Hi,
We plan to run some targeted whole transcriptome rna-seq using the AmpliSeq RNA methodology currently being sold by Life Technologies/Thermo Fisher. As a background on the technique, LT has designed 100-200bp probes for >20,800 features from RefSeq and provide that as a tool to study gene level differential expression. You can find more information here:
I have been using DESeq2 for our whole transcriptome sequencing data and am wondering if DESeq2 will work for this targeted approach. I am reading/trying to understand more about the Math behind DESeq2 and on the surface it seems that DESeq2 should work fine because if I understand it correctly,"gene length" doesn't necessarily factor in the sizefactor based feature level normalization (unlike RPKM/FPKM, which in this case can be replaced by RPM/FPM). However, I am curious to know if I am overlooking something that might affect how DESeq2 would behave for such an experimental design.
I'll appreciate any insights on this.
Thank you!
We plan to run some targeted whole transcriptome rna-seq using the AmpliSeq RNA methodology currently being sold by Life Technologies/Thermo Fisher. As a background on the technique, LT has designed 100-200bp probes for >20,800 features from RefSeq and provide that as a tool to study gene level differential expression. You can find more information here:
I have been using DESeq2 for our whole transcriptome sequencing data and am wondering if DESeq2 will work for this targeted approach. I am reading/trying to understand more about the Math behind DESeq2 and on the surface it seems that DESeq2 should work fine because if I understand it correctly,"gene length" doesn't necessarily factor in the sizefactor based feature level normalization (unlike RPKM/FPKM, which in this case can be replaced by RPM/FPM). However, I am curious to know if I am overlooking something that might affect how DESeq2 would behave for such an experimental design.
I'll appreciate any insights on this.
Thank you!
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