- de novo Sequencing
- Whole Genome Resequencing
- Exome Sequencing
- Target Region Sequencing
- Whole Genome Mapping
- Sanger Sequencing
- Single-Cell DNA Sequencing
- Human MHC-Seq
- Single-Cell Sequencing
- FFPE Samples
- Immune Repertoire Sequencing
- Strong bioinformatics analysis capabilities on RNA-Seq(Quantification) data
- High reproducibility: high library construction repeatability and sequencing repeatability (Pearson correlation ≥ 0.99)
- Two platforms, Illumina Hiseq2000 and Ion Proton, are available depending on the specific needs.
- RNA-Seq (Quantification) based on Hiseq2000: high-throughput and a wide range of read lengths (50 ~150bp read lengths are available)
- RNA-Seq (Quantification) based on Ion Proton: rapid turnaround time and longer read sequencing length (average read length is 100-120bp).
- Wide detection range: accurate quantification of a wide range of transcripts(from a few to hundreds of thousands), enabling identification of rare transcripts
- High accuracy: RPKM method analysis for gene expression
"As the principal investigator on two sequencing projects that are pushing the state-of-the-art (viral discovery using metagenomic sequencing and phylogenomic analysis of 1000 plant species), I cannot expect everything to work on the first iteration but I do expect my sequencing provider to work together with me to correct whatever went wrong regardless of whose fault it was. BGI-Shenzhen does that admirably."
- Dr. GaneKa-Shu Wong, Professor and iCORE Chair in Biosystems Informatics Department of Biological Sciences, University of Alberta
BGI has validated the reproducibility, sensitivity, and detection range of RNA-Seq(Quantification).
Some results are as follows:
Materials: Human Brain Reference RNA (HBRR) from FirstChoice ®;
Universal Human Reference RNA (UHRR) from Stratagene
Standardized method of gene expression analysis: RPKM (Reads per kb per million reads) is used to quantify the gene expression level. The method is able to eliminate the influence of different gene lengths and sequencing discrepancy in the measurement.
Figure 1: Technical Replicate Results of RNA-Seq (Quantification). From the technical replicate data, the spearman R is 0.992 and 0.993, respectively.
The results suggest that RNA-Seq has high reproducibility.
Figure 2: Correlation between results from RNA-Seq (Quantification) and qPCR RNA-Seq is a highly accurate quantitation method. The scatter-plot of figure 2 show a high correlation between results from RNA-Seq and qPCR. Spearman R is 0.915 and slope is 1.022.
Figure 3: Detection Range of RNA-Seq (Quantification) is wider than microarray, and RNA-Seq can detect more low abundance genes than micriarray.
Analysis of Transcriptome Differences between Resistant and Susceptible Strains of the Citrus Red Mite Panonychus citri (Acari: Tetranychidae). Plos One. DOI: 10.1371. (2011).
The citrus red mite is a worldwide citrus pest and a common sensitizing allergen of asthma and rhinitis. It has developed strong resistance to many registered acaricides, However, the molecular mechanisms of resistance remain unknown. In this study, a comparative transcriptome study was carried out in the susceptible strain (SS) and resistant strain of the Citrus red mite to study mite resistance. 2,701 differentially expressed genes (DEGs) based on the uniquely mapped reads were identified by the comparing the differences between RS and SS. Moreover, we identified 211 metabolism genes and target genes related to general insecticide resistance such as P450 and Cytochrome b, and further compared their differences between RS and SS.
Standard Bioinformatics Analysis
- Data filtering includes removing adaptors, contamination and low-quality reads from raw reads
- Assessment of sequencing
- Gene expression annotation.
Advanced Bioinformatics Analysis
- Differential gene expression analysis
- Expression pattern analysis of differentially expressed genes (DEGs)
- Gene ontology enrichment analysis of DEGs
- Gene ontology classification (WEGO analysis)
- Pathway enrichment analysis of DEGs
- Protein-protein interaction network analysis
Custom Bioinformatics Analysis
- We can also perform customized analyses to meet the requirements of specific projects.
- Sample condition: Integrated total RNA samples that have been treated with DNase; Avoid protein contamination during RNA isolation
- Sample quantity (for library construction): Total RNA ≥ 5μg
- Sample concentration: ≥ 200 ng/µL
- Sample purity: OD260/280 = 1.8-2.2; OD260/230 ≥ 1.8; for animal RIN ≥ 7.0, for plant and fungi RIN ≥ 6.5; 28S:18S ≥ 1.0
The standard turnaround time for the workflow (above) is based on the platform utilized: ~30 business days (Hiseq2000 platform) and ~20 business days (Ion Proton platform).