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

基因和基因组的进化是地球上巨大的生物多样性的原因。然而，尽管它作为生命最基本的属性发挥着核心作用，但进化的过程仍然鲜为人知，目前的模型通常无法跨越进化可以发挥作用的不同尺度。这项工作通过开发新的模型和算法来解决这一根本缺陷，这些模型和算法同时解释了真核生物基因组进化中最普遍的过程：基因复制、丢失和融合。新的计算框架和方法将使研究人员能够系统地解释数据集，做出更可靠和稳健的推断，并提高我们对基因组进化的理解。由于这些历史学构成了许多基因组研究的基础，这些结果将反过来惠及生物学的许多领域。此外，PI致力于培养下一代科学家。作为该项目的一部分，PI将为大量的本科生提供引人注目的研究经验，开发数据科学的本科课程，并继续参与几个旨在扩大STEM参与的大学范围和外部倡议。这项研究开发了系统发育协调领域的模型和算法，将基因树与其物种树进行比较，以推断将它们联系在一起的进化事件。对于前核生物，最流行的协调方法允许基因复制和基因丢失，这只适用于在较大进化距离采样的物种，或者允许合并，这只适用于在较近进化距离采样的物种。也就是说，每个模型只提供了进化的部分视图，限制了它们的适用性和准确性。通过将这两个模型联系起来，这项研究将影响如何表示基因家族进化以及如何推断和分析协调。具体地说，这项工作解决了该领域的三个关键问题：大规模数据集的算法挑战，区分生物信号和噪声的统计学挑战，以及跨基因组推广的建模挑战。预期的贡献包括协调问题的新算法和启发式方法，解决多分叉树的方法，以及可以解释每个物种的多个样本和物种杂交的模型。此外，这里开发的联合进化模型和推理算法可能会推动该领域进一步统一的方法。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.