Bioinformatics and computational biodiversity

The group focusses on the bioinformatic analysis of high throughput sequencing data in biodiversity research. We apply computational techniques to investigate eco-evolutionary theories and hypotheses in microorganisms and the link between biodiversity and ecosystem functions using molecular data. These includes metagenome and -transcriptome studies for freshwater habitats, transcriptome and genome assemblies of protist model species (Chrysophyceae) and the assessment of their molecular reactions and responses to environmental stressors.



The newly funded CRC RESIST (SFB 1439) aims to understand and explain the mechanisms underlying the degradation of and recovery from multiple stressors in stream ecosystems.

For more information see projects page and on the CRC website:



The application of high throughput sequencing for exploring biodiversity poses high demands on bioinformatics workflows for automated and reproducible data processing. Natter has been constructed using Snakemake to be a highly scalable, flexible and reproducible workflow for the processing of raw amplicon sequencing data. The workflow contains all analysis steps from the quality assessment over read assembly, dereplication, chimera detection, split-sample merging, OTU-generation, to the taxonomic assignment of OTUs.
Available at:


TaxMapper is an analysis tool for a reliable mapping to a provided microeukaryotic reference database and part of a comprehensive Snakemake. It is used to assign taxonomic information to each NGS read by mapping to the database and filtering low quality assignments. Additionally, TaxMapper is part of a metatranscriptome Snakemake workflow developed to perform quality assessment, functional and taxonomic annotation and (multivariate) statistical analysis including environmental data. The workflow is provided and can be easily adapted for metatranscriptome analysis of any environmental sample.
For download see:


R package for the integrated, functional analysis of biological networks. The nodes of a network, e.g. PPI, are scored by transforming p-values from statistical test on groups of *omics data. Subsequently, a maximum-scoring subnetwork is calculated that represents the most differentially regulated module of genes.
For detailed information see:


The xHeinz project is coordinated by the Algorithmic Bioinformatics group headed by Gunnar Klau in Düsseldorf:



Jun. 2018. Across European Freshwaters - Assessment of Protist Diversity and Functions using High-Throughput Sequencing and TaxMapper. NIOZ. Texel, The Neatherlands.

Aug. 2017. A metatranscriptome workflow and its application to European freshwater ecosystems. ICOP. Prague, Czech Republic.

Sept. 2016. Taxonomic assignment of protist metatranscriptomes. ECCB workshop “W11 – Recent Computational Advances in Metagenomics (RCAM’16)”. Den Hague, The Netherlands.

Feb. 2014. Current Opportunities and Challenges in Protist (Meta-) Transcriptome Analysis - A Bioinformatic Perspective. 33rd annual meeting of the German Society for Protozoology. Essen, Germany.

Jun. 2011. Robust Subnetworks – Computing Confidence Values for Functional Modules. Ascona Workshop: Statistical Challenges and Biomedical Applications of Deep Sequencing Data. ETH Zürich, Acona, Switzerland.

Nov. 2010. BioNet - Routines for the functional analysis of biological networks. Bioconductor Developer Meeting Europe, EMBL, Heidelberg, Germany.

Apr. 2010. Robust subnetworks – Confidence scores for integrated functional modules. Group of Ian Overton, Medical Research Council, Human Genetics Unit, Western General Hospital, Edinburgh, UK.

Apr. 2010. Robust subnetworks – Confidence scores for integrated functional modules. Group of Florian Markowetz, Cancer Research UK, Cambridge Research Institute, Cambridge, UK.