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  • Differentiate DNA-Seq / RNA-Seq and Exome sequencing data

    Hi there,

    If you have situation that you have files from three sequencing platforms ( DNA-Seq / RNA-Seq and exome sequencing ) and do not know which belongs to which, can you distinguish them in some way?

    I have an idea, but wanted to check what you guys think?

    - Map some reads from each technology to one chromosome ( to save time and computing power )

    - The reads that map everywhere, exons + introns = DNA-Seq reads

    - Reads that map to exons +- 50bp = Exome seq reads

    - Reads which have very less or zero coverage in intronic regions = RNA-Seq reads.

    What do you guys think? Is there any other way? ( Other than files sizes )

    Thanks a ton,

  • #2
    Seems reasonable. If you have a known chromosome. :-)

    Comment


    • #3
      Originally posted by westerman View Post
      Seems reasonable. If you have a known chromosome. :-)
      What do you mean by a known chromosome?

      Comment


      • #4
        Try working with, oh, say data from a Whale. :-)

        In some ways, if you are making the thought experiment of "how do I differentiate between different unlabeled data sets" then you might as well make it even more interesting -- try to differentiate between data that is both unlabeled in platform and species.

        Given data from a human sample, then, yes your approach seems reasonable.

        Comment


        • #5
          How about if we make bed files for the alignments of exome and whole genome?

          Would we be able to differentiate them using the UCSC browser ? I believe the tracks for the two should look very different.

          What do you guys think ?

          Comment


          • #6
            You could better differentiate between Exome and RNA-seq reads by watching the mean insert size, since aligning RNA-seq reads that span exons would lead to long insert sizes and/or split-reads. If the RNA-seq data is stranded than it's even easier.

            I guess the easiest method for visualization is IGV (http://www.broadinstitute.org/igv/)

            So take a subset from those reads, align them and watch them in IGV. (the only problem you might run into is a very shallow coverage which doesn't give you a nice picture in every gene...)

            Comment

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