Long-Read Epitranscriptomics Sequencing
RNA methylations have important epigenetic functions, and they are of great significance to study the spatiotemporal specificity of epigenetics. CD Genomics's whole transcriptome methylation sequencing is based on the long-read sequencing platforms. Combined with whole-genome bisulfite processing and bioinformatics data analysis, we perform low-cost, high-efficiency, and high-accuracy transcriptome-wide RNA methylation mapping.
Overview
Epitranscriptomics has received extensive attention in the scientific community, and it can study the effect of methylation modifications carried by RNA on gene expression. To date, more than one hundred modifications have been found on RNA. These modifications are largely distributed on non-coding RNAs (ncRNAs), especially rRNAs, tRNAs and snRNAs, and are necessary for the normal functions of ncRNAs in translation and splicing.
CD Genomics utilizes long-read sequencing platforms to achieve whole-transcriptome methylation detection. The long-read sequencing technologies of PacBio SMRT and Oxford Nanopore have the characteristics of high throughput and long read length, and have the advantages of high efficiency and single base resolution. Without the need for additional sequence amplification, the long-read sequencing can directly obtain the regional methylation information of the whole transcriptome, which provides an effective solution for assessing methylation in human, plant, microorganism and other transcriptomes. Long-read sequencing enables scientists to predict gene function and regulation as well as other key biological and clinical features more accurately.
Features
| High Efficiency | Transcriptome-Wide | Experienced Scientist Team | Professional Bioinformatics |
|---|---|---|---|
| Adopts carefully optimized experimental procedure, achieving high efficiency and specificity. | Accurate localization of RNA methylation sites in the whole transcriptome. | Can provide a full set of professional services from experimental design, sample testing, data analysis, etc. | Strong bioinformatic team provides conventional analysis and in-depth data analysis. |
Project Workflow
1. Sample Preparation
RNA purification; quality assessment and quantification.
2. Library Preparation
cDNA enrichment; cDNA library preparation.
3. Sequencing
Circular consensus sequencing (CCS); continuous long read (CLR) sequencing.
4. Data Analysis
Visualize and preprocess results, and perform custom bioinformatics analysis.
Bioinformatics Analysis Pipeline
In-depth data analysis:
- Sequencing data filtering and quality control
- Methylation detection and annotation: methylation site distribution, structural annotation, methylation level calculation, sequence characteristics
- Comparative analysis: principal component analysis, correlation analysis, cluster analysis
- KEGG/GO analysis
- COG analysis
- NR analysis
- Pfam analysis
- SwissProt analysis
Sample Requirements
Tissue sample: above 100 mg
Cell sample: cell amount: above 1×107
RNA sample: RNA quantity ≥ 5 μg; RNA purity: OD260/280 = 1.6~2.3; OD260/230 ≥ 1.5; RNA quality: 28S:18S ≥ 1.5 or RIN ≥ 7
Please make sure that the RNA is not significantly degraded.
Sample storage: Cell samples or fresh tissue pieces (cut into 5-10 mg pieces) can be treated with TRIzol or RNA protectant, frozen in liquid nitrogen, and stored at -80°C. RNA samples can be dissolved in ethanol or RNA-free ultrapure water, and stored at -80°C. Avoid repeated freezing and thawing during sample storage.
Shipping Method: When shipping RNA samples, the RNA sample is stored in a 1.5 mL Eppendorf tube, sealed with sealing film. Shipments are generally recommended to contain 5-10 pounds of dry ice per 24 hours.
Deliverable: FastQ, raw data, coverage summary, QC report, experiment results, custom bioinformatics analysis.
References:
- Javaran VJ, Moffett P, Lemoyne P, et al. Grapevine virology in the third-generation sequencing era: From virus detection to viral epitranscriptomics. Plants. 2021 Nov;10(11).
- Motorin Y, Marchand V. Analysis of RNA modifications by second-and third-generation deep sequencing: 2020 update. Genes. 2021 Feb;12(2).
