2.5 RNASeq reads check and genome coverage
Quality checks of the raw RNA-Seq reads were performed using Fastqc (Andrews, 2014).
Reads were trimmed with trimmomatic (version 0.38, Bolger et al. 2014). Raw reads were mapped to an Oncorhynchus mykiss reference genome from NCBI (Omyk_1.0, https://www.ncbi.nlm.nih.gov/assembly/GCF_002163495.1/, Annotation release ID:100) using STAR (version 2.7.1a; Dobin et al., 2013) to obtain the number of genes recovered by each technique, 3’ Tag-Seq vs. whole mRNA-Seq (NEB).
In order to perform the bioinformatic analyses on samples with an equal number of reads, we randomly selected 11 million reads per sample for all the analyses performed only on 3’ Tag-Seq reads and 40 million reads per sample for all the analyses performed using whole mRNA-Seq (NEB) reads. Previous work has shown that >10M reads whole mRNA-Seq and 3’ Tag-Seq perform similarly in recovering transcripts of different length (Ma et al.,2019). Reads were mapped again to theOncorhynchus mykiss reference genome. HT-Seq (version 0.11.1; Anders et al. 2015) was then used to quantify the number of reads uniquely mapped to each gene of the O. mykiss reference genome. Finally, a python script provided with Stringtie (prepDE.py) was used to generate a gene counts matrix (Pertea et al., 2016).