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  • Ouuuw
    Junior Member
    • Feb 2014
    • 3

    #1

    Should I extract RNA from tissues with constant weight?

    Hi~ I am very new to a biological lab.

    Now, I am going to identify DE genes from intestinal samples of health cows and sick cows.

    When I extracted the RNA, the tissues I used were different in weight. But when I prepared the library for illumine sequencer, I did adjust the concentration of RNA to a same value, and the same quantity of RNA was used ( I think it was 1ug totally).

    Now, the RNA-seq data is back, and I get the read count using htseq-count. Can I use those data directly to identify DE genes using edgeR? Or, should I normalize those data first using a housekeeping gene first?

    Could anyone be kind and let know this, if you happen to know the answer? I'd really appreciate that...
  • dpryan
    Devon Ryan
    • Jul 2011
    • 3478

    #2
    Just use the normalization provided by edgeR, which will typically be more robust than using a single gene (or even a handful) for normalization. While there are cases when this won't work well, they're relatively rare.

    Comment

    • Ouuuw
      Junior Member
      • Feb 2014
      • 3

      #3
      Originally posted by dpryan View Post
      Just use the normalization provided by edgeR, which will typically be more robust than using a single gene (or even a handful) for normalization. While there are cases when this won't work well, they're relatively rare.
      Thank you very much dpryan! So it is not necessary to measure the weight of tissues used for RNA extraction, right?

      Comment

      • dpryan
        Devon Ryan
        • Jul 2011
        • 3478

        #4
        It's not generally necessary, no. Even if one prep produces twice as much RNA as another, the same amount (more or less) will be loaded for sequencing. When amount of starting material does come into play is if one is looking at transcriptional amplification, when you end up using spike-ins or normalizing to the amount of DNA, but these aren't typical use cases (i.e., you'd very likely know about this already were you working on one of these cases).

        Comment

        • Ouuuw
          Junior Member
          • Feb 2014
          • 3

          #5
          Devon, thank you so much for the help~

          Comment

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