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).