• Visual analytics for plant pangenomes [2020] [Website (project partners only)]
    PhD student: Astrid van den Brandt (TU Eindhoven)
  • Fusarium pangenomics [2019]
    PhD student: Meixin Yang
  • Pangenomic applications for plants and pathogens [2018]
    PhD student: Eef Jonkheer
    In collaboration with: Biointeractions and Plant Health (Wageningen Research) and Genetwister Technologies (Wageningen)
    Summary: This project aims to develop pangenomic applications for plants and pathogens, to investigate the applicability of pangenomes: pangenome-based discoveries and improved efficiency and/or accuracy. The project will revolve around pangenomic technologies, as implemented in PanTools, applied to biological cases that are relevant in both fundamental and applied research. The research will be precompetitive in nature, extending the pangenomic infrastructure as currently being developed in the Bioinformatics Group at Wageningen University. We specifically focus on the following topics: gene classification, gene-family evolution, variant exploration (sequence and structural variation).
  • VLPB: Exploring pangenomics for crops [2017] [Website (members only)]
    Research assistant: Eef Jonkheer
  • EPS: Pangenomics for crops [2015] [Website]
    PhD student: Siavash Sheikhizadeh
    Summary: In recent years, several projects have been initiated to map the genetic landscape of various plants and food crops, by sequencing the genomes of large numbers of varieties. While measurements are generated ever more easily, the integration and analysis of the avalanche of data generated by these projects has now become a major bottleneck. Current bioinformatics tools, assuming the existence of a single reference genome, no longer suffice to properly store, process and analyze such pangenomic datasets. To accurately represent and analyze genetic variation within a species and between species, we need more sophisticated data structures.

    In this project, we will develop computational methods to compress multiple genome sequences into a novel pangenome representation, and exploit this for intuitive comparative visualization and highly efficient analysis of large numbers of related genomes. To this end, we will construct new algorithms to support key analyses and (visually) explore the pangenome at different levels of aggregation. The pangenome representation and the accompanying tools will transform the way in which genome content, organization and evolution are studied in the plant sciences.