RNA Sequencing Experiment and Analysis

RNA was isolated from tissue of the MSA harvested from Ubi::HvFT3 and null segregant plants grown under LD and SD conditions. MSA tissue was harvested at the developmental stages W2.0 and W3.0 to 3.5 2 h before the end of the light period. Leaves surrounding the MSA were removed manually, and the apex was cut using a microsurgical stab knife (5-mm blade at 15° [SSC#72-1551]). For each of three biological replicates, at least 10 MSA were pooled. The MSA harvested for RNA extraction were frozen immediately in liquid nitrogen and stored at −80°C. The RNA was isolated as described in van Esse et al. (2017). The Illumina cDNA libraries were prepared according to the TruSeq RNA sample preparation (version 2; Illumina). A cBot (Illumina) was used for clonal sequence amplification and generation of sequence clusters. Single-end sequencing was performed using a HiSeq 2500 (Illumina) platform by multiplexing 8 libraries resulting in ∼18 million reads per library. The sequencing data quality was verified using FastQC software (version 0.10.1, http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/) before further processing using the CLC Genomics workbench (version 6.0.4, CLCbio). PCR duplicates were removed from the raw sequencing data using the Duplicate Read Removal plugin of CLC. The reads were trimmed with an error probability limit calculated from the Phred scores of 0.05, allowing for a maximum of two ambiguously called nucleotides per read. Reads shorter than 60 bp, subsequent to the quality-based trimming, were removed from the dataset.

The obtained RNA sequencing reads were mapped to a barley high confidence transcripts reference (Beier et al., 2017; Mascher et al., 2017) using Salmon in quasi-mapping-based mode as described in van Esse et al. (2017). When building the quasi-mapping-based index, an auxiliary k-mer hash over k-mers of length 31 was used. U (unstranded single end read) was chosen as library type to quantify the reads of each library. The expected number of reads (NumReads) that have originated from each transcript given the structure of the uniquely mapping and multimapping reads and the relative abundance estimates for each transcript and transcripts per million values were extracted using Salmon (Patro et al., 2017). The NumReads was used in downstream analysis. Transcripts with expression levels greater than five NumReads in at least three libraries under each condition (LD, SD) were retained. The expected number of reads and normalized counts per million values are provided in Supplemental Table S4. Differentially regulated reads were called using the R bioconductor package Limma-vroom (Ritchie et al., 2015) using a Benjamin and Hochberg adjustment for multiple testing for calculation of the adjusted P values (false discovery rate; FDR values). For expression analysis, an FDR value of 0.05 was used as cut-off value for the selection of differentially-expressed transcripts (DETs). DETs were extracted per developmental stage between the genotypes and per genotype between the developmental stages. Hierachical cluster analysis was done in R using Pearson correlation coefficients. The overrepresentation analysis of particular GO terms was performed using the R-package TopGo (Alexa and Rahnenfuhrer, 2016). The Venn diagram was drawn using the R package VennDiagram (Chen and Boutros, 2011). The correspondence of MLOC to HORVU gene identifiers was estimated using reciprocal blastn analysis (identity score > 95%). The gene names were extracted based on the MLOC identifiers as annotated in Digel et al. (2015).

Illumina data is available in the European Short Read Archive, EBI ArrayExpress E-MTAB-7158. Accession numbers of major flowering-time genes are listed in Supplemental Table S5.