Computational Methods
Evolutionary and ecological interactions and feedbacks are complex and studying adaptive evolution in response to ecological conditions is challenging. Genomic signals of natural selection can be confounded by demographic changes and other selection pressures, and the methods on the forefront of theoretical development are typically developed for model organisms and can be inappropriate for use with non-model species. For this reason, my work includes the development and careful application of computational and statistical methods that combine theory, big data, and machine learning to study non- and new model species.
Detecting selection with machine learning
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Simulations for understanding non-model species
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My machine learning method, Flex-sweep (Lauterbur et al. 2023, MBE) to identify diverse selective sweeps genome-wide that improves on previous methods in its versatility and ability to identify older sweeps.
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Recently, I led a push to expand the species catalog for the stdpopsim framework for standardizing population genomic simulations for methods development and analysis (Lauterbur et al. 2023, ELife). Originally stdpopsim included six model species; now it includes an additional 15 species, most non- or new models.
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