CAGEE: Computational analysis of gene expression evolution

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Biological advances have revealed that many traits are controlled by the amount of a gene produced in each cell, not just the sequence of each gene and protein. Genes can differ in these expression levels between different tissues within the same organism or between species in the same tissue. Understanding how these levels change—and how they control the appearance and function of organisms—is a key challenge for current research. This project will develop new methods for understanding gene expression changes between tissues and species, as well as how these changes are coordinated. The research applies new statistical approaches to produce open-source software for carrying out the analyses. The software will allow all scientists to be able to use these new tools on multiple platforms, contributing to the national cyberinfrastructure. This work also will improve and accelerate research with multiple societal benefits, including enabling new biological discoveries. This project will support the training of a postdoctoral researcher, graduate students, and undergraduates from groups that are underrepresented in science and technology careers. A classroom lesson plan and computer lab on understanding evolutionary trees will be developed and distributed to partner institutions.Changes in gene expression have been shown to be responsible for many differences between species, and recent technological advances mean that such data are easy to collect, even in non-model organisms. By measuring expression levels of the same gene in multiple species, we can begin to understand the history of changes in gene expression across organisms. By measuring expression in thousands of genes, we can further understand the mechanisms and modes by which gene expression evolves. This research develops a new software package (CAGEE) for studying gene expression evolution across a phylogenetic tree. The statistical approach developed here also makes it possible to study multiple tissues or sexes in a single framework, allowing us to make statistically rigorous inferences about differences in the rates of evolution among samples. Integrating these tools into a single software package available on multiple platforms enables these methods to be applied by a wide group of users.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.