Dr. Ilias Lagkouvardos

Research Group Leader
Technische Universität München

Dr. Ilias Lagkouvardos is a microbial ecologist and data integration enthusiast who utilizes bioinformatic methods and tools to address questions related to microbial diversity and ecology in various environments. The primary focus of his research group is the elucidation of the role of distinct microbial compositions in human health and disease by means of sequencing of the versatile 16S rRNA gene. In addition, we explore microbial genomics and metagenomics sequences for insights into the functionality and dynamics of complex microbial communities.Through integration of thousands of microbial profiles, we investigate the sequence supported global diversity and the occurrence of still undescribed microbial species in different environments. Additional insights are gained over the intrinsic forces guiding the assembly of those communities that will help us effectively and in a personalized manner modulate human microbiome towards a healthier composition.For better understanding of the developing stages of our microbiome and the ways we can control it for better health, we apply machine learning algorithms that will allow us to predict the future course of a microbial community by looking at different timepoints, starting from birth towards adulthood. One basic line of research in the Lagkouvardos group is to utilize state-of-the-art methods and tools to build specialized analytical pipelines and thereby develop user-friendly bioinformatic solutions for public use. One example of such novel bioinformatic tool is the IMNGS platform (www.imngs.org), a web database that automatically retrieves all publicly available 16S rRNA gene amplicon sequences and processes them in a uniform and organized way. The group also released Rhea, a set of R scripts for the analysis of microbial profiles (lagkouvardos.github.io/Rhea/). Courses disseminating expert knowledge on sequence analysis and demonstrating the usability of these tools are organized regularly.