RNA-Seq (Transcriptome)
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RNA-Seq (Transcriptome) is used for transcriptome quantification and structural analysis. The transcriptome analysis lays the foundation for gene structure and function research. RNA-Seq delivers unbiased transcriptome information for basic and medical research, pharmacogenomics research, and drug discovery and development.

Benefits:

  1. BGI’s extensive bioinformatics analysis expertise for RNA-Seq data
  2. Breadth of BGI’s experience in sequencing diverse species (de novo and with reference genome)
  3. BGI’s rapid turnaround time leads to faster publication and reduced R&D cost
  4. RNA-Seq, unlike microarrays, does not require prior knowledge of the genome and therefore offers the following advantages:
    1. Discover novel transcripts
    2. Identify alternative splicing
    3. Study transcriptome polymorphisms
    4. Examine gene fusion events

Customer Testimonials:

"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. Gane Ka-Shu Wong, Professor and iCORE Chair in Biosystems Informatics Department of Biological Sciences - University of Alberta

The 1000 Plants de novo Transcriptomes Project

The 1000 plants de novo Transcriptomes Project plans to use new generation technology to de novo sequence and assemble the transcripts of 1,000 plants. This is an initiative project funded by the government of Alberta. BGI is one of the major participants of the 1000 Plants Initiative. BGI initially seeks to increase the number of plant species for which transcript sequence information is publicly available and to learn about their biology and evolutionary history. In later phases, the initiative might focus on commercial applications of the results. Fewer than 100 plant genomes have been characterized by sequencing so far, even at the EST level, judged by data submitted to GenBank. This project will greatly expand the knowledge of plant biodiversity.

Deep RNA Sequencing at Single Base-Pair Resolution Reveals High Complexity of the Rice Transcriptome. Genome Research 2010:646-654.

Deep RNA

RNA sequencing enabled the detection of transcripts expressed at an extremely low level. The results suggest that transcriptional regulation in rice is vastly more complex than previously believed.

Bioinformatics:

BGI Tech provides two types of bioinformatics analyses: de novo and transcriptome resequencing.

De novo Transcriptome Assembly

  1. Standard bioinformatics analysis
    1. Data filtering includes removal of adaptors and low-quality reads from raw reads
    2. Statistics analysis and evaluation of data
    3. Assembly results (Contig length distribution, Unigene* length distribution)
    4. Unigene function annotation and COG classification
    5. Unigene GO classification
    6. Unigene pathway analysis
    7. Coding region sequence (CDS) prediction
    8. Analysis of differential unigene expression (two or more samples should be provided)
    9. Gene ontology (GO) classification and pathway enrichment analysis of differentially expressed unigenes (two or more samples should be provided)
    10. SNP analysis (eukaryotes only)
    11. SSR marker identification and primer design (eukaryotes only)
  2. Advanced bioinformatics analysis
    1. Principal component analysis (PCA) (five or more samples should be provided)
    2. Condition–specific expression analysis (five or more samples should be provided)

Transcriptome Resequencing

  1. Standard bioinformatics analysis (reference genes, genome sequences, and gene annotation should be provided)
    1. Data filtering includes removal of adaptors, contamination, and low-quality reads from raw reads
    2. Assessment of sequencing (alignment statistics, randomness assessment of sequencing, and distribution of reads along the reference gene)
    3. Gene expression and annotation (gene coverage and coverage depth)
    4. Analysis of differential gene expression (two or more samples should be provided)
    5. Expression pattern analysis of differentially expressed genes (DEGs) (eukaryotes only)
    6. Gene ontology analysis of DEGs (eukaryotes only)
    7. Pathway enrichment analysis of DEGs (eukaryotes only)
    8. Refinement of gene structures (eukaryotes only)
    9. Identification of alternative spliced transcripts (eukaryotes only)
    10. Prediction and annotation of novel transcripts
    11. SNP analysis
  2. Advanced bioinformatics analysis
    1. Gene fusion analysis (for human only, preferably more than 15 pairs of control + case)
    2. Principal component analysis (PCA) (five or more samples)
    3. Condition–specific expression analysis (five or more samples)
    4. Correlation analysis between samples
    5. Venn diagram of gene distribution specific to one sample and/or across all test samples
    6. Visualization of genome alignment results using IGV
    7. Protein-protein interaction network analysis
    8. Transcription factor analysis (plant only)
    9. KEGG pathway enrichment scatter diagram
    10. Transcript quantification
    11. Exon quantification
    12. InDel analysis
    13. RNA editing analysis

Duplex-Specific Nuclease (DSN) Normalization Transcriptome Sequencing Bioinformatics Analysis

  1. The analysis content is the same as that performed in de novo transcriptome sequencing except for the analysis of differentially expressed genes

Custom Bioinformatics Analysis

  1. We can also perform other customized analyses to meet the requirements of specific projects.

Sample Requirements:

Regular transcriptome sequencing

  1. Sample conditions: Total RNA samples that have been treated with DNase. Avoid protein contamination during RNA isolation.
  2. Sample quantity (for single library construction):
    1. Pant and fungi: total RNA ≥ 20μg
    2. Bacteria: total RNA ≥ 5μg
    3. Mammal (human, rat and mouse): total RNA ≥ 5μg
    4. Other species: total RNA ≥ 10μg
  3. Sample concentration:
    1. Plant and fungi: ≥ 250ng/µl
    2. Bacteria: ≥ 65ng/µL
    3. Mammal (human, rat and mouse): ≥ 65ng/uL
    4. Other samples: concentration ≥ 150ng/µl
  4. Sample purity:OD260/280 = 1.8-2.2, OD260/230 ≥ 2.0
    1. Plant and fungi: 28S:18S RNA ≥ 1.0, RIN ≥ 6.5
    2. Bacteria: 23S:16S RNA ≥ 1.0, RIN ≥ 7.0
    3. Animal: 28S:18S RNA ≥ 1.0, RIN ≥ 7.0

Low-input transcriptome sequencing

Total RNA isolated from tumors or dead tissue can often be in limited amounts and poor quality. However, some of these sources are of clinical importance. To consistently achieve high-quality data from these types of samples, we recommend low-input transcriptome sequencing.
  1. Sample conditions: Total RNA samples that have been treated with DNase. Avoid protein contamination during RNA isolation.
  2. Sample quantity (for single library construction):
    1. Pant and fungi: total RNA ≥ 2μg
    2. Mammal (human, rat and mouse): total RNA ≥ 200ng
    3. Other species: total RNA ≥ 1μg
  3. Sample concentration:
    1. Plant and fungi: ≥ 250ng/µl
    2. Mammal (human, rat and mouse): ≥ 65ng/uL
    3. Other samples: concentration ≥ 150ng/µl
  4. Sample purity:OD260/280 = 1.8-2.2, OD260/230 ≥ 2.0
    1. Plant and fungi: RNA 28S:18S ≥ 1.0, RIN ≥ 6.5
    2. Animal: RNA 28S:18S ≥ 1.0, RIN ≥ 7.0

Turnaround Time:

The standard turnaround time for the workflow (above) is 40 business days.

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