MRNA-Seq Related Studies in Rice
What is RNA-Seq
- RNA-seq (RNA sequencing), also called whole transcriptome sequencing, uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample representing a specific tissue and at a specific given moment in time. RNA-seq can be used to analyze the continually changing transcriptome in cells. It can facilitate the ability to look at alternative gene spliced transcripts, post-transcriptional modifications, gene fusion, mutations/SNPs and changes in gene expression. It can also be used to determine exon/intron boundaries and verify or amend previously annotated 5' and 3' gene boundaries. The de novo RNA-Seq is particularly practical in nonmodel plant species mainly because a reference genome is not required. In addition to mRNA transcripts, RNA-Seq can look at different populations of RNA to include total RNA, small RNA, such as miRNA, tRNA, and ribosomal profiling.
- The Figure 1 explains an basic overview of the workflow for analysis of RNA-Seq data. Steps 1 and 2: An average RNA-Seq experiment will yield millions of sequence reads in a Fastq file. Indexing of RNA-Seq libraries with 6 bp barcodes allows for sequencing of multiple samples in the same sequencing reaction. The indexed reads need to be demultiplexed in order to assign each read to the corresponding sample. Step 3: The quality of the obtained raw Fastq files can be checked with FastQC and adjusted when needed. Next, the reads are mapped against a reference transcriptome and/or genome; Bowtie or BWA can be used for direct alignments, and TopHat also for gapped and/or spliced alignments. Step 4: If differences in splice variants are not of interest, the total number of genome-mapped reads per gene can be summarized based upon existing gene models using HTSeq. Step 5: For downstream applications of the summarized genome and/or transcriptome-mapped reads, the count data need to be normalized according to the RPKM/FPKM, median count or TMM standard. Step 6: The summarized genome and/or transcriptome-mapped count data can be tested for significant differences in transcript abundance between samples, using the popular tools DESeq, edgeR, and baySeq. To test for differences in alternative splicing of mRNA, the splice- aligned data derived from TopHat are subjected to MiSO, Cufflinks, or DEXSeq analysis. Light blue boxes, read alignment tools; purple boxes, packages that organize the data; orange boxes, test tools for differential expression (including the normalization method incorporated in the differential expression tool). Abbreviations: RNA-Seq, next-generation RNA-sequencing; RPKM/FPKM, reads or paired-end fragments per kilobase of exon model per million mapped reads; TMM, trimmed mean of M-values.
Why choose RNA-Seq?
- Gene expression analysis is widely used to unravel regulatory mechanisms that control cellular processes in plants, animals, and microbes. Microarrays have been highly instrumental in profiling the global expression of genes, although this hybridization-based technology is largely restricted to known genes and has a limited range of quantification. RNA-Seq extends the possibilities of transcriptome studies to the analysis of previously unidentified genes and of splice variants. Moreover, RNA-Seq offers an unlimited dynamic range of quantification at reduced technical variability. These advantages, coupled with the declining cost of sequencing, make RNA-Seq an increasingly attractive method for whole-genome expression studies in many biological systems, including species with unse- quenced genomes.
The future of RNA-Seq
- The past few years witnessed a rise in the use of RNA-Seq for genome-wide transcriptome studies and will soon become standard practice. Accordingly, many methods have been developed to analyze the RNA-Seq data, but improve- ments are needed to deal with problems associated with multireads and estimating the abundance of splice variants. Upcoming third-generation sequencing, also known as single-molecule sequencing, has the potential to sequence complete transcripts at once. If indeed transcripts can be sequenced without the need of fragmentation of the cDNA and without PCR amplification, this would drastically reduce computing time and improve significantly the correct assignment of sequencing reads. This opens possibilities for whole-genome expression profiling at an un- precedented level of detail.
