text: user6525: I was thinking about the best method for normalization, which takes gene length into account (in order to compare genes)...
  Do you think I can do that? :
  - taking raw counts and dividing each gene by its length
  - using the function rlog (DESeq2) on these counts divided by gene length (I would modify the rlog function to allow it to be used on decimal data).
  I was wondering if it would be ok to do that.
  Thank you very much for your help
  
  user3541: I am not sure what you mean by "decimal data", however, if you really want to use "gene length" information for normalization, take a look at EDASeq. Unlike DESeq or EdgeR, its normalization step takes gene length into account to produce "normalized counts", which you can feed into DESeq or EdgeR according to their manual.
  Below is from the said manual: 
  
  The normalized counts (or the original counts and the offset) obtained
    using the EDASeq package can be supplied to packages such as edgeR
    (Robinson, McCarthy, and Smyth 2010) or DESeq (Anders and Huber 2010)
    to find differentially expressed genes.
  
  
  user73: I've found that normalisation of variance-stabilised counts from short-read sequencing based on the length of the longest transcript isoform seems to work well. Here's a bit more detail on that from the methods of our paper:
  
  A variance-stabilizing transformation (VST) was applied to count data using the DESeq2 function varianceStabilizingTransformation, producing gene expression values that are adjusted for variance across sample conditions. To compare different genes to each other, this matrix was further transformed by dividing by transcript length in kbp to generate VSTPk values.
  
  [FWIW, single-molecule long reads don't need any length-normalisation carried out on them]
  
original_text: 6525: <p>I was thinking about the best method for normalization, which takes gene length into account (in order to compare genes)...</p>
  
  <p>Do you think I can do that? :
  - taking raw counts and dividing each gene by its length
  - using the function rlog (DESeq2) on these counts divided by gene length (I would modify the rlog function to allow it to be used on decimal data).</p>
  
  <p>I was wondering if it would be ok to do that.</p>
  
  <p>Thank you very much for your help</p>
   3541: <p>I am not sure what you mean by "decimal data", however, if you really want to use "gene length" information for normalization, take a look at <code>EDASeq</code>. Unlike <code>DESeq</code> or <code>EdgeR</code>, its normalization step takes gene length into account to produce "normalized counts", which you can feed into <code>DESeq</code> or <code>EdgeR</code> according to their <a href="https://bioconductor.org/packages/release/bioc/vignettes/EDASeq/inst/doc/EDASeq.html#differential-expression-analysis" rel="nofollow noreferrer">manual</a>.</p>
  
  <p>Below is from the said manual: </p>
  
  <blockquote>
    <p>The normalized counts (or the original counts and the offset) obtained
    using the EDASeq package can be supplied to packages such as edgeR
    (Robinson, McCarthy, and Smyth 2010) or DESeq (Anders and Huber 2010)
    to find differentially expressed genes.</p>
  </blockquote>
   73: <p>I've found that normalisation of variance-stabilised counts from short-read sequencing based on the length of the longest transcript isoform seems to work well. Here's a bit more detail on that from the methods of <a href="https://doi.org/10.1084/jem.20160470" rel="nofollow noreferrer">our paper</a>:</p>
  <blockquote>
  <p>A variance-stabilizing transformation (VST) was applied to count data using the DESeq2 function varianceStabilizingTransformation, producing gene expression values that are adjusted for variance across sample conditions. To compare different genes to each other, this matrix was further transformed by dividing by transcript length in kbp to generate VSTPk values.</p>
  </blockquote>
  <p>[FWIW, single-molecule long reads don't need any length-normalisation carried out on them]</p>
  