Catherine Breton

Catherine Breton focuses on population genomics of natural and cultivated species. She earned her Ph.D. in population genomics and evolution at Université Paul‑Cézanne Marseille in 2006, where her dissertation identified the refugial zone and the double‑domestication zone of the olive tree. After a post‑doctoral fellowship at the University of Georgia (Athens, USA) in 2011 studying the evolution of the parasitic plant Orobanche, she decided to merge molecular and statistical approaches for a comprehensive analytical workflow. She later completed a master’s in bioinformatics at the University of Montpellier in 2014.

Her research also includes the genomics of starvation tolerance in the Mediterranean Sea bass (Dicentrarchus labrax) and the hybrid zones of the house‑mouse complex (Mus musculus). She emphasizes that genomics helps characterize genetic diversity across wild and domesticated species and reveals how they have evolved through major Earth‑history changes.

Early in her post‑doc, Catherine resolved to control every step of high‑throughput data analysis. She taught herself scripting, which initially lengthened her computer time but ultimately made data analysis more rewarding despite occasional library‑compilation frustrations. Today she brings over fifteen years of experience to the analysis and integration of next‑generation sequencing (NGS) data, including long‑read technologies.

In 2018 she joined Bioversity International to bridge genomics, resource‑genetics researchers, and computational scientists. Her primary research interest lies in leveraging NGS data to enhance in‑situ and ex‑situ conservation and utilization of crop genetic resources. Her overarching goal is to apply cutting‑edge genomic tools to deepen our understanding of plant diversity.

Since 2018, Catherine has been dedicated to characterizing banana germplasm. She analyzes data for the Musa Germplasm International Transit Centre (ITC), the Drought‑Tolerance (DGD) program, and natural banana populations in Papua New Guinea, Vietnam, China, and Laos (the B4BB program with CIRAD). She also contributes to the Banana Genome Hub (BGH), Gigwa, and various projects such as GWAS and population‑genomics initiatives.

From 2020 onward, she pursued independent training in machine learning, deep learning, and artificial intelligence, integrating these methods into genomics pipelines for high‑throughput analysis. Beginning in January 2024, she has been building ML/DL pipelines to classify banana accessions at ITC Leuven (MsaDeepMosaic). Using whole‑genome sequencing data together with the mosaic‑reconstruction tool VcfHunter, she is designing a model that classifies mosaic structures via abstract image‑recognition techniques.