Projects List
| Project Title | Species | Published years | Academic Journal | RiceWiki Project ID |
|---|---|---|---|---|
| Using RNA-seq to Profile Gene Expression of Spikelet Development in Response to Temperature and Nitrogen during Meiosis in Rice (Oryza sativa L.) | Oryza sativa L. ssp. indica | 2015 | PLoS ONE | IC4R001-RNA-Seq-2015-26714321 |
| Dynamic Analysis of Gene Expression in Rice Superior and Inferior Grains by RNA-Seq | Oryza sativa L. ssp. Japnoica | 2015 | PLoS ONE | IC4R002-RNA-Seq-2015-26355995 |
| RNA-seq reveals differentially expressed genes of rice (Oryza sativa) spikelet in response to temperature interacting with nitrogen at meiosis stage | Oryza sativa L. ssp. indica | 2015 | BMC Genomics | IC4R003-RNA-Seq-2015-26576634 |
| Five pectinase gene expressions highly responding to heat stress in rice floral organs revealed by RNA-seq analysis | Oryza sativa | 2015 | Biochemical and Biophysical Research Communications | IC4R004-RNA-Seq-2015-26032497 |
| Phosphorus remobilisation from rice flag leaves during grain filling: an RNA-seq study Running title: P remobilisation from senescing rice flag leaves | Oryza sativa L. ssp. Japnoica | 2016 | Plant Biotechnology Journal | IC4R005-RNA-Seq-2016-27228336 |
| Transcriptome analysis of nitrogen-starvation-responsive genes in rice | Oryza sativa L. ssp. Japnoica | 2015 | BMC Plant Biology | IC4R006-RNA-Seq-2015-25644226 |
| Comparative Leaf and Root Transcriptomic Analysis of two Rice Japonica Cultivars Reveals Major Differences in the Root Early Response to Osmotic Stress | Oryza sativa L. ssp. Japnoica | 2016 | Rice | IC4R007-RNA-Seq-2016-27216147 |
| Transcriptome Analysis of Salt Stress Responsiveness in the Seedlings of Dongxiang Wild Rice (Oryza rufipogon Griff.) | Oryza rufipogon Griff. | 2016 | PLoS ONE | IC4R008-RNA-Seq-2016-26752408 |
| De Novo Assembly and Characterization of Oryza officinalis Leaf Transcriptome by Using RNA-Seq | Oryza officinalis | 2015 | BioMed Research International | IC4R009-RNA-Seq-2015-25713814 |
| Transcriptome analysis in different rice cultivars provides novel insights into desiccation and salinity stress responses | Oryza sativa L. | 2015 | Scientific Reports | IC4R010-RNA-Seq-2016-27029818 |
| Massive parallel sequencing of mRNA in identification of unannotated salinity stress-inducible transcripts in rice | Oryza sativa L | 2010 | BMC Genomics | IC4R011-RNA-Seq-2010-21122150 |
| Transcriptomic analysis of rice (Oryza sativa) developing embryos using the RNA-Seq technique | Oryza sativa | 2012 | PLoS One | IC4R012-RNA-Seq-2012-22347394 |
| Comparative transcriptome analysis of transporters, phytohormone and lipid metabolism pathways in response to arsenic stress in rice (Oryza sativa) | Oryza sativa | 2012 | New Phytologist | IC4R013-RNA-Seq-2012-22537016 |
| Transcriptomic analysis of rice (Oryza sativa) endosperm using the RNA-Seq technique | Oryza sativa | 2013 | Plant Molecular Biology | IC4R014-RNA-Seq-2013-23322175 |
| Global epigenetic and transcriptional trends among two rice subspecies and their reciprocal hybrids | Oryza sativa | 2010 | Plant Cell | IC4R015-RNA-Seq-2010-20086188 |
| Transcriptomes of isolated Oryza sativa gametes characterized by deep sequencing: evidence for distinct sex-dependent chromatin and epigenetic states before fertilization | Oryza sativa | 2013 | Plant Journal | IC4R016-RNA-Seq-2013-24215296 |
| Time-series RNA-seq analysis package (TRAP) and its application to the analysis of rice, Oryza sativa L. ssp. Japonica, upon drought stress | Oryza sativa L | 2014 | Methods | IC4R017-RNA-Seq-2014-24518221 |
| RNA-Seq analysis of differentially expressed genes in rice under varied nitrogen supplies | Oryza sativa | 2015 | Gene | IC4R018-RNA-Seq-2015-25447912 |
| Transcriptome analysis of rice root heterosis by RNA-Seq | Rice | 2013 | BMC Genomics | IC4R019-RNA-Seq-2013-23324257 |
| Transcriptome Analysis of Salt Stress Responsiveness in the Seedlings of Dongxiang Wild Rice | Oryza rufipogon Griff | 2016 | PLoS One | IC4R020-RNA-Seq-2016-26752408 |
