The Raes Lab combines large-scale sequencing, cohort studies, microbiology and synthetic ecology with computational approaches to investigate the functioning and variability of the human microbiome and study its alteration and modulation in disease.
Recent technological advances such as metagenomics and next-generation sequencing make it possible, for the first time, to study the various microbiota of the human body at a previously unseen scale. These advances have allowed the initiation of the International Human Microbiome Project, aiming at genomically characterizing the totality of human-associated micro-organisms (the “microbiome”).
Studying the complexity of the human ecosystem at this resolution allowed the birth of a new, exciting subfield in computational biology which will eventually allow classical, cellular-level systems biology to progress towards modeling entire communities (“ecosystems biology”) and untangling interspecies networks of competition, collaboration and communication at the molecular level.
At the same time, human cohort and intervention studies allow important insights in the functioning and variability of the healthy human microbiome and its components and investigate its alteration in disease. In this context, we recently discovered the existence of gut flora types (enterotypes) linked to diet, motility and inflammation and are studying the predictive power of microbial markers for various intestinal diseases (auto-immune, neurological and metabolic pathologies as well as cancer). In addition, we integrate metagenomics, metatranscriptomics and meta-metabolomics data to study functional consequences of dysbiosis. Using in vitro synthetic ecology apporaches, we investigate modulation strategies to be developed in novel microbiota-based therapies, ranging from diet, pre- and (next-gen)probiotics, faecal transplants as well as drugs.
Population-level analysis of gut microbiome variation.
In the framework of the Tara Oceans Project, we studied the interactions between planktonic organisms to show how important the ocean is for life on our planet
Plankton networks driving carbon export in the global ocean.
Determinants of community structure in the global plankton interactome
We focus on the development of computational methods for the analysis of (next-generation) sequence data and the investigation of community properties from metagenomics, metatranscriptomics and meta-metabolomics data.
CoNet – Cytoscape plugin
CoNet is a tool that detects significant non-random patterns of co-occurrence (copresence and mutual exclusion) in incidence and abundance data. It has been designed with (microbial) ecological data in mind, but can be applied in general to infer relationships between objects observed in different samples (for example between genes present or absent across organisms). CoNet runs on command line and as a Cytoscape plugin [http://apps.cytoscape.org/apps/conet]
You can install CoNet from the Cytoscape App Store or from CoNet's website where you can also find Tutorials and Documentation.
Gomixer - Get your gut meta-omics data analysis and visualization in one GO!
GOmixer is a tool dedicated to the functional analysis and visualization of gut metaomics data. With a data matrix and a few clicks, GOmixer shows which pathways are over or under represented in the dataset. Datasets analyzed outside of GOmixer, can benefit from its visualization features to explore the relationship between pathways and species-function associations.
GOmixer is available as a web application
- What is GOmixer?
GOmixer is a tool dedicated to the functional analysis and visualization of gut metaomics data. With a data matrix and a few clicks, GOmixer shows which pathways are over or under represented in the dataset. Start .Datasets analyzed outside of GOmixer, can benefit from its visualization features to explore the relationship between pathways and species-function associations. Start.
- Why GOmixer?
Metaomic resources (i.e. tools, databases, ...) are spread over the web and often present generic solutions for all environmental data whether it is gut, soil or ocean.GOmixer solves this problem by using a gut-specific metabolic module framework and by integrating all its services into one streamlined web application.
Glyphicons Free licensed under CC BY 3.0. [https://creativecommons.org/licenses/by/3.0/]
© Raes lab
A tool to generate metabolic modules profiles from metagenomic samples
GMMs is available as a command-line tool from https://github.com/raeslab/GMMs
LotuS - less OTU scripts and sdm
LotuS - less OTU scripts and sdm - simple demultiplexer complete 16S amplicon pipeline and quality filtering of sequences.
Visit LotuS' Website for installation, tutorials and documentation.
QMP - Quantitative Microbiome Profiling
An R-script covering the different steps of quantitative microbiome profiling (QMP) as described in the article 'Quantitative microbiome profiling links gut community variation to microbial load'
QMP is available for download from https://github.com/raeslab/QMP
GBM -Gut-brain modules - Manually curated gut-brain modules
A database of manually curated gut-brain modules (GBMs), based on extensive literature review. Each GBM corresponds to a single neuroactive compound production or degradation process.
It is available for download here: GBMs.zip
Valles-Colomer, M. et al. The neuroactive potential of the human gut microbiota in quality of life and depression. Nat. Microbiol. (2019).
QMP² - QMP in PSC and IBD
Quantitative microbiome profiling (QMP) matrix for the study dataset used in the article 'Quantitative microbiome profiling disentangles inflammation- and bile duct obstruction-associated microbiota alterations across IBD/PSC diagnoses'