Cancer computational biology software

The Gerstung group develops software for analysing cancer genomics data, and tools for translational research. Here, we provide links to some of our most commonly used software.

AML multistage predictions

The complete source code for our analysis of AML multistage predictions can be found on our github page. The analysis comes with an online calculator, which is currently hosted at The tool runs in a Docker image which can be found on Docker hub as gerstunglab/aml-multistage.


An R package with high-dimensional extensions of the Cox proportional hazards model. To be used for survival predictions based on high-dimensional genomic covariates. Available through GitHub


Quantitative variant callers for detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs. The new shearwater algorithm (beta) computes a Bayes classifier based on a beta- binomial model for variant calling with multiple samples for precisely estimating model parameters such as local error rates and dispersion and prior knowledge, e.g. from variation data bases such as COSMIC. Available through GitHub

Genome campus fellowships

ESPOD postdoctoral programme