Computational Biology for Infection Research

The Department of “Computational Biology for Infection Research” studies the human microbiome, viral and bacterial pathogens, and human cell lineages within individual patients by analysis of large-scale biological and epidemiological data sets with computational techniques. Focusing on high throughput meta’omics, population genomic and single cell sequencing data, we produce testable hypotheses, such as sets of key sites or relevant genes associated with the presence of a disease, of antibiotic resistance or pathogenic evasion of immune defense. We interact with experimental collaborators to verify our findings and to promote their translation into medical treatment or diagnosis procedures. To achieve its research goals, the department also develops novel algorithms and software.


Computational microbiome research

A research focus of BIFO is the study of microbial communities, including bacteria, viruses and eukaryotic community members, and their relevance for human health and disease. The human microbiota is implicated in a variety of diseases and subject of experimental studies at HZI. Direct metagenome, -transcriptome or -proteome sequencing of microbial community samples enables the study of the majority of microorganisms that cannot be obtained in pure culture, corresponding to the vast majority of the microbial world.

Research in BIFO focuses on establishing data-driven computational approaches that further advance individualized infection medicine in the clinic, such as computational biomarker discovery from microbial omics data, i.e. genotype-phenotype and genotype-environment inference, and the data-driven discovery molecular predictors of host disease status and pathogen phenotypes. We also develop methods for common meta’ome data types, and promote the development of standards and best practices via the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI).

BIFO currently focuses on the following problems and questions:

  • Can we identify biomarkers for clinically relevant phenotypes from microbiome data using machine learning approaches and reliably predict these phenotypes? This is particularly relevant for the analysis of cost-efficient 16S data, which however does not encode any information about the functional gene repertoire of a sample.  
  • Which software is particularly well suited for processing different kinds of metagenome samples? A. McHardy founded and organizes (together with A. Sczyrba) CAMI, the Initiative for the Critical Assessment of Metagenome Interpretation, which aims to establish standards and best practices in metagenome analysis by organizing benchmarking challenges for method developers.
  • Can we reconstruct the genomes of individual strains from metagenomics data? This question has large clinical relevance, as individual strains of the same species can have very different phenotypes (e.g. the probiotic E. coli Nissle versus the EHEC strain).
  • Which traces does the adaptation of microbial communities to a certain environment leave in the microbiome? Specifically we are interested in this question for the human microbiota and for the spread of antibiotic resistances.
  • What can we learn about the role of the microbial CRISPR-CAS system in the human microbiome by systematic metagenome analyses combined with deep learning techniques?

Selected publications

  1. F. Meyer, A. Bremges, P. Belmann, S. Janssen, A. C. McHardy, D. Koslicki
    Assessing taxonomic metagenome profilers with OPAL.
    Genome Biology 2019, 20, 1:51

  2. A. Fritz, P. Hofmann, S. Majda, E. Dahms, J. Droge, J. Fiedler, T. R. Lesker, P. Belmann, M. Z. DeMaere, A. E. Darling, A. Sczyrba, A. Bremges, A. C. McHardy
    CAMISIM: Simulating metagenomes and microbial communities
    Microbiome 2019, 7, 1:17

  3. E. Asgari, P. C. Munch, T. R. Lesker, A. C. McHardy*, M. R. K. Mofrad*
    DiTaxa: Nucleotide-pair encoding of 16S rRNA for host phenotype and biomarker detection (*shared last authors)
    Bioinformatics 2018, Epub: bty954

  4. E. Asgari, K. Garakani, A. C. McHardy, M. R. K. Mofrad
    MicroPheno: predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples
    Bioinformatics 2018, 34: i32

  5. A. Bremges, A. C. McHardy
    Critical Assessment of Metagenome Interpretation Enters the Second Round
    mSystems 2018, 3: e00103

  6. F. Meyer, P. Hofmann, P. Belmann, R. Garrido-Oter, A. Fritz, A. Sczyrba, A. C. McHardy
    AMBER: Assessment of Metagenome BinnERs
    GigaScience 2018, 7

  7. A. Sczyrba, P. Hofmann, P. Belmann, …, T. Rattei, A.C. McHardy
    Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software.
    Nature Methods 2017, doi:10.1038/nmeth.4458

  8. P.C. Münch, B. Stecher, A.C. McHardy
    EDEN: evolutionary dynamics within environments.
    Bioinformatics 2017, 33(20): 3292–3295

  9. K. Patil, P. Haider, P.B. Pope, P.J. Turnbaugh, M. Morrison, T. Scheffer, A.C. McHardy        
    Taxonomic metagenome sequence assignment with structured output models.                                                                                                                                  
    Nature Methods 2011, 8(3): 191-192


  • Dr. Fernando Meyer
  • Dr. Ehsaneddin Asgari
  • Dr. Till Robin Lesker
  • Dr. Zhiluo Deng
  • Adrian Fritz
  • Philipp Münch
  • Tzu-Hao Kuo



  • Justin O’Grady & Gemma Kay, Quadram Institute, Norwich, UK
  • Markus Cornberg, Hannover Medical School, Hannover, Germany
  • Thomas Schulz, Hannover Medical School, Hannover, Germany
  • Curtis Huttenhower, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.
  • Barbara Stecher, Medical Microbiology and Hospital Epidemiology, Max von Pettenkofer Institute, Ludwig Maximilian University of Munich, Munich, Germany
  • Phil Pope and Vincent Eijsink, Norwegian University of Life Sciences, Aas, Norway
  • Nadine Ziemert, Natural Product Genome Mining, Eberhard Karls University of Tübingen, Tübingen, Germany (DZIF collaboration)
  • Alexander Sczyrba, Aaron Darling, Tanja Woyke…and the further CAMI initiative
  • Till Strowig, Microbial Immune Regulation, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany


  • Paul Schulze-Lefert, Max Planck Institute for Plant Breeding Research, Cologne, Germany
  • Phil Pope and Vincent Eijsink, Norwegian University of Life Sciences, Aas, Norway
  • Johannes Gescher, Institute of Applied Biosciences (IAB), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
  • Mark Morrison, CSIRO Livestock Industries, Queensland, Australia
  • Jeffrey Gordon and Peter Turnbaugh, Center for Genome Sciences, Washington University, St. Louis, Missouri, USA
  • Phil Hugenholtz, Australian Center for Ecogenomics, Queensland, Australia
  • Isidore Rigoutsos, Computational Medicine Center, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
  • Andreas Brune, Research Group Leader, Department of Biogeochemistry, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
  • Mila Chistoserdova, Department of Chemical Engineering, University of Washington, Seattle, Washington, USA
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