By merging the NGS technology, bisulfite sequencing, and restriction enzymes, Novogene provides genome-wide methylation profiles at very affordable costs.
Applications
- Applications in clinical research fields, such as tumor-subtype classification, molecular marker identification, drug target location determination, and pathological mechanism studies
- Applications in crop development, crop adaptability, and agronomic traits in the agricultural sector
Benefits
- Cost-effective relative to WGBS in mammalian research.
- Precise localization of methylation sites with single-base resolution.
- Covers millions of CpG sites genome-wide.
- Low input DNA requirement.
Specifications: DNA Sample Requirements
| Sample Type | Required amount | Purity |
| Genomic DNA | ≥ 1 μg | A260/280=1.8-2.0 |
Specifications: Sequencing and Analysis
| Sequencing Platform | Illumina NovaSeq 6000 Sequencing System |
| Read Length | Paired-end 150 bp |
| Recommended Data Amount | ≥ 10 Gb clean data per sample |
| Content of Data Analysis | Data quality control
MspⅠ cutting efficiency Alignment to the reference genome mC-calling Methylation level and frequency distribution DMS detection DMRs and DMPs detection and annotation Enrichment analysis of differentially methylated genes Visualization of BS seq data |
Project Workflow
The Novogene RRBS service comprises four steps. The first step involves sample preparation which is followed by library preparation, sequencing, and finally data analysis using bioinformatics pipelines. To construct a methylation library, RNA fragments of various lengths are created using a restriction enzyme treatment, followed by sodium bisulfite conversion of unmethylated cytosines into uracils. Libraries are then sequenced using Illumina PE150 and eventually data extraction and bioinformatics analysis are performed.
Novogene audits every experimental step strictly to ensure the accuracy and reliability of the sequencing data. The quality control of the RRBS library pool is performed to ensure high-quality data output fundamentally. Obtaining good-quality data is the premise to ensure that bioinformatics analysis is correct, comprehensive, and credible.
