CAREER: Algorithms for Gene Family Evolution with Gene Duplication, Loss, and Coalescence

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Evolution of genes and genomes is responsible for the immense biological diversity on our planet.However, despite its central role as the most fundamental property of life, the process of evolutionremains poorly understood, and current models have typically been unable to span the diversity ofscales at which evolution can act. This work addresses this fundamental shortcoming by developing newmodels and algorithms that simultaneously account for the most prevalent processes in eukaryotic genefamily evolution: gene duplication, loss, and coalescence. The new computational framework andmethods will enable researchers to systematically interpret data sets, make substantially more reliableand robust inferences, and improve our understanding of genome evolution. Because these historiesform the basis of many genomic studies, these results will, in turn, benefit many areas of biology.Additionally, the PI is committed to educating the next generation of scientists. As part of this project,the PI will provide compelling research experiences for a substantial number of undergraduates, developundergraduate courses in Data Science, and continue engaging in several college-wide and externalinitiatives aimed at broadening participation in STEM.This research develops models and algorithms in the field of phylogenetic reconciliation, whichcompares a gene tree with its species tree to infer the evolutionary events that link them. Foreukaryotic organisms, the most popular reconciliation methods allow for gene duplications and genelosses, which is appropriate only for species sampled at large evolutionary distances, or allow forcoalescences, which is appropriate only for species sampled at close evolutionary distances. That is,each model provides only a partial view of evolution, limiting their applicability and accuracy. Bybridging these two models, this research will impact how gene family evolution is represented and howreconciliations are inferred and analyzed. Specifically, this work addresses three key problems in thefield: algorithmic challenges of scaling to large datasets, statistical challenges of distinguishing biologicalsignal from noise, and modeling challenges of generalizing across genomes. Expected contributionsinclude novel algorithms and heuristics for the reconciliation problem, methods for resolvingmultifurcating trees, and models that can account for multiple samples per species and for specieshybridization. In addition, the joint evolutionary models and inference algorithms developed here maymotivate further unified approaches in the field.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.