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How to define a expressed gene based on RNA-seq data

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  • How to define a expressed gene based on RNA-seq data

    Recently,I have been working with RNA-seq data,and one problem I met is how to define a expressed gene.I use RPKM to nomalize the expression level,but the cutoff value to define a expressed gene is a problem.Can somebody give me any suggestions?I have read some papers,some of them said 1 (RPKM) would be fine,but according your experiences,how to do that?

  • #2
    I posted this same question and nobody replied.

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    • #3
      Its a question I have asked as well many times and searched extensively for. First, if you have reads mapping uniquely to a gene, then I don't think you can say that its not expressed, only that its expressed at very low levels. Any cutoff it seems to me, will most likely be an arbitrary one, not based on actual biological meaning.

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      • #4
        One way to derive a threshold value is to follow the procedure in this paper:

        http://www.ploscompbiol.org/article/...l.pcbi.1000598

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        • #5
          I fully agree with chadn737.

          The only sensible defintion of "expressed" I can see is that transcripts of the gene have been produced. And this is definitely the case already when you see a single read (unless the read could have been from another locus).

          With microarrays, people use the term "expressed" to denote "present above background". This makes sense only if one refrains from calling genes with flourescense at background level "not expressed", because there is no way to say whether a gene is really fully switched off or simply so weakly expressed that its signal cannot be seen above the background autoflourescence. Somehow, this did not keep people from assuming that any gene they cannot see on their array is "switched off". (I had endless discussions with wet-lab collaborators who insisted that we give a percentage of non-expressed genes "because everybody does so".)

          Now, with high-coverage RNA-Seq, it turns out that genes are hardly ever perfectly silent -- although I wonder if this really that surprising. (If this very low transcription turned out to be functional rather than just leakage, that would be surprising, I suppose.)

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          • #6
            Originally posted by Simon Anders View Post
            I fully agree with chadn737.

            The only sensible defintion of "expressed" I can see is that transcripts of the gene have been produced. And this is definitely the case already when you see a single read (unless the read could have been from another locus).

            With microarrays, people use the term "expressed" to denote "present above background". This makes sense only if one refrains from calling genes with flourescense at background level "not expressed", because there is no way to say whether a gene is really fully switched off or simply so weakly expressed that its signal cannot be seen above the background autoflourescence. Somehow, this did not keep people from assuming that any gene they cannot see on their array is "switched off". (I had endless discussions with wet-lab collaborators who insisted that we give a percentage of non-expressed genes "because everybody does so".)

            Now, with high-coverage RNA-Seq, it turns out that genes are hardly ever perfectly silent -- although I wonder if this really that surprising. (If this very low transcription turned out to be functional rather than just leakage, that would be surprising, I suppose.)
            The idea of leaky transcription is one that really needs to be addressed. Particularly when some studies have shown that the majority of transcriptional events do not even make full-length mRNAs:

            In vivo dynamics of RNA polymerase II transcription
            http://www.nature.com/nsmb/journal/v.../nsmb1280.html

            Then there is the fact that there is often poor correlation between transcriptomics and proteomics data confounds the issue. If the reads are mapping to the gene, I think its real, but at those low levels, I agree that it seems questionable whether or not it is functional.

            I like to thing that if a reasonable estimate of leaky transcription could be obtained and applied to transcriptomic data, that you would see increased correlation with proteomic data.

            Since I mainly have used RNA-seq for differential expression, my concern at what point are you confident that a gene is differentially expressed. Doesn't the "shot noise" of DESeq address this issue?

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            • #7
              What are the assumptions about the sample source?

              If the assumption is that every single cell of multi-cell sample source is frozen in an identical,homogeneous biological state, these results would be biologically relevant. But from my perspective, the possibility of (many) different (multiple) levels of cellular heterogeneity in any sample of cells primarily characterized based on gross histology should be considered. Such that the dynamics of a given cell population can become better identified.

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