I am a research scientist at Wageningen University & Research. My research interests include exploring -omics data to understand patterns and mechanisms of plant biotic interactions and co-evolution. Together with the WUR Bioinformatics group (Dr. Marnix Medema), Plant-Microbe interaction group (Prof. Saskia Van Wees/Prof. Corné Pieterse) at Utrecht university and Microbial ecology group at NIOO-KNAW (Prof. Jos Raaijmakers), I explore and implement innovative -omics integration strategies to map genes and their expression patterns to metabolites that play key roles in host-microbe interactions.
PhD in Bioinformatics, 2014
University of Camerino Italy
MSc. in Bioinformatics, 2008
Nottingham Trent University UK
BSc in Biotechnology, 2006
University of Pune India
In my current postdoctoral research at Wageningen University & Research and at Utrecht University, I investigate interactions between endophytes and their host-plants by integrating genomics, transcriptomics and metabolomics data. Please check the NWO Groot in the project section and a summary of my project in the form of a poster.
My overall research interest aims to integrate and explore -omics datasets (genomics, transcriptomics, epigenomics, proteomics and metabolomics) to understand patterns and mechanisms of biotic interactions and co-evolution.
My postdoctoral work at the University of Exeter and Rothamsted Research were mainly driven by key questions in resistance evolution of insect-pests to natural and synthetic insecticides. I used genome-wide investigations and computational approaches to answer such questions, ensuring these are relevant to a broad scientific community.
With the emergence of large amounts of omics data, computational approaches for the identification of plant natural product biosynthetic pathways and their genetic regulation have become increasingly important. While genomes provide clues regarding functional associations between genes based on gene clustering, metabolome mining provides a foundational technology to chart natural product structural diversity in plants, and transcriptomics has been successfully used to identify new members of their biosynthetic pathways based on coexpression. Thus far, most approaches utilizing transcriptomics and metabolomics have been targeted towards specific pathways and use one type of omics data at a time. Recent technological advances now provide new opportunities for integration of multiple omics types and untargeted pathway discovery. Here, we review advances in plant biosynthetic pathway discovery using genomics, transcriptomics, and metabolomics, as well as recent efforts towards omics integration. We highlight how transcriptomics and metabolomics provide complementary information to link genes to metabolites, by associating temporal and spatial gene expression levels with metabolite abundance levels across samples, and by matching mass-spectral features to enzyme families. Furthermore, we suggest that elucidation of gene regulatory networks using time-series data may prove useful for efforts to unwire the complexities of biosynthetic pathway components based on regulatory interactions and events.