Transcriptome is a collection of all the transcripts present in a given cell or a population of cells. Transcriptome research may reveal the molecular mechanism of a particular biological process or a disease, and has been widely applied in the areas of basic research, clinical diagnostics as well as drug discovery.
Transcriptome sequencing, or RNA-seq, is to apply NGS technology on cDNA that are reverse- transcribed from total RNA, so as to capture a snapshot of the transcriptome at a given time point. RNA-seq not only can quickly and comprehensively investigate the dynamics of gene expression profile changes, but also can accurately interrogate sequence or structure variants on the transcriptome. RNA-seq is also a superior technology in detecting low aboundant or new transcripts.
Technique Highlights
– Unlike microarray which relay on prior knowledge of known transcripts for probe designing, RNA- seq is able to detect both known and novel transcripts without the requirement for probe designing.
– RNA-seq offers greater specificity and sensitivity for quantifying gene expression changes compared to other existing technology platforms. This is because RNA-seq calculates the expression level by digital counting of sequence reads which can be uniquely mapped to the specific loci of the genome. Issues such as cross-hybridization, non-ideal hybridization often encountered in microarray will not be seen in RNA-seq.
– RNA-seq quantifies a broader dynamic range of expression levels compared to microarray since this quantification is not limited by background at lower end and signal saturation at high end.
– RNA-seq is able to discriminate expression differences between highly similar gene family members or between highly similar splicing variants, which is a challenge with microarray.
– RNA-seq not only can accurately quantify expression level but also offer extra valuable information such as transcript border determination, RNA editing, alternative splicing, gene fusion.
Sample Requirement
1. Sample type: Total RNA without degradation or DNA contamination.
2. Starting amount of total RNA: >= 20 ug/sample
3. Sample conc.: 100 ~ 200 ng/uL
4. Sample purity: OD260/280>=1.8; 28S/18S >=1.5
5. Sample Integrity: 2100 Bioanalyzer RIN value >=8.0
Service Workflow
1. Cusomtized sequencing experimental design
2. Sample collection
3. RNA extraction and QC
4. Library preparation
5. Bioinformatics analysis
6. Project report
Library Preparation Workflow
Data Analysis Workflow
Deliverables
1. Raw data stored as FASTQ files
2. Overview of sequencing data such as total data output, genome coverage, genome distribution, sequencing depth across the length of low/medium/high expressed transcript.
3. Reference gene annotation.
4. Quantitation and differential expression analysis.
5. Expression profile clustering analysis
6. GO terms enrichment analysis
7. Pathway-based analysis
8. Gene regulation network construction
9. alternatie splicing variants identification
10. Novel transcripts prediction and annotation
11. Indel and SNP detection
12. Customized data analysis according to specific project requirements
