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’s 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 wide variety of biological research as well as 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
Microorganism (≥ 5 ×107), tissue, environmental samples, total RNA, etc.
 HiSeq 4000, PE150 Total RNA ≥3μg, Concentration ≥70 ng/μL
Eukaryotic RNA Sequencing Cell, tissue, serum, plasma, total RNA, etc.
Total RNA ≥ 2μg (minimum 1μg), concentration ≥ 50 ng/μL

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

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