Table 1.
Assembly statistics for the de novo transcriptome.
Fig 1.
GO analysis of the assembled transcriptomes of C. lyallii and C. pallens.
Transcripts were categorised into 32 different GO categories sub-divided into molecular function, biological process, and cellular component.
Fig 2.
Identification of transcription factors from C. lyallii and C. pallens.
A subset list of the number of transcription factors identified from the assembled transcriptome of C. lyallii and C. pallens.
Fig 3.
Identification of different protein classes.
Categorisation of the peptide sequences predicted from the assembled transcriptome of C. lyallii and C. pallens.
Fig 4.
Functional classification of the C. lyallii transcriptome.
The identified unigenes were further grouped into 25 categories based on the homology search against the COG database.
Fig 5.
Volcano plot showing differentially expressed genes.
15,234 significantly differentially expressed genes were identified between the leaves and apical meristems. The red dots represent significantly up-regulated genes and green dots represent significantly downregulated genes with a P-value < 0.05.
Fig 6.
Comparisons of transcriptional profiles across samples.
Heat-map showing hierarchical clustering resulting from a pairwise comparison of transcript expression levels. Clustering is represented between three separate biological replicates of leaves and apical meristematic (AM) tissue, respectively.
Fig 7.
Relative expression of the flowering-pathway genes CONSTANS-LIKE, CRYPTOCHROME, APETELA 2, and DICER-LIKE 4 across leaves and apical meristems.
Expression profile is based on RNA-seq data, normalised by the TMM method and RT-qPCR data showing relative fold change, normalised to the selected reference genes. The RT-qPCR experiment was performed using three biological replicates for each tissue with three technical replicates ±S.D.
Fig 8.
Seasonal gene expression analysis of selected flowering-pathway genes in C. lyallii and C. pallens.
The relative expression was calculated using 2-ΔΔCt method, represented by data from two biological replicates with three technical replicates ± S.D.
Table 2.
Annotation of flowering-related genes selected for RT-qPCR analysis.