Gene-expression analysis

Sequencing of all protein-coding genes

This is why you should use gene-expression analysis:

Transcriptome analysis is the most popular method since it provides a total overview of the gene-expression levels in your sample. All protein-coding (poly-A containing) transcripts are consistently and accurately represented. It 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.

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 5 ng. It is applicable for FFPE-material or other (partly) degraded and challenging samples (see below).

For whole blood analysis, we offer globin reduction that removes the globin transcripts originating from erythrocytes from your sample prep, so you reduce the sequencing capacity that is required for your sample with ~30-40%. 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.

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.

See how we work with you on your project:

  • mRNA measurement based on poly-A selection
  • Globin reduction for whole blood samples is optional
  • Optimal total RNA input: 100 ng / sample
  • Ideally deliver ≥250 ng RNA to allow for QC and normalization
  • Minimally validated RNA input: 10 ng
  • For RNA quality of RIN / RQN ≥7
  • Sequencing of poly-A containing transcripts
  • Quality score Q30 of ≥80% for PE 150 reads
  • TAT: 4-6 weeks
  • Sequencing on NovaSeq 6000 PE150 or NextSeq 500 SR75
  • Gene-expression analysis: 20 million reads

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This amount is sufficient for performing a statistically sound transcriptome analysis. It is a  more accurate and complete alternative for micro-array sequencing. Contact customer support if you require another amount of reads per sample.

  • Structural variant analysis

To determine all structural variants, such as isoforms, splice variants and gene-fusions we advise a sequencing depth of 50 – 100 million reads per sample. You will receive a full transcriptome analysis, as wells as a reliable overview of SNPs and structural rearrangements.

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The specifications of the data-set that you can expect are listed in your personal quotation. Generally, the sequencing quality score (Q score; ≥30 represents high quality) must be ≥80% for PE 150 reads (Illumina’s official guarantee ≥75%). Our average score of 2018 was
Q30 ≥ 90%.

A              Gene-expression analysis (raw data)
B              Count table
C              PCA plot, heatmap, pathway analysis

Discuss you project with Raymond

We have years of experience in the field. Talk with us about best practices and how we can help your research.

Raymond Egging

Director Marketing & Sales Diagnostics/R&D

+31 71 568 1050