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).