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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.
A wrapper program to parallelize and automate runs of "Structure", "fastStructure" and "MavericK".
Furthermore, Structure_threader further extends on the parallelization by also automating the estimation of "K", and the MeanQ plotting. These are outputted in 2 formats, a typical vectorial format, of "publication quality" plots, and a new, interactive plot, which is particularly suited for data exploration.
Next-generation sequencing datasets are becoming more frequent, and their use in population studies is becoming widespread. For non-model species, without a reference genome, it is possible from a panel of individuals to identify a set of SNPs that can be used for further population genotyping. However the lack of a reference genome to which the sequenced data could be compared makes the finding of SNPs more troublesome.
Sequence databases, such as NCBI, are a very important resource in many areas of science. Downloading small amounts of sequences to local storage can easily be performed using any recent web browser, but downloading tens of thousands of sequences is not as simple.
