A transcriptome refers to the sets of all transcription products in a cell under a certain physiological condition, including mRNA, rRNA, tRNA, and non-coding RNA. In short, it means the set of all mRNAs. The research scope of transcriptomes is all mRNA of a particular cell in certain state. Based on Synbio Technologies’ high-throughput sequencing technology, almost all RNA information of a tissue or organ can be sequenced comprehensively. Eukaryotic and prokaryotic RNA sequencing is used to discover expressed genes in cells, tissues, or individuals under different physiological or pathological conditions. A transcriptome bonds a genome’s genetic information and biological functions. Nowadays, RNA sequencing is widely applied to a variety of biological research, clinical diagnosis, and drug development.
Applications
- Medical Research: Disease markers, disease diagnosis and classification, disease recurrence diagnosis, disease mechanism, clinical efficacy evaluation, drug toxicology evaluation, personalized therapy.
- Life Science Research: Abiotic environmental relationships, plants and microorganisms, phenotypic identification, metabolic pathway and functional genomic studies, medicinal plants.
Competitive Advantages
- High Data Quality: Rich experience in library construction for prokaryotic RNA sequencing to reach good rRNA removal efficiency.
- High Coverage: High or low abundances can be identified and quantified simultaneously.
- Strand-specific RNA-seq Library: The dUTP strand-specific RNA-seq library is used to ensure the directivity of transcripts and accurate quantitative results.
- Comprehensive Analysis: Specific probes and reference genomic information are not necessary to detect genes but also to discover new transcripts.
- Integrative Analysis of Multiomics: Full spectrum & comprehensive analysis of biomolecule function and regulatory mechanisms.
Competitive Advantages
Service Specifications
| Service | Sample Type | Sequencing Model | Sampling Requirements |
|---|---|---|---|
| Prokaryotic RNA Sequencing | HiSeq 4000, PE150 | Total RNA ≥3μg, Concentration ≥70 ng/μL | |
| Eukaryotic RNA Sequencing | Cell, tissue, serum, plasma, total RNA, etc. |
Project Design
The design idea of a transcriptome experiment is to compare different genes, and the common type is to compare the experimental group and the control group. With time and space factors considered, multiple comparative analyses can be implemented according to different growth stages or the occurrence and development of diseases. At least 3 biological replicates are required for each group.
Analysis Items
1. Prokaryotic transcriptome sequencing
| Number | Analysis Item | Number | Analysis Item |
|---|---|---|---|
| 1 | Raw data processing and data quality control | 7 | Differential gene cluster analysis |
| 2 | Reference genome alignment | 8 | KEGG enrichment analysis of differential genes |
| 3 | Quality assessment of RNA-Seq | 9 | Antisense transcript prediction |
| 4 | Gene expression level analysis | 10 | Operon analysis |
| 5 | Differential gene expression analysis | 11 | sRNA analysis |
| 6 | GO enrichment analysis of differential genes | 12 | Mutation analysis |
2. Eukaryotic transcriptome sequencing with reference genome
| Number | Analysis Item | Number | Analysis Item |
|---|---|---|---|
| 1 | Raw data output statistics | 7 | KEGG annotation of Unigene |
| 2 | Reference genome comparison and statistics | 8 | GO enrichment analysis of differential genes |
| 3 | Analysis of gene expression abundance | 9 | KEGG enrichment analysis of differential genes |
| 4 | SNP and InDel anslysis | 10 | Prediction of new transcripts |
| 5 | GO enrichment analysis | 11 | Differential splicing analysis |
| 6 | GO annotation for Unigene | 12 | DEU analysis (Differential Exon Usage) |
3. Eukaryotic transcriptome sequencing without reference genome
| Number | Analysis Item | Number | Analysis Item |
|---|---|---|---|
| 1 | Raw data output statistics and quality control | 8 | KOG annotation for Unigene |
| 2 | Transcript splicing | 9 | SNV/SNP analysis |
| 3 | Length distribution statistics and GC content statistics of Unigene and Transcript | 10 | SSR analysis of Unigene |
| 4 | Predict coding protein frame according to splicing sequence | 11 | Analysis of gene expression abundance |
| 5 | Unigene functional annotation | 12 | Differential gene expression analysis |
| 6 | KEGG enrichment analysis | 13 | GO enrichment analysis of differential genes |
| 7 | Profile analysis of differential gene expression | 14 | KEGG enrichment analysis of differential genes |
Data Analysis
Box Diagram of Gene Expression Distribution
PCA Analysis
Differential Gene Volcano Plot
Differential Gene Venn Diagram
Cluster Heatmap
Cluster Heatmap
Differential Gene Trend Analysis
Variation Locus Region Statistics
