Eukaryotic RNA sequencing is based on Next Generation Sequencing (NGS) and provides comprehensive and rapid access to all transcript sets of a particular cell or tissue in certain state. It is used to study gene structure and function, alternative splicing and prediction of new transcript, etc. Eukaryotic RNA sequencing can be divided into reference transcriptome and non-reference transcriptome according to with or without reference genomes.
Eukaryotic non-reference RNA sequencing is the sequencing of all RNA transcribed by specific tissues or cells of eukaryotes in a specific situation; Eukaryotic reference RNA sequencing is the sequencing of all mRNA transcribed by specific tissues or cells of eukaryotes in a specific situation. Compared with the reference genome, it can not only comprehensively and rapidly analyze the mRNA sequence and abundance information, but also the gene structure and the new transcript.
Competitive Advantages
- High Quality: With the advanced HiSeq 4000 sequencing platform, high-quality s data can be read quickly and efficiently.
- High Coverage: Digital signals directly measure the sequence of almost all transcript fragments.
- Stand-specific Library Construction: Use dUTP stand-specific library construction proposal to ensure the directionality and accurate quantitative results of transcripts.
- Whole Transcriptome Analysis: Specific probes or genomic information are not necessary, we can directly perform the most comprehensive transcriptome analysis on any species.
- Correlation Analysis: Combining multiple data groups, carrying out cross-group correlation analysis.
Service Procedures
Service specifications
| Services | Sample Type | Sequencing Model | Sampling Requirements |
|---|---|---|---|
| Eukaryotic RNA Sequencing | Cell, tissue, serum, plasma, total RNA, etc. | HiSeq 4000, PE150 | Total RNA ≥ 2g (minimum 1g) Concentration ≥ 50 ng/μL |
Analysis items
- Eukaryotic RNA Sequencing — Reference Genome Sequence
- Output statistics of original data
- Comparison and statistics with reference genome
- Expression abundance analysis of genes and transcripts
- Transcriptome level SNP and InDel analysis
- GO functional analysis
- The GO annotation result of Unigene
- KEGG metabolic pathway annotation of Unigene
- GO functional enrichment analysis of differentially expressed genes and KEGG metabolic pathway enrichment analysis
- Prediction of new transcript
- Variable shear analysis
- DEU (Different Exon Usage) analysis(limit biological repeat samples)
- Eukaryotic RNA Sequencing — Non Reference Genome Sequence
- Output statistics of original data and basic quality control processing
- Transcriptome splicing (according to sequencing data)
- Transcript length distribution and GC content statistics of Unigene
- Predicted coding protein frames (CDS) based on the splicing result sequence
- Unigene functional annotation (NR, Swissport, KOG, KEGG, Pfam)
- Analysis of KEGG metabolic pathways
- Gene differential expression profile analysis (clustering diagram, scatter diagram, volcanic diagram)
- The KOG annotation result of Unigene
- Transcriptome level SNV/SNP analysis
- SSR analysis of Unigene (SSR data information, SSR distribution statistics)
- Analyze gene expression abundance according to RPKM calculation criteria (expression data information and expression statistics)
- Analysis of differences in gene expression between samples
- GO functional enrichment analysis of differentially expressed genes and KEGG metabolic pathway enrichment analysis
