Bioinformatics

Data analysis is crucial for the successful completion of your Next Generation Sequencing (NGS) project.

This is why you should use total transcriptome:

Standard gene-expression analysis is performed on protein-coding RNA, since this provides you the fastest and affordable method to visualize gene-expression. With Total transcriptome, you extend your vision towards all transcripts, including long non-coding RNA (lncRNA) or other regulatory RNA species.

Total RNA sequencing helps you to overcome common causes for RNA degradation, such as FFPE-fixation and laser-capture methods. Additionally, precious samples from a cohort might not be handled optimally, blocking the only source to obtain transcriptome profiles for your study. Our scientists have set up validated procedures to analyze these challenging samples. This includes clinical-grade sample isolation from FFPE material and tailored data-analysis.

A ribosomal RNA (rRNA) depletion step is usually required, since the 16S and 18S ribosomal subunits make up ~60-90% of all RNA species present in your sample. You make better use of the sequencing space by removing them at the sample preparation phase. What remains is messenger RNA, and other types of RNA such as long non-coding RNAs (lncRNAs).

See how we work with you on your project:

  • Measurement of all transcripts
  • rRNA depletion and globin reduction are optional
  • Challenging samples: The average RNA transcript size must exceed 50 bp to allow for unique mapping and analysis. Check your (column-)isolation procedure to determine starting from which size (bp) the RNA fragments are retained.
  • 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 RIN / RQN of all qualities;
  • Stranded sequencing of all RNA transcripts, including rRNA depletion
  • Quality score Q30 of ≥80% for PE 150 reads
  • TAT: 4-6 weeks
  • Sequencing on NovaSeq 6000 PE150
  • Gene-expression analysis: 40 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 80 – 120 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