GSM3703433: 0 h A rep1; Nannochloropsis oceanica; RNA-Seq
Cultured cells (50 ml per sample) were harvested for RNA isolation after N- or NR treatments at 6, 12, 24, 48 and 72 h. For each time point two biological replicates were harvested by centrifugation (4000 x g, 10 min, 22°C). Cells were flash-frozen in liquid N and ground  using TissueLyser II (Qiagen). RNA was extracted using the E.Z.N.A® Plant RNA kit (OMEGA) according to the manufacturer's instructions. RNA quality was verified using the Bioanalyzer (Agilent). Single-end, 50 bp nucleotide sequences were acquired for each sample using an Illumina HiSeq 2500 (MSU-Research Technology Support Facility). RNA-Seq reads were trimmed using Trimmomatic (v0.32) (Bolger et al., 2014) with removal of leading and trailing low quality bases (below quality score 10 with a 4-base wide sliding window) as well as TruSeq Single End adaptors. The resulting reads were aligned to the N. oceanica CCMP1779 genome assembly (GSE36959) (Vieler et al., 2012) using STAR (2.3.0e) (Dobin et al., 2013) by only allowing unique mapping, a maximum of 4 mismatches per pair and a maximum intron length of 10,000 bp. The HTSeq (0.6.1) (Anders et al., 2015) was used to generate a count table for all genes using the exon as the feature type as well as the intersect-nonempty mode so that only reads that were completely aligned to two genes are considered ambiguous. HTSeq count tables were used for differential expression analysis using DEseq2 (Love et al., 2014). Differential gene expression was examined using DESeq2 (Love et al., 2014). A generalized linear model was fit, and we tested on condition (deprivation or recovery) and the time:condition interaction with log2-fold changes greater than 1. Significant differentially expressed genes with p-values less than 0.01 were identified after Benjamini-Hochberg (BH) multiple testing correction. Differentially expressed genes were clustered based on expression using K-means clustering (k = 4) with the Pearsons correlation distance metric within MeV (4.8.1) (Saeed et al., 2003).                                                              References:                                                                                                                                                                          Anders S, Pyl PT, Huber W (2015) HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 31: 166-169    Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30: 2114-2120    Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15: 550    Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M, Sturn A, Snuffin M, Rezantsev A, Popov D, Ryltsov A, Kostukovich E, Borisovsky I, Liu Z, Vinsavich A, Trush V, Quackenbush J (2003) TM4: a free, open-source system for microarray data management and analysis. Biotechniques 34: 374-378    Vieler A et al. (2012) Genome, functional gene annotation, and nuclear transformation of the heterokont oleaginous alga Nannochloropsis oceanica CCMP1779. PLoS Genet 8: e1003064 RNA libraries were prepared for sequencing using standard Illumina protocols