title: RNA-Seq of tomato: uninfected and elicited leaves
design: Library preparation was made with TruSeq Stranded mRNA kit (Illumina, United States) on 1 g total RNA. Libraries were then sequenced on NovaSeq6000 S1 Flowcell 100 cycles (Illumina, United States) following the standard protocol (75 bp single-end reads, High-Output kit) at the VIB Nucleomics core. Low-quality ends and adapter sequences were trimmed off from the Illumina reads with FastX 0.0.14 and Cutadapt 1.152. Subsequently, small reads (length < 35 bp), polyA-reads (more than 90% of the bases equal A), ambiguous reads (containing N), low-quality reads (more than 50% of the bases < Q25), and artifact reads (all but three bases in the read equal one base type) were filtered using FastX 0.0.14 and ShortRead 1.40.0. With Bowtie 2.3.3.1 we identified and removed reads that align to phix_illumina (Illumina, United States). Reads were aligned by STAR 2.5.2b to the reference genome (S. lycopersicum SL2.50.38). Default parameters were used, except for two pass Mode (Basic). Using Samtools 1.5, reads with a mapping quality smaller than 20 were removed from the alignments. Cufflinks v2.2.1 and mergeBed from the Bedtools toolkit were used to reconstruct transcripts and to identify features using reference annotation. The next steps of the analysis were conducted using R (R-3.5.3) with Bioconductor package. EDASeq was used to normalize each sample using full quantile normalization according to its GC content, library size, and RNA composition. Based on Spearman correlation, hierarchical clustering highlighted an outlier in tomato plants, which was therefore removed from the analysis. Differentially expressed genes (DEGs) were identified with edgeR 3.20.8 based on a negative binomial generalized linear model (GLM). We did not use the normalized counts directly but worked with offsets. False discovery rates (FDRs) were calculated based on p-values with BenjaminiHochberg method to correct for multiple comparison errors.