Example: RNA-Seq Analysis Workflow

We facilitate transcriptome analysis of tissues and single cells with next-generation bulk and single-cell sequencing technology. Our RNA-seq analysis workflows incorporate the latest top-performing publicly available tools and proprietary programming solutions that cover all steps of the data processing, starting from quality control of the raw data up to the statistical analysis of the gene expression values.

Bulk RNA-Seq Data Analysis

For a typical bulk RNA-seq experiment, once the raw sequencing data are available, the standard analysis package consists of:

  • quality assessment of the raw data
  • alignment of the reads to genome/transcriptome
  • data mining, including principal component analysis and clustering
  • identification of differentially expressed genes between treatment groups using a multivariate statistical model
  • pathway annotation and visualization of differentially expressed genes

Example volcano plot showing the results of a statistical test for scoring differential gene expression between two groups of samples.


The advanced data analysis package will always be custom-tailored to your project according to your needs. For example, such a package can contain:

  • a thorough literature review of the biological/medical context or the experiment
  • a deeper investigation of genes relevant in the context
  • assessment of the effect of additional sample characteristics on gene expression patterns
  • based on all observed effects, more advanced statistical models will be set up to find genes that differentially express genes even at lower effect sizes
  • more detailed annotation and biological/clinical interpretation of the differentially expressed genes

Example heatmap showing the clustering of samples and genes based on highly variable genes.