Code for Environmental transcriptomics under heat stress: Can environmental RNA reveal changes in gene expression of aquatic organisms?

Overview of the pipeline used to analyze RNA-seq data from environmental RNA and Daphnia pulex tissue samples for the publication “Environmental transcriptomics under heat stress: Can environmental RNA reveal changes in gene expression of aquatic organisms?” by Hechler, Yates, Chain and Cristescu. Raw FASTQ files underwent initial sequencing quality inspection using FastQC (Andrews, 2010). Low quality sequences and adapters were removed using Trimmomatic (Bolger et al., 2014). All statistical analyses were conducted using R (R Core Team, 2021, R version 4.1.2). With default parameters, STAR (Dobin et al., 2013) was used to map all eRNA and oRNA reads that passed quality control and trimming. FeatureCounts (Liao et al., 2014) was used on the BAM file output from STAR to quantify gene counts. Differential expression analysis was conducted with DESeq2 (Love et al., 2014). GO enrichment analysis was conducted using topGO (Alexa & Rahnenfuhrer, 2022). We used the SqueezeMeta metatranscriptomics pipeline for the eRNA community-wide analysis (Tamames & Puente-Sánchez, 2019) and performed differential gene expression analyses as described above for Daphnia, after we mapped, using Kallisto (Bray et al., 2016), eRNA reads to the reference genomes and transcriptomes of species known a priori to persist in the communities and highly abundant species identified by SqueezeMeta. Lastly, we used SimKa to estimate k-mer similarity between eRNA samples (Benoit et al., 2016)