Computational microbiome and microbial pathogen research

BIFO studies 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. 

Our research 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 focuses on the following problems and questions:

  • Can we identify biomarkers for clinically relevant phenotypes from microbiome data using artificial intelligence approaches and reliably predict these phenotypes?
  • Which software with which settings is particularly well suited for processing different types 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 and pathogens to a certain environment leave in the microbiome and their genomes? Specifically we are interested in this question 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? 

Researchers

  • Dr. Zhiluo Deng
  • Dr. Fernando Meyer
  • Dr. Philipp Münch
  • Dr. Nasim Safaei
  • Steven Medina
  • Dr. Ehsaneddin Asgari (associated)

Collaborators

  • Benjamin Maasoumy, Anke Kraft, Markus Cornberg, 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
  • the CAMI initiative
  • Till Strowig, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany

Past collaborators

  • Justin O’Grady & Gemma Kay, Quadram Institute, Norwich, UK
  • Thomas Schulz, Hannover Medical School, Hannover, 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

Selected publications

*Z.-L. Deng, *N. Safaei, S. *L. Schütte, V. Ohlendorf, *B. Maasoumy, *A. C. McHardy. High Prevalence and Local Dissemination of Daptomycin-Resistance Mutations for Enterococcus faecium in Cirrhotic Patients (*shared first and senior authors). doi: 10.1053/j.gastro.2025.08.046. Gastroenterology 2026, 170(1): 228–230.

K. Hu, F. Meyer, Z.-L. Deng, E. Asgari, T. H. Kuo, P. C. Münch, A. C. McHardy. Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes. doi: 10.1093/bib/bbae206. Briefings in Bioinformatics 2024, 25(3): bbae206.

C. Huttenhower, R. D. Finn, A. C. McHardy. Challenges and opportunities in sharing microbiome data and analyses. doi: 10.1038/s41564-023-01484-x. Nature Microbiology 2023, 8(11): 1960–1970.

P. C. Münch, C. Eberl, S. Woelfel, …, C. Huttenhower, *A. C. McHardy, *B. Stecher. Pulsed antibiotic treatments of gnotobiotic mice manifest in complex bacterial community dynamics and resistance effects (*shared last authors). doi: 10.1016/j.chom.2023.05.013. Cell Host & Microbe 2023, 31(6): 1007–1020.e4.

F. Meyer, A. Fritz, Z.-L. Deng, …, A. C. McHardy. Critical Assessment of Metagenome Interpretation: the second round of challenges. doi: 10.1038/s41592-022-01431-4. Nature Methods 2022, 19(4): 429–440.

Z.-L. Deng, P. C. Münch, R. Mreches, A. C. McHardy. Rapid and accurate identification of ribosomal RNA sequences via deep learning. doi: 10.1093/nar/gkac112. Nucleic Acids Research 2022, 50(10): e60.

P. C. Münch, E. A. Franzosa, B. Stecher, *A. C. McHardy, *C. Huttenhower. Identification of Natural CRISPR Systems and Targets in the Human Microbiome (*shared last authors). doi: 10.1016/j.chom.2020.10.010. Cell Host & Microbe 2021, 29(1): 94–106.e4.

A. Khaledi, A. Weimann, M. Schniederjans, E. Asgari, T. H. Kuo, A. Oliver, G. Cabot, A. Kola, P. Gastmeier, M. Hogardt, D. Jonas, M. R. Mofrad, A. Bremges, *A. C. McHardy, *S. Häussler. Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics (*shared last authors). doi: 10.15252/emmm.201910264. EMBO Molecular Medicine 2020, 12(3): e10264.

A. Sczyrba, P. Hofmann, P. Belmann, …, A. C. McHardy. Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software. doi: 10.1038/nmeth.4458. Nature Methods 2017, 14(11): 1063–1071.

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. doi: 10.1038/nmeth0311-191. Nature Methods 2011, 8(3): 191–192.