Thomas Bigot

As a bioinformatics engineer, I design analysis methods to extract sequences of known or novel pathogens from metagenomic sequencing data of animal or clinical samples. My work involves evaluating existing methods based on local alignment or k-mer approaches, and determining the best combinations of analyses in collaboration with biologists who provide the samples. This work is critical in a context of pandemic or rapid diagnosis, as it allows for the referencing of more and more pathogen sequences and the identification of new pathogen/disease combinations. The main challenge lies in the detection of viral sequences, which evolve more rapidly than those of other pathogens and for which our knowledge is still limited.

I hold a PhD in bioinformatics, which focused on the detection of topological patterns in phylogenetic trees. I then contributed to the Bio++ library and worked on the implementation of fast likelihood calculations in a program for reconciling phylogenetic trees. Since joining the Bioinformatics Hub in 2016 and as a member of the PAGE expertise group, I have participated in various projects related to sequence homology analysis, including alignment, creation of homologous sequence databases, HMM profiles, and k-mer signatures. As a developer (Python/C++), I am particularly interested in optimizing laboratory prototypes and creating user-friendly tools for non-specialized users. My activity is closely related to the Pathogen Discovery Laboratory, with which I work in a privileged partnership.

Academic Degrees

PhD (2013)

Bioinformatics (Evolutionary Biology), under the supervision of Guy Perrière, LBBE, University of Lyon, France. Automated search for patterns in phylogenetic trees.
This PhD was an opportunity to investigate how to efficiently extract phylogenetic information from collections of trees of homologous gene families, using the topological information that these trees contain, in the same way as one does when analyzing a tree visually. I have created a formalism to describe these tree patterns and a software suite to execute these queries on thousands of trees in a few seconds. This software suite was published in BMC Bioinformatics: Bigot et al. 2013 (10.1186/1471-2105-14-109).
This concept of topological pattern searching can be used to describe specific patterns corresponding to a list of constraints, but also in a more generic way, to automatically extract trees containing only one gene version per species (unicopy trees) or to find the best root with respect to a reference species tree, or by using duplications contained in the gene tree.

Master (2009)

Health and Populations (speciality Bioinformatics, Biostatistics, Genome), Université Lyon 1.