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    • #3
      Thanks, I get that and have no problem when loading a sequencer for optimal cluster density. I'm also not going to quantify 192 16s libraries via qPCR for multiple projects. I more looking to get better/ more even read distribution for multiplexed library pools.

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      • #4
        For amplicons such as 16S that has been prepped using the same protocol, pooling based on dsDNA specific reagents such as PicoGreen results in up to 3x variation in output which is similar to bead or plate based normalisation methods.

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        • #5
          Old post I know, sorry to be late to the party, but if you're still around RickC7, I'd like to see if you made any inroads on the normalization front. It seems like labs doing a lot of samples use the Nextera normalization method using beads and denaturing the library to get fragments off the bead. I am trying to create something similar for Truseq without denaturing.

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          • #6
            I haven't yet tried bead based normalization, but I have tried some plate normalization methods like SequalPrep. It did not work well in our hands. For larger sample numbers, like when we are doing 16s, we now use fluoro-plate reader, then create a normalization plate template for our EPMotion. This works best, still get a few outliers but not bad. I randomly check a few samples to make sure unincorporated primers and such are removed.

            For smaller projects <24 samples, I just use qubit/bioanalyzer. Calculate and dilute all libraries down to 4nM then pool. I get better read distribution and cluster density using qubit/bioanalyzer than qPCR. I can easliy squeeze out 600million PF reads on our Next-Seq at 85% PF and 85% Q30. My %Phix loading is usually close to being spot-on, so that tells me my quantification is accurate.

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