About

About me

I am a curious and dedicated bioinformatics young scientist. I was, and still is, mostly intrigued by microbial communities within food products (e.g., cheeses, sourdough) and within hosts (e.g., plants, animals). I like to gain knowledge about them using tools I’m familiar with such as DNA sequences and the associated bioinformatics toolbox, but I look forward to gain more insights on them by learning new skills. My computational training owes a lot to the open source community and therefore I value sharing clear code that anyone can reproduce, and I like it best when the solution is elegant as well as efficient.

This website serves both as a place for my personal coding projects as well as a resume. My profile is also available on the following platforms: ResearchGate, LinkedIn and GitHub.

About my scientific experience

I am currently a postdoc in the Thomas Clavel Lab in Aachen, Germany. My research is focused on microorganisms colonizing and interacting within the intestinal tract using the lab anaerobic cultivation expertise. I can also rely on the NFDI4Microbiota consortium that belongs to the German National Research Data Infrastructure that support researchers investigating microbiomes towards FAIR and open data.

I obtained my PhD in 2019 at the University of Bordeaux (France). My thesis was entitled “Comparison and evaluation of bioinformatic and statistical approaches for the analysis of the pathobiome of crop plants” and was advised by Dr. Corinne Vacher and Dr. Jessica Vallance. My PhD grant and my research were funded by the INRAE (the French National Research Institute for Agriculture, Food and Environment) and Bordeaux Sciences Agro (a higher education institute and agronomic research facility).

Pathobiomes can be defined as the subset of microorganisms associated with a host plant in interaction with a pathogen. The aim of my PhD project was to determine the most relevant bioinformatic and statistical approaches to reconstruct microbial interaction networks from metabarcoding data. The biological model was grapevine (Vitis vinifera) and the fungal agent of grapevine powdery mildew, Erysiphe necator.

First, I conducted with my collaborators a bioinformatics pipeline benchmark using a mock community. During my PhD, I was able to infer microbial networks from sequenced grapevine leaves samples using various tools. These networks have to be considered as hypotheses of interactions that needs to be tested. We attempted some of these tests using co-cultures and text-mining.