2.7 Differential gene expression analysis
Differences in gene expression among groups were identified by differential expression analysis performed using DESeq2 on raw read counts (non-normalized, as suggested by DESeq2). To allow comparison between QuantSeq reads and NEB reads data, we only used one sequencing direction as suggested for the DESeq2 program for the latter. The false discovery rate (FDR) was adjusted to 0.05, corresponding to a recovery at most of 5% of false positives following the DESeq2 manual. We use the default options for all the other parameters. We look at differences in gene expression between sampling methods, harvest tissue time, tissue type, and QuantSeq vs. NEB in Table 2: (see Tables 1, 2 and data on Dryad per detailed information about comparisons and sample size for each comparison; minimum N = 4). The log2 fold changes obtained from DESeq2 were used as a measure of how many more (or less) genes are expressed in one group versus the other. We considered genes having different expression if the adjusted p-value (using the adjusted p-value results in less false positives) was < 0.05.
Finally, previous work has indicated an increase in read count for longer transcripts using NEB than QuantSeq (Ma et al. 2019; but see Crow et al., 2022). To further address the relationship between gene length and genes differentially expressed between QuantSeq and NEB, we conducted an assessment using the known transcript length from orthologous genes in zebrafish in Ensembl 101 (Yates et al. 2020) based on gene name for genes that were detected to be differentially expressed between the two library types. We used the zebrafish instead of the rainbow trout genome as the former has a more curated annotation (and thus more precise gene length information) than the latter. We also used the same approach to specifically assess if transcript length could influence absence of gene expression or not detection (mean base = 0 in DESeq2 output) in one but not the other library type, QuantSeq or NEB.