There is a critical need to improve the robustness and accessibility of computational approaches in population genomics. The PI's long-term goals are to develop methods for inferring evolutionary history from population genomic data and to support the scienti?c community in their use. The ob- jectives of this supplement application are to bring his widely used software dadi to the cloud and to improve its development and documentation. The rationale for the proposed development is that dadi is computationally intensive and often used by empirical research groups with modest com- putational resources, so bringing dadi to the cloud will dramatically increase these researchers' access to sophisticated population genomic modeling.
In Aim 1, the PI proposes to collaborate with cloud computing experts to bring dadi to several cloud environments, including those served by NIH STRIDES.
In Aim 2, the PI proposes to improve the development environment for dadi, through more com- plete and automated testing.
In Aim 3, the PI proposes to improve the user environment for dadi, through improved documen- tation and interoperability with standard ?le formats. The expected outcome of the proposed supplement is a dramatically improved software tool for inferring models of population history and natural selection from population genomic data. This outcome is expected to positive impact on the ?eld of population genomics, by increasing the accessibility and robustness of the widely-used dadi software. Project Summary/Abstract
The proposed research is relevant to public health because understanding evolutionary history and natural se- lection plays a key role in combating genetic and infectious diseases. The proposed research will make methods for learning evolutionary history from genetic data available to a wider range of users. These methods will form a foundation for predicting, for example, rates of adaptation of pathogens and the architecture of human genetic disease. Project Narrative
