What is Whole Transcriptome Sequencing?
Novogene’s Whole Transcriptome Sequencing service equips the researcher with cutting-edge NGS solutions that provide in-depth bioinformatic analysis on all transcripts including mRNAs and non-coding RNAs. This competitive approach investigates and explores potential transcriptional and regulatory network mechanisms, while providing key insights into interaction and intersection functionality from a comprehensive transcriptomic perspective.Service Specifications
Whole Transcriptome Sequencing finds its applications in:
- Profiling mRNA and ncRNA in a single run
- Exploring miRNA sponge and target regulatory elements
- Investigating regulatory networks among lncRNA/circRNA-miRNA-gene pairs
Benefits of Whole Transcriptome Sequencing
- Whole Transcriptome Sequencing provides a more comprehensive analysis of transcriptional regulation network, compared to mRNA-seq, lncRNA-seq, sRNA-seq, and circRNA-seq, respectively.
- Whole transcriptome sequencing help researchers in identifying biomarkers across a wide transcript range.
- WTS allows the capture of both known and new features
- It enables the whole transcriptome profiling across a broad dynamic scale
|Library Type||Sample Type||Amount||RNA Integrity Number
|lncRNA Library & small RNA Library||Total RNA||≥ 3 μg||Animal ≥ 7.5, Plant ≥ 7, with smooth baseline||OD260/280 = 1.8-2.2;
OD260/230 ≥ 1.8;
|lncRNA library & small RNA library & circRNA library||≥ 5 μg|
Sequencing Parameter and Analysis
|Platform||Illumina Novaseq 6000|
|Read length||Paired-end 150bp for lncRNA/circRNA library
Single-end 50bp for small RNA library
|Recommended Data Amount||≥ 40 million read pair per sample (lncRNA library);
≥ 20 million read pair per sample (small RNA library);
|Content of Data Analysis||
The first step of the project workflow includes the sample quality control (Sample QC) to ensure that your samples meet the criteria of the RNA-Seq technique. Then, the appropriate library is prepared according to your target organism and subsequently tested for its quality (Library QC). Next, a paired-end 150 bp sequencing strategy is used to sequence the lncRNA and circRNA library and single-end 50bp is used to sequence the small RNA library. The resulting data go through quality data control (Data QC) to guarantee the quality of the resulting data. Finally, bioinformatic analyses are performed and publication-ready results are provided. The following flowsheet describes the step-by-step protocol.
Total RNA was extracted from the spinal cord dorsal horn tissue
1. lncRNA library: NEBNext UltraTM Directional RNA Library Prep Kit for Illumina
2. small RNA library: NEBNext Multiplex Small RNA Library Prep Set for Illumina
3. sequenced on Illumina HiSeq 2500 platform, 125 bp paired-end and 50-bp single-end reads, respectively.
This study comprehensively identifies regulated ncRNAs of the spinal cord and to demonstrate the involvement of different ncRNA expression patterns in the spinal cord of NP pathogenesis by sequence analysis. This information will enable further research on the pathogenesis of NP and facilitate the development of novel NP therapeutics targeting ncRNAs.
Correlation analysis of circRNA and miRNA and mRNA
The circRNA-miRNA-gene triplets in which circRNA, miRNA, and genes are all differentially expressed were selected, and their numbers were counted in each comparison.
Figure 1 circRNA-miRNA-gene counting and networks
The histogram graph shows the number of differentially expressed genes, circRNA, miRNA in lncRNA-miRNA-gene triplets in each comparison
circRNA harbors miRNA
binding sites and acts as a molecular sponge for a miRNA to competitively keep miRNA from suppressing its downstream target genes of the corresponding miRNA family. Based on the competitive endogenous RNA (ceRNA) hypothesis, we filter out circRNA and genes targeted by the same miRNA and construct ceRNA regulation networks to reveal the expression regulation mechanisms of circRNA at the whole transcriptome level.
Figure 2 Interaction network of circRNA-miRNA-gene
In the figure, different shapes represent different RNA types, and different colors represent the up-and down-regulation of
the RNA genes
The size of a node is proportional to its degree. If more lines are connected to a node, its degree will be greater, and correspondingly the size will also be larger. These nodes are more likely to be in a core position in the network.
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