Elizabeth Allman
University of Alaska Fairbanks, AK·Mathematics & Statistics
Working out how species are related, and drawing that history as a family tree, is a central task in biology. Such trees have uses ranging from evolution research to tracking viral strains like the one that causes COVID.
- Fields
Mathematics & Statistics
Ecology & Evolution
Computer Science
- Topics
phylogenetics
phylogenetic networks
semi-directed networks
- Grants
- 1 known grant, through Aug 2029
What they work on
- Prove parameter identifiability theorems under the Network Multispecies Coalescent Model, showing which features of complex semi-directed networks can be recovered from summary distributions of genomic data.
- Translate identifiability results into fast, scalable, provably consistent distance-based algorithms that estimate network and tree-of-blobs topologies from DNA sequences or gene trees.
- Develop consensus methods, statistical support measures for features of networks and trees of blobs, and dissimilarity measures for comparing pairs of semi-directed phylogenetic networks.
- Implement the new algorithms in freely available, user-friendly software and validate all methods on simulated and empirical genome-scale datasets.
More about the research
But species sometimes hybridize, combining genetic material from different parent species in new ways. Butterflies do this when wing patterns pass between species. Histories that include hybridization are better drawn as networks than as trees. Whole genomes and thousands of gene trees are now available, so researchers need methods that are both fast and statistically sound for recovering these networks from such data.
Why it matters
Better tools for detecting hybridization could help evolutionary biologists reconstruct histories in which species exchanged genetic material. They could also aid other fields that trace branching-and-merging relationships, including biomedical research and stemmatology, the study of how copies of historical texts descend from one another. Free software would make these methods available to anyone working with genome-scale data.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Andrew D Kern
University of Oregon, OR·Ecology & Evolution
Develop methods to infer dispersal, how far individuals move, directly from population genomic data, and assess how spatial structure affects GWAS, genome-wide studies linking variants to traits, with Anopheles gambiae data in mind.
Nathaniel Hardy
Auburn University, AL·Ecology & Evolution
Estimate evolutionary relationships among aphid lineages by sequencing target-enriched genomic data from about 700 aphid species, advancing aphid classification.
Paul Marjoram
University of Southern California, CA·Mathematics & Statistics
Develop a probabilistic evolutionary model using phylogenetic trees and experimental Gene Ontology annotations to automatically predict functions of uncharacterized genes.
The school
University of Alaska Fairbanks: 7 labs on PI Pages, $246M a year on research, 31 research doctorates a year.
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