GSE103300
Analysis of Genes with Alternatively Spliced Transcripts in the Leaf, Root, Panicle and Seed of Rice using RNA Sequencing
Purpose: RNA-Sequencing was applied to differentiate transcriptomes among the leaf, root, 1-cm panicle and young seed. Methods: All four organs in this study were prepared from Oryza sativa L. cv. Ilmi. Leaves and roots were harvested separately at 14 days after sowing in soil. Flowers before pollination were collected in the range of 0.5-1.5-cm panicles (P1cm). Seeds at 20-30 days after pollination (S21DAP) were collected at the internal ripening stage of the grains. Total RNA was isolated from tissues using Hybrid-RTM (GeneAll, Korea) extraction following the manufacturer's manual.  mRNA was purified using the TruSeq RNA sample preparation kit (http://www.illumina.com/) following the company's protocol. Following purification, the mRNA was fragmented, and first-strand cDNA was generated using reverse transcriptase and random primers. Second-strand cDNA synthesis was then performed using DNA polymerase I and RNase H. These cDNA fragments were subjected to an end-repair process, the addition of a single A base, and adaptor ligation. The products were purified and enriched by polymerase chain reaction (PCR) to create a cDNA library in a bridged amplification reaction that occurred on the surface of the flow cell. A flow cell containing millions of unique clusters was loaded into a HiSeq 2000 for automated cycles of extension and imaging. Each sequencing cycle was carried out in the presence of all four nucleotides, generating a series of images with each representing a single base extension at a specific cluster. Libraries were constructed for the leaf, root, P1cm, and S21DAP. Repetitions were performed for all samples, except roots. In the initial sequencing, 2.7-0 Gb was sequenced at each end. These results generated 16-43 X 106 paired-end reads. Results: Programs such as TopHat, Cufflinks and HTSeq were used to process sequence reads. Differential exon usage was examined with the DEXseq package in Bioconductor. Briefly, the rice genome sequence (IRGSP-1.0_2014-06-25) in the RAP database (http://rapdb.lab.nig.ac.jp) was indexed using Bowtie 2 with an FM Index based on the Burrows-Wheeler transform (Langmead et al., 2009). Reads in FASTQ files were aligned using TopHat (v2.0.4) with default parameters (Kim et al., 2013). TopHat aligned the RNA-Seq reads to genomes using the short read aligner Bowtie 2 and then analyzed the mapping results to identify splice junctions between exons. In the program, samtools (version 1.2) was used for indexing and sorting of bam and sam files. For transcriptome assembly and isoform quantitation from the RNA-Seq reads, Cufflinks was used and tested for comparison (Trapnell et al., 2012). Prior to statistical analysis of exons using the DEXseq package, per-gene read counts were performed with htseq-count in HTSeq-0.6.1 using the annotated gtf file (Anders et al., 2015). To extract exons among transcripts, non-overlapping exonic regions were defined with dexseq_prepare_annotation.py, and then the per-exon read counts were extracted with the script dexseq_count.py in the DEXSeq package (Anders et al., 2012). For each exon of each gene, the data contain the number of reads in each sample that overlap with the exon. If an exon's boundary is not the same in all transcripts, the exon is separated into two or more parts, referred to as ''counting bin''. A read that overlaps with several counting bins of the same gene is counted for each of these. The program returned one file for each biological replicate with the exon counts. Finally, differential exon usage was assessed with the DEXseq package in Bioconductor (http://bioconductor.org). In the package, generalized linear models (GLMs) for model read counts and parameters are calculated from a negative binomial (NB) distribution of these count data (McCullagh and Nelder 1989). Conclusions: In this study, rice RNA-Seq was applied to differentiate alternatively spliced transcripts between representative rice organs, including the leaf, root, panicle at 1 cm, and young seed. Transcripts were classified according to organ enrichment, and the results are collectively explained in terms of evolutionary pressure. The portion of loci with multiple transcripts due to alternative splicing was higher among constitutively expressed genes than organ-preferential loci. The allowance of alternative splicing is maximized by avoiding expression of the other alternative splicing transcript under unwanted circumstances. Among loci, transcripts with a longer CDS tended to be more highly expressed than those with a shorter CDS. These genome-wide technologies might be useful for studying transcriptomes and their biological significance with regard to pre-mRNA splicing. Overall design: A flow cell containing millions of unique clusters was loaded into a HiSeq 2000 for automated cycles of extension and imaging. Each sequencing cycle was carried out in the presence of all four nucleotides, generating a series of images with each representing a single base extension at a specific cluster. Libraries were constructed for the leaf, root, P1cm, and S21DAP. Repetitions were performed for all samples, except roots. In the initial sequencing, 2.7-0 Gb was sequenced at each end. These results generated 16-43 X 106 paired-end reads.