
Apparently the job application already appears closed, despite the deadline being today. Please contact us via email instead if you would like to apply!
The main focus of this computational biology group is the analysis of microbiome data, from both the human gut and the environment. We integrate metagenomic data with associated metadata and other omics data to develop a global understanding of the interactions between bacteria and their environment and to gain insight on the development and progression of diseases. Current and past projects include:
In addition, we are involved in projects with other groups at EMBL, e.g. to study the interactions of proteins in the model bacterium Mycoplasma pneumoniae. For more information on our research, please also see our homepage on the main EMBL website.
Fecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associations
Nishijima et al., Cell, 2024
Paternal microbiome perturbations impact offspring fitness
Argaw-Denboba et al., Nature, 2024
SPIRE: a Searchable, Planetary-scale mIcrobiome REsource
Schmidt et al., Nucleic Acids Research, 2024
Functional and evolutionary significance of unknown genes from uncultivated taxa
Rodríguez del Río et al., Nature, 2023
Drivers and determinants of strain dynamics following fecal microbiota transplantation
Schmidt et al., Nature Medicine, 2022
A faecal microbiota signature with high specificity for pancreatic cancer
Kartal et al., Gut, 2022
2022: Towards the biogeography of prokaryotic genes by Coelho et al.
2021: Combinatorial, additive and dose-dependent drug-microbiome associations by Forslund et al.
2018: Structure and function of the global topsoil microbiome by Bahram et al.
2018: Extensive impact of non-antibiotic drugs on human gut bacteria by Maier et al.
2015: TARA Oceans special issue in Science, e.g. Structure and function of the global ocean microbiome by Sunagawa et al.
2015: Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota by Forslund et al.
2013: Genomic variation landscape of the human gut microbiome by Schloissnig et al.
2011: Enterotypes of the human gut microbiome by Arumugam et al.
2010: A human gut microbial gene catalogue established by metagenomic sequencing by Qin et al.
2008: Drug Target Identification Using Side-Effect Similarity by Campillos et al.
2005: Comparative Metagenomics of Microbial Communities by Tringe et al.
2002: Functional organization of the yeast proteome by systematic analysis of protein complexes by Gavin et al.
Apparently the job application already appears closed, despite the deadline being today. Please contact us via email instead if you would like to apply!
We're starting to analyse the incoming microbiome data, with several researchers using mainly sequence-based approaches. Now we are looking for additional expertise: if you are working in environmental modelling, soil ecology, biogeochemistry, ecophysiology, systems biology, etc. please apply! (2/2)
We're looking for a postdoc on the Computational Analysis of Environmental #Microbiome Data! https://embl.wd103.myworkdayjobs.com/en-US/EMBL/job/Postdoctoral-Fellow---Computational-Analysis-of-Environmental-Microbiome-Data_JR844
In the past two years, @embl.org scientists have gathered more than 3000 soil, sediment & water samples from Europe's coastlines within the #TREC expedition (1/2)
We're once again hosting the Human #Microbiome conference at @EMBLEvents , organized by Ami Bhatt, Nicola Segata, Mani Arumugam and Peer Bork! We always have a great lineup of speakers, so register now and think about an abstract to submit (abstract submission deadline in June) https://s.embl.org/ees25-08-ma
Hot off the press: "Fecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associations" https://www.sciencedirect.com/science/article/pii/S0092867424012042
Here is our thread on the preprint https://mstdn.science/@BorkLab/112122558187549698
If you have human gut microbiome data (metagenomics or 16S), you can use our tool at https://microbiome-tools.embl.de/mlp/ to predict cell counts, so that you can find out if any microbiome changes in your study population are actually driven by changes in bacterial cell counts