Gene expression profiling provides an affordable approach to examine differential gene expression analysis between groups of samples, such as various treatments, time-points, or disease versus control samples.
Why analyse gene expression?
A sensitive indicator of changes in biological processes
Our ISO-accredited service includes all novel features: Unique Molecular Identifiers (UMIs), identification of antisense transcripts, and can handle a broad range of input RNA, starting from 10ng. All protein-coding (poly-A containing) transcripts are consistently and accurately represented.
For whole blood analysis, we offer globin reduction that removes the globin transcripts originating from erythrocytes, so you reduce the sequencing capacity that is required per sample with 30 to 40%. The removal of ribosomal RNA is not necessary, since these transcripts do not contain a poly-A tail. For custom transcript removal or targeted RNA seq-approaches, contact our scientific support team.
1. Initial meeting
2. Sample delivery
- Optimal total RNA input: 250ng-1000ng / sample
- Ideally deliver ≥250ng RNA to allow for QC and normalization
- Minimally validated RNA input: ≥10ng
- For RNA quality of RIN / RQN ≥7
3. Sample entry QC
4. Library prep/QC and sequencing
- Gene expression analysis: 20 million reads
This amount is sufficient for performing a statistically sound gene expression analysis. Contact customer support if you require another amount of reads per sample.
5. Data QC
Your project manager checks the data quality by analyzing the quality metrics of the run. Final inspection of the data takes place and the dataset is transferred onto our portal. It is also possible to receive your data on hard disk.
- TAT: 3-5 weeks
- Gene-expression analysis (raw data)
- Quality score Q30 of ≥80% for PE 150 reads
- Count table (optional)
- Differential gene expression analysis (optional)
- PCA plot, heatmap, pathway analysis (optional)
Our experienced bioinformaticians generate quality assured data-sets or deliver publication-ready results through our validated pipelines. For more demanding projects our experts work closely with you to provide a fully custom analysis.
Principle component analysis, gene ontology enrichment and hierarchical clustering are examples of our extensive data analysis options for gene expression profiling.
Structural variant analysis
To determine structural variation such as isoforms, splice variants and gene-fusions we advise a sequencing depth of 50 – 100 million reads per sample.
We have summarized key information about our gene expression profiling service into a service specification sheet.
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