Bioinformatics in biodiversity and biomedicine

One focus of my current research is in the field of (eukaryotic) microbial ecology and limnology. Here we use and develop different biostatistical and bioinformatic methods to study ecosystems, their microbial community, ecologically relevant organism groups, ecosystem functions and how they are influenced by environmental factors and stressors using molecular methods.
Another focus is on medical research using computational methods in human genetics, oncology, medical microbiology, and radiation biology; applications include structural variant detection, analysis of novel pathogenic organisms, and exome and transcriptome analyses in genetic diseases.



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:



Here, we present an updated version of the pipeline, Natrix2, which incorporates VSEARCH as an alternative clustering method with better performance for 16S metabarcoding approaches and mothur for taxonomic classification on further databases, including PR2, UNITE and SILVA. Additionally, Natrix2 includes the handling of Nanopore reads, which entails initial error correction and refinement of reads using Medaka and Racon to subsequently determine their taxonomic classification.
Available at:


The application of high throughput sequencing for exploring biodiversity poses high demands on bioinformatics workflows for automated and reproducible data processing. Natrix 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.