Gene expression analysis is the simultaneous evaluation of expression levels of thousands of genes in different samples. Expression profiling has been around for almost two decades by means of the microarray technology, and has well established data analysis procedures.   With the recent sequencing based technologies however it is possible to overcome the shortcomings of microarray: limited coverage. This allows a possibility to study isoform switching, post transcriptional changes, gene fusion and novel isoforms.


A typical expression analysis workflow follows these steps:


You have designed an experiment for differential expression analysis, using next generation sequencing (RNA-seq).  E.g. you have samples of two different conditions and want to identify novel isoforms switching between the two conditions.


You run a pilot sequencing for some samples.


We automate the workflow to be able to feed in the rest of the sequence data.


On completion, you download and explore the results. You have an option to have the resulting Excel table with expression values and candidate genes and alignment files, or you can also have an interactive web interface, with which you can explore your data further, as you can see in http://…. Here you can search among candidate genes based on different aspects e.g. gene functions, pathways or sequence similarity.


Optionally we can help you with the experiment design, e.g. with statistical power calculation or sample number calculation, or to decide if it’s better to use NGS or to use microarray technology.


We together develop a workflow for the raw data analysis, fine tune based on the pilot data (e.g. using tophat, cufflinks), assess the results of the pilot data, to see the validity of the assumptions. The basic steps are:
  • quality assessment
  • junction mapping
  • quantification


We run the analysis, you can check the progress on a web interface. We can optionally run the analysis on multiple Amazon AWS instances to speed up the process.