Understanding the evolution of crops using deep learning and evolutionary models

拟南芥是与模式植物拟南芥关系最密切的作物。有趣的是，由于多倍体化（全基因组复制），它们通常具有拟南芥基因的多个拷贝。这个项目将研究如何调控的DNA序列的进化，使用的方法，将允许定量比较与alternativeevolutionary models.Artificial intelligence algorithms，用于注释文本和图像最近已被应用于解释DNA序列。Ezer实验室开发了一种AI，用于注释拟南芥中新的调控区域，基于DNA序列（通过图灵研究所研究资助获得的未发表的初步结果）。这些数据是在预先存在的RNA-seq数据的大型数据库上训练的。博士生将改进这个AI模型，然后将其应用于最近已经提供了大量RNA-seq数据集的Arabica。与此同时，学生将开发和实现启动子进化的各种进化数学模型。他们将比较注释的调节序列与进化模型的预测。这将帮助我们确定哪种进化模型最能解释拟南芥调控序列的进化。由于大多数这些模型是在非植物物种中开发的，并且没有多倍体，这将为跨生物界的调控序列进化之间的相似性和差异性提供重要的启示。这项工作还将展示数学模型如何与人工智能结合使用，以实际了解作物系统的进化。

Brassicas are the crop species which are most closely related to the well-studied model plantArabidopsis. Interestingly, they often have multiple copies of Arabidopsis genes, due to polyploidisation(whole genome duplication). This project will investigate how the regulatory DNA sequences ofBrassicas have evolved, using methods that will allow quantitative comparison with alternativeevolutionary models.Artificial intelligence algorithms that are used to annotate texts and images have recently been appliedto interpret DNA sequences. The Ezer lab has developed an AI for annotating new regulatory regions inArabidopsis, based on DNA sequence (unpublished preliminary results acquired through a TuringInstitute research grant). This data is trained on large databases of pre-existing RNA-seq data.The PhD student will refine this AI model and then apply it to Brassicas, where extensive RNA-seqdatasets have recently been made available. In parallel, the student will develop and implement variousevolutionary mathematical models of promoter evolution. They will compare the annotated regulatorysequences with the predictions made by the evolutionary models. This will help us identify whichevolutionary model best explains the evolution of regulatory sequences in Brassicas. As most of thesemodels have been developed in non-plant species and in the absence of polyploidisation, this will shedan important light on the similarities and differences between regulatory sequence evolution acrossbiological kingdoms. The work will also show how mathematical models can be used in conjunctionwith AI to arrive at a practical understanding of the evolution of crop systems.