Computational Biology of Infection Research

The research of the group focuses on the data-driven analysis of biological questions from infection research, as well as method development to solve prediction problems for large biological data sets.

Leader

Computational microbiome research

A second research focus of the department is the study of microbial communities and their adaptation to and interactions with the human host from meta‘omics data. 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. We focuses on method development for meta’ome data analysis, promoting the development of standards and best practices via the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI) and meta’ome analyses in collaboration with researchers from HZI and the German Center for Infection Research (DZIF).  

The department currently focuses on the following problems and questions:

  • 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. 
  • Which software is particularly well suited for processing different kinds of metagenome samples? A. McHardy founded and organizes, together with A. Sczyrba and T. Rattei 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).
  • 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.  

Selected publications

  1. A. Sczyrba, P. Hofmann, P. Belmann, …, T. Rattei, A.C. McHardy 
    Critical Assessment of Metagenome Interpretation—a benchmark of computational metagenomics software.
    Nature Methods 2017, doi:10.1038/nmeth.4458
  2. P.C. Münch, B. Stecher, A.C. McHardy 
    EDEN: Evolutionary Dynamics within Environments. 
    Bioinformatics 2017, 33(20): 3292–3295
  3. B.J. Kunath, A. Bremges, A. Weimann, A.C. McHardy, P.B. Pope
    Metagenomics and CAZyme discovery. 
    Methods in Molecular Biology 2017, 1588, 255-277
  4. I. Gregor, A. Schönhuth, A.C. McHardy 
    Snowball: strain aware gene assembly of metagenomes. 
    Bioinformatics 2016, 32(17): i649-i657
  5. I. Gregor, J. Dröge, M. Schirmer, C. Quince, A.C. McHardy 
    PhyloPythiaS+: a self-training method for the rapid reconstruction of low-ranking taxonomic bins from metagenomes. 
    PeerJ 2016, 4: e1603
  6. J. Dröge, I. Gregor, A.C. McHardy 
    Taxator-tk: precise taxonomic assignment of metagenomes by fast approximation of evolutionary neighborhoods. 
    Bioinformatics 2015, 31(6): 817-24
  7. D. Bulgarelli, R. Garrido-Oter, P.C. Münch, A. Weimann, J. Dröge, Y. Pan, A.C. McHardy, P. Schulze-Lefert 
    Structure and function of the bacterial root microbiota in wild and domesticated barley. 
    Cell Host Microbe 2015, 17(3): 392-403
  8. S. Hacquard, R. Garrido-Oter, A. González, S. Spaepen, G. Ackermann, S. Lebeis, A.C. McHardy, J.L. Dangl, R. Knight, R. Ley, P. Schulze-Lefert 
    Microbiota and Host Nutrition across Plant and Animal Kingdoms. 
    Cell Host Microbe 2015, 17(5): 603-616
  9. P.B. Pope, W. Smith, S.E. Denman, S.G. Tringe, K. Barry, P. Hugenholtz, C.S. McSweeney, A.C. McHardy, M. Morrison 
    Isolation of Succinivibrionaceae implicated in low methane emissions from Tammar wallabies.
    Science 2011, 333(6042): 646-648
  10. 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

Researchers

  • Dr. Andreas Bremges
  • Dr. Till Robin Lesker
  • Dr. Fernando Meyer
  • Ehsaneddin Asgari
  • Peter Belmann
  • Adrian Fritz
  • Peter Hofmann
  • Philipp Münch

Current and past collaborators

  • Barbara Stecher, Medical Microbiology and Hospital Epidemiology, Max von Pettenkofer Institute, Ludwig Maximilian University of Munich, Munich, 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
  • Nadine Ziemert, Natural Product Genome Mining, Eberhard Karls University of Tübingen, Tübingen, Germany (DZIF collaboration)
  • Alexander Sczyrba, Aaron Darling, Thomas Rattei, Tanja Woyke…und die weitere CAMI Initiative
  • Johannes Gescher, Institute of Applied Biosciences (IAB), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
  • Till Strowig, Microbial Immune Regulation, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany
  • Dietmar Pieper, Microbial Interactions and Processes, Helmholtz Centre for Infection Research (HZI), Braunschweig, 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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