Hello, My name is Kevin Lee, and I am a PhD candidate in Bioinformatics at Georgia Institute of Technology. I have been trying to decide between various normalization techniques for RNA-seq methods for a parasite infection time-course study. The RNA-seq data that I will have will be from infected blood and will contain RNA from both the host and the parasite. I believe that it is best to separate the analysis of these two RNA sub-sets for the purposes of normalization. I have been using DESeq because it is clearly superior to FPKM. Recently however, I was intrigued by the new Cuff Diff 2 software. As I have weighed the two methods (DESeq versus CuffDiff2), I see the benefits of each. CuffDiff2 has the advantage that it quantifies isoform abundance, but it uses FPKM to "normalize" expression levels across samples. DESeq, however, estimates library size much more robustly than RPKM. Since my study will be looking at immune response to a parasite over time, I expect that there will be at least a few genes that are VERY differentially expressed in one or more of the conditions/time points. Consequently, I believe that a DESeq normalization approach will yield much more accurate analysis. Does this seem like a reasonable assessment? If I do choose to use DESeq, one what level should I quantify transcription: exonic, gene, or isoform? Exonic seems the most straight-forward but also has the drawback of representing a much smaller "area", and individual exons will have much fewer reads that map compared to the number of reads mapping to the gene (of which the exon is a part). If quantifying by gene, how is a gene defined: as all exons from all isoforms? I don't know of any way to quantify isoforms as is done by CuffDiff, and this is the main reason I am hesitant about using DESeq. One possible approach is to use reads that fall within any annotated exon as being part of a gene. And using those measures to normalize and test for differential expression. And in parallel use DEXSeq to test for differential exon usage. Does this seem like a reasonable approach? Any further advice? Cheers, Kevin -- Kevin Lee Georgia Institute of Technology Department of Biology PhD candidate in Bioinformatics [[alternative HTML version deleted]]

Hi Kevin > One possible approach is to use reads that fall within any annotated exon > as being part of a gene. And using those measures to normalize and test > for differential expression. And in parallel use DEXSeq to test for > differential exon usage. Does this seem like a reasonable approach? Any > further advice? As one of the author of both DESeq and DEXSeq, I probably cannot give you an unbiased opinion on how our tools compare to cuffdiff; but if you go for our methods, then yes, what you wrote is the approach we recommend. Simon