RNA Sequencing

Transcriptome refers to the sets of all transcription products in a cell under a certain physiological condition broadly, including mRNA, rRNA, tRNA and non-coding RNA. In a narrow sense, it means the set of all mRNAs. The research object of transcriptome is all mRNA of particular cell in certain state. Based on Synbio Technologies high-throughput sequencing technology platform, almost all RNA information can be sequenced to discovery differentially expressed genes in cells, tissues or individuals under different physiological or pathological conditions. Transcriptome is an inevitable link between genome 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 pathways and functional genomic studies, medicinal plants.

Competitive Advantages

  • High Data Quality: With rich experience in library construction for prokaryotic RNA sequence 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 was 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 analysis, comprehensive analysis of biomolecule function and regulatory mechanisms.

RNA Sequencing Technical Strategy

RNA-Sequencing

Service Specifications

ServiceSample TypeSequencing ModelSampling Requirements
Prokaryotic RNA Sequencing
Microorganism (≥ 5 ×107), tissue, environmental samples, total RNA, etc.
HiSeq 4000, PE150Total RNA ≥3μg, Concentration ≥70 ng/μL
Eukaryotic RNA SequencingCell, tissue, serum, plasma, total RNA, etc.
Total RNA ≥ 2μg (minimum 1μg), concentration ≥ 50 ng/μL

Project Design

The design idea of transcriptome experiment is to compare different genes, and the common type is to compare the experimental group and the control group. As time and space factors considered, multiple comparative analyses can be implementated 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
NumberAnalysis ItemNumberAnalysis Item
1Raw data processing and data quality control7Differential gene cluster analysis
2Reference genome alignment8KEGG enrichment analysis of differential genes
3Quality assessment of RNA-Seq9Antisense transcript prediction
4Gene expression level analysis10Operon analysis
5Differential gene expression analysis11sRNA analysis
6GO enrichment analysis of differential genes12Mutation analysis
2. Eukaryotic transcriptome sequencing with reference genome
NumberAnalysis ItemNumberAnalysis Item
1Raw data output statistics7KEGG annotation of Unigene
2Reference genome comparison and statistics8GO enrichment analysis of differential genes
3Analysis of gene expression abundance9KEGG enrichment analysis of differential genes
4SNP and InDel anslysis10Prediction of new transcripts
5GO enrichment analysis11Differential splicing analysis
6GO annotation for Unigene12DEU analysis (Differential Exon Usage)
3. Eukaryotic transcriptome sequencing without reference genome
NumberAnalysis ItemNumberAnalysis Item
1Raw data output statistics and quality control8KOG annotation for Unigene
2Transcript splicing9SNV/SNP analysis
3Length distribution statistics and GC content statistics of Unigene and Transcript10SSR analysis of Unigene
4Predict coding protein frame according to splicing sequence11Analysis of gene expression abundance
5Unigene functional annotation12Differential gene expression analysis
6KEGG enrichment analysis13GO enrichment analysis of differential genes
7Profile analysis of differential gene expression14KEGG enrichment analysis of differential genes

Data Analysis

gene-expression

Box diagram of gene expression distribution

PCA analysis

Differentially gene volcano plot

Differentially gene venn diagram

Clustering heatmap

KEGG pathway enrichment plot

Differential gene trend analysis

Variation locus region statistics