RNA-Sequencing of spinach tissues with contrasting oxalate contents

Purpose: The goals of this study are to compare differentially expressed transcripts in roots and leaves of spinach cultivars with distinct oxalate contents using transcriptome profiling (RNA-seq) Overall design: Methods: Leaf and root samples for RNA Seq analysis were collected from three independent spinach plants of cultivar PI175311 and Bloomsdale were flash-frozen in liquid nitrogen for further analysis. Results: The clustering of the index-coded samples was performed on a cBot Cluster Generation System using PE Cluster Kit cBot-HS (Illumina) according to the manufacturer's instructions. After cluster generation, the libraries were sequenced on an Illumina Hiseq platform, and 150 bp paired-end reads were generated. Raw reads of fastq format were processed to obtain clean reads by removing the adapter, reads containing ploy-N, and low quality reads from raw data. At the same time, Q20, Q30, and GC content, the clean data were calculated. Reference genome and gene model annotation files were downloaded from SpinachBase (http://spinachbase.org/). Index of the reference genome was built using Bowtie v2.2.3, and paired-end clean reads were aligned to the reference genome using TopHat v2.0.12. : HTSeq v0.6.1 was used to count the reads mapped to each gene. Reads Per Kilobase of exon per Megabase of library size (RPKM) were calculated from mapped read by featureCounts. Differential expression analysis of High N and Low N conditions (three biological replicates per tissue per treatment) was performed using the DESeq R package (1.18.0) (Anders and Huber, 2010). Genes with P-value < 0.05 found by DESeq were assigned as differentially expressed. Conclusions: The transcriptomic changes in leaf and root tissues was used for RNA-Seq analysis. A total of six libraries from leaf and root tissue were sequenced for each cultivar using the Illumina HiSeq platform. On average, 60 and million raw reads were generated from leaf tissues for bothe the cultivars. Across all reads , the Q20 and Q30 percentage was more than 98 and 94%, respectively (sequencing error rate was less than 0.02%), and GC content for the libraries was ~43%. Among all the libraries, the ratio of total mapped reads was above 85%, of which ~81 % reads uniquely mapped to the reference genome . The data generated from all libraries provided a foundation for quality analyses.

研究目的：本研究旨在利用转录组测序（Transcriptome profiling, RNA-seq），对比草酸盐含量存在显著差异的菠菜品种根与叶组织中的差异表达转录本。实验设计与方法：用于RNA测序分析的叶与根组织样本，分别取自品种PI175311与Bloomsdale的各3株独立菠菜植株，经液氮快速冷冻后用于后续实验。索引标记样本的集群构建于cBot集群生成系统上完成，所用试剂为PE Cluster Kit cBot-HS（Illumina），操作严格遵循厂商说明书。集群构建完成后，利用Illumina HiSeq测序平台对文库进行测序，获得150 bp双端读段。对FASTQ格式的原始读段进行预处理：移除接头序列、含连续N碱基的读段及低质量读段，以得到清洁读段。同时计算清洁数据的Q20、Q30质量值与GC含量。参考基因组与基因模型注释文件从菠菜数据库（SpinachBase，http://spinachbase.org/）下载。利用Bowtie v2.2.3构建参考基因组索引，再通过TopHat v2.0.12将双端清洁读段比对至参考基因组。使用HTSeq v0.6.1统计比对至每个基因的读段数，通过featureCounts计算每百万文库规模每千外显子碱基的读段数（RPKM）。针对高氮与低氮处理条件（每个组织每个处理设置3次生物学重复）的差异表达分析，采用DESeq R包（1.18.0）完成（Anders和Huber, 2010），将DESeq分析得到的P值<0.05的基因定义为差异表达基因。结果：本研究对两个品种的叶、根组织共构建6个文库，并通过Illumina HiSeq平台完成测序。两个品种的叶组织平均产出约60百万条原始读段。所有读段的Q20与Q30质量占比分别超过98%与94%（测序错误率低于0.02%），文库的GC含量约为43%。所有文库的总比对读段占比均高于85%，其中约81%的读段可唯一比对至参考基因组。结论：本研究生成的所有文库数据为后续高质量转录组分析奠定了基础。