CoBiG2 aim is to implement population genomics approaches to study the evolutionary and ecological diversification of species in natural environments and to study the genomic process of adaptation of organism and populations to their environments. Additionally we also aim to carry out associations studies between traits and genotypes and how they can very under different environmental conditions. Both aims are particular relevant for forecasting the impact of environmental changes on natural populations as well as for implementation of marker-assisted selection. Understanding the genetics and genomics of the environmental change and its consequences for biodiversity and its preservation is the overall goal of this research group.
In close interaction with the above work and goals, third-party and in-house developed software, bioinformatics pipelines and data mining computational techniques are used to analyze the generated population-genetic, phylogenetic, and phylogeographic datasets. A more recent challenge in this direction is the analysis of the large datasets that we are about to generate using next-generation sequencing technology under a population genomics and gene discovery framework, to address the scientific questions above.
Silva, D.N., Várzea, V., Paulo, O.S. & Batista, D. (2018) Population genomic footprints of host adaptation, introgression and recombination in coffee leaf rust.Molecular Plant Pathology, 19(7), 1742-1753. DOI:https://doi.org/10.1111/mpp.12657 (IF2016 4,697; Q1 Plant Sciences)
Monteiro, F., Frese, L., Castro, S., Duarte, M.C., Paulo, O.S., Loureiro, J. & Romeiras, M.M. (2018) Genetic and genomic tools to asssist sugar beet improvement: the value of the crop wild relatives.Frontiers in Plant Science, 9(74), 1-8. DOI:10.3389/fpls.2018.00074 (IF2016 4,298; Q1 Plant Sciences)
Seabra, S.G., Fragata, I., Antunes, M.A., Faria, G.S., Santos, M.A., Sousa, V.C., Simões, P. & Matos, M. (2018) Different genomic changes underlie adaptive evolution in populations of contrasting history.Molecular Biology and Evolution, 35(3), 549–563. DOI:10.1093/molbev/msx247 (IF2017 10,217; Q1 Evolutionary Biology)