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Identify and visualize differentially expressed genes from RNA-Seq data?

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  • Identify and visualize differentially expressed genes from RNA-Seq data?

    Hi All,
    I have aligned RNA Seq data for four healthy controls and four patients, in bam format. Then I made a table for RPKM for all the genes. Now how do I quickly identify and visualize those differentially expressed genes in patients? In other words, how to identify which genes are expressed more in patients but not in healthy control? I've tried SAM (significant analysis for microarray ) but no good. Any suggestions? Thanks!

  • #2
    There are several programs in R that are for differential expression of RNA-seq data:

    EdgeR
    DESeq
    BaySeq
    DEGseq

    However the input for these programs is raw read counts, not RPKM normalized genes.

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    • #3
      What tool did you use?
      is the result by exon? or gene? how does it correct for gene overlap?

      How would you like to visualize the results? As a list? On the genome? On pathways or ontologies?

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      • #4
        @chadn737
        Thanks!
        @Richard Finney
        I used Seqmonk to generate RPKM , results are by exons but can be averaged to by genes. I would like to visualize in a hierarchical clustered heat map, like many microarray data.

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        • #5
          Hello mediator,

          I have found that MeV is very helpful in visualizing RNA-seq expression data. It is well-documented and easy to implement.

          Cheers,
          Kevin

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          • #6
            @kjlee
            Thanks for the advice! I already use MeV for microarray. Pretty handy. Let's see how RNA-Seq data goes with MeV.

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            • #7
              Hi all
              I have RNA-seq data (2 control sets and 2 test sets). I have generated the *.sorted.bam files (~1GB) through bowtie software. Right now I am using cufflinks software to generate RPKM values to get differential gene expression quantitatively. In cufflinks, I followed the command:
              cufflinks control_1.sorted.bam
              It takes much time on cluster (thats reasonable for 1gb INPUT files I guess). The output file (say transcript.gtf) is having RPKM values. I want to know how to proceed further to get final list of differentially expressed genes with corresponding RPKM values.
              Do I have to run the above command for all input files (viz. control_2.sorted.bam, test_1.sorted.bam, test_2.sorted.bam)?
              Also How to compare RPKM values finally ?
              Thanks in advance..
              Kumardeep

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              • #8
                Hi Kumardeep

                You should always start a new thread if you're asking a new question, even if it is related (you can always refer to the other thread).

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