RNA sequencing alignment, quantification and differential expression
The RNA-Seq reads were aligned against Arabidopis thaliana TAIR10 transcriptome models using bowtie2 (Langmead & Salzberg, 2012) with the -very-sensitive preset. Transcript quantification was performed by the eXpress software v1.5.1 (Roberts & Pachter, 2013) using default parameters. The transcript counts were used as input for the R/Bioconductor package baySeq v2.6.0 (Hardcastle & Kelly, 2010) in order to perform differential expression analysis. This package was run using default parameters and with a false discovery rate (FDR) of 0.05. For each condition (day, night), each mutant was compared with Col-0.
Publicly available datasets from studies of differential expression related to light signalling and a control unrelated to light signalling were downloaded from the European Bioinformatics Institute (EBI) for comparison with the results for our srt1-4 hete mutant. These external datasets are listed in Supplemental Table 3. The differentially expressed transcripts were divided into a list of up regulated and a list of down regulated transcripts. Then the overlap between the up and down regulated lists for srt1-4 hete and each of the corresponding up and down regulated lists for the five comparisons was identified. The R/Bioconductor package, GeneOverlap v1.24.0 (Shen, 2020), was used to determine whether the overlap was statistically significant. Because the internal and external datasets do not contain exactly the same genes, the parameter “genome size” was given as the number of gene identifiers common to both data sets. For the comparison between srt1-4 hete in the day and the “white light” (WL) data from EBI, the genes in each of the four overlaps were tested for enrichment of Gene Ontology (GO) terms (Ashburner et al ., 2000) using the R/Bioconductor package GOstats v2.54.0 (Falcon & Gentleman, 2007) with GO.db v3.11.4 (Carlson, 2020a) and org.At.tair.db v3.11.4 (Carlson, 2020b).