Schrider Lab
Principal Investigator: Daniel R Schrider
Institution: University of North Carolina at Chapel Hill (Chapel Hill, NC)
Primary Subfield: Computational Biology
Recruitment Status: Inquire for rotation/fellowship opportunities
Research Focus
Project Summary/Abstract Background: A major challenge in evolutionary genomics is to characterize the forces shaping patterns of genetic variation. For instance, disentangling the effects of natural selection on genetic diversity from those of demographic changes is notoriously challenging. This question has important implications for our understanding of key evolutionary processes: how do species successfully adapt to new selective pressures, and can we determine which genes were responsible for these adaptations? Researchers typically address these and similar problems using statistical summaries of genome sequence variation that provide insights into the evolutionary forces at play. However, because such approaches typically rely on a univariate summary of the data, valuable information present in the original dataset is lost. A more fruitful strategy would be to use multidimensional representations of genomic data or even the totality of the input (e.g. a matrix representation of a sequence alignment). Modern deep learning methods represent an enticing route toward using high- dimensional representations of sequence data to make accurate evolutionary inferences. As the size of…
Active NIH Awards
- 5R35GM138286-07: Advancing evolutionary genetics through deep learning (2026)
- 2R35GM138286-06: Advancing evolutionary genetics through deep learning (2025)
- 5R35GM138286-05: Advancing evolutionary genetic inference in humans and other taxa (2024)
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