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  • What are the available best options for plotting RNA-seq data?

    Hello

    I was just wondering what are the available best options available for plotting and visualizing RNA-seq data.

    thanks in advance
    sujata

  • #2
    You can't really "plot" the RNA-Seq data, you can display the alignments or read densities on any of the multiple currently available genome browsers. Or you can plot specific things about the data in myriad ways...
    Last edited by GKM; 11-26-2010, 12:07 AM.

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    • #3
      artemis is fine

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      • #4
        We like IGB, it does the job well and is very flexible. Others include Jbrowse, IGV etc -- look in the software wiki.

        Tablet is great, but support for annotations is still in development ...

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        • #5
          For totally flexible plotting and associated computations on the data (but using a programmatic interface with a steep learning curve), the GenomeGraphs package from Bioconductor, and the associated data manipulation and integration tools:
          Wolfgang Huber
          EMBL

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          • #6
            Originally posted by Wolfgang Huber View Post
            For totally flexible plotting and associated computations on the data (but using a programmatic interface with a steep learning curve), the GenomeGraphs package from Bioconductor, and the associated data manipulation and integration tools:
            http://www.bioconductor.org/help/wor...put-sequencing
            Is there an RNA-Seq specific workflow within the bioconductor package?
            --
            bioinfosm

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            • #7
              SeqMonk (which we develop, so I'm slightly biased!) can plot spliced alignments and you can use it either as a simple data browser, or calculate and plot quantitated data. You can import a SAM file straight from tophat.

              There's a section in our training manual which briefly discusses options for RNA-Seq (p40)

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              • #8
                If it's any help I've just put up a video showing a simple RNA-Seq analysis in SeqMonk - you can see how the data looks and some of the simple tools for identifying differential expression.

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                • #9
                  Hey thats great!

                  Thanks for the video ..
                  --
                  bioinfosm

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