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NSF
An award is made to Rice University to enable the development of PhyNetPy—a comprehensive, open-source Python library, available to developers and biologists working on or using phylogenetic networks. The library will offer user-friendly inference and analysis methods, fundamental data structures, and essential components for developers to rapidly implement their ideas and contribute to the expansion of the phylogenetic network toolkit. This project will foster research in software engineering, mathematical modeling, and algorithm design while providing valuable training opportunities for graduate students and postdoctoral researchers at the intersection of computing and biology. Research findings will be incorporated into courses taught by the principal investigator at Rice University and shared through publications in peer-reviewed journals and conference proceedings. Additionally, the software library will be made accessible via a dedicated website that includes user and developer manuals, tutorials, demonstration videos, and a discussion forum. Researchers from the biology and computing fields have long been creating mathematical models, algorithmic solutions, and software tools to reconstruct the evolutionary histories of genes, genomes, and species from genomic data. Notably, extensive libraries of data structures and algorithms have been developed to enable broader community contributions in creating tools for this purpose. A salient feature of almost all of these efforts is the mathematical modeling of evolutionary history as a tree. While tree models are adequate for representing certain evolutionary histories, more complex processes such as hybridization and horizontal gene transfer are better modeled using phylogenetic networks. However, the development of software tools for inferring and analyzing phylogenetic networks has not kept pace with the plethora of phylogenetic tree software. Specifically, there is a notable absence of a general-purpose software library that enables developers to quickly prototype new algorithms and methods, hindering progress in this area. This project aims to rectify this gap by developing PhyNetPy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Up to $1.9M
2029-08-31
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