At the Section of Microbiology, we are working to understand and exploit the immense functional diversity and adaptive potential of natural microbial communities using cutting-edge molecular techniques including high throughput DNA sequencing, cell-sorting, single-cell detection, and real time biofilm formation at the Bioflux centre.
Microbes play vital roles including recycling of compounds in nature and in human and animal health. Despite the ability to live as single cells, bacteria and fungi have evolved over billions of years to engage in multicellular networks dominated by complex interactions. We believe that research based on a holistic perception of the microorganisms as integrated members of complex microbial communities will lead to future breakthroughs in the research for a better environment and for human health.
Our research on complex microbial communities is conducted within four main topics:
At the section of Microbiology we use a selection specialist technology platforms such as high throughput sequencing or in situ reporter systems to investigate microbial interactions in environments, host associated microbiomes or in vitro or synthetic model systems. We are constantly refining and improving these systems by continued development and collaboration with experts from around the world. If you think some of these may be useful for you or your department, or have an idea for a common project we would like to hear from you.
Contact us for collaborationTake a closer look at our short introduction to the microscope and find our contact details .
Just a sequence awayContact us for more information on how we can collaborate on sequencing of your data
CRISPRCasTyper is a new automated software with improved capabilities for identifying and typing CRISPR arrays and cas loci across prokaryotic sequences, based on the latest classification and nomenclature (44 subtypes/variants). Besides subtyping CRISPR-Cas loci we also implemented a novel feature which subtypes CRISPR arrays based on the sequence composition of the direct repeats. This allows the typing of orphan and distant CRISPR arrays which, for example, are commonly observed in fragmented metagenomic assemblies.
Furthermore, the tool provides a graphical output, where CRISPRs and cas operon arrangements are visualized in the form of colored gene maps. This can aid in annotation of partial and novel systems through synteny, and also a simple way to check what genes are flanking the CRISPR-Cas loci.
Check out our preprint for all the features and for a benchmark showing the high accuracy of CRISPRCasTyper: https://doi.org/10.1101/2020.05.15.097824
DAtest - differential abundance analysis
DAtest is an R package made for aiding researchers in choosing a differential abundance method. There are a plethora of methods for conducting differential abundance analysis, but no gold standard. DAtest uses a permutation and spike-in technique to evaluate the sensitivity and specificity of the different methods on your own dataset. Furthermore, we made it simple to run multiple methods and compare their output, thereby making it easy to assess the robustness of the results.
RCon3D - quantitative 3D analyses of confocal microscopy images
RCon3D is an R package for high-throughput quantitative 3D analyses of confocal microscopy images of microbial biofilms, microcolonies and communities. With RCon3D you can do co-aggregation analyses (how likely are two strains to aggregate together), occupancy analysis (what is occupying the space around a specific strain), finding and quantifying aggregates, dynamic sectioning (split in top and bottom biofilm based on biomass), and much more.
The package is available at GitHub: https://github.com/Russel88/RCon3D, and has been used in several papers, such as:
Amplicon data analysis
We have a full tutorial for analysing amplicon sequencing data all the way from raw data to figures and statistics. It is tailored for students and researchers who are new to the R programming language and to statistical analysis. We have tried to make it comprehensive including both background material, links to in-depth material, and of course snippets of R code to conduct the analyses. We use it in our course The Human Microbiome - The Experiment, and we made it public as other students or researchers might find it useful.
The tutorial is available at: https://microucph.github.io/amplicon_data_analysis/
At the Section of Microbiology we believe that research based on a holistic perception of the microorganisms as integrated members of complex microbial communities will lead to future breakthroughs in the research for a better environment and for human health.
Our research has shown that evolution is not only driven by the survival of the fittest, but maybe even more so by survival of the best collaborating partners.
We try to transpose this realization to the way we work within the group and with external partners.
If you are curious on whom our partners are, you can find more information if you follow the LINK