Understanding the evolution of crops using deep learning and evolutionary models

油菜是与模式植物拟南芥亲缘关系最密切的作物。有趣的是，由于多倍化(全基因组复制)，它们通常具有多个拟南芥基因副本。这个项目将研究甘蓝属调控DNA序列是如何进化的，使用的方法将允许与另一种进化模型进行定量比较。用于注释文本和图像的人工智能算法最近已被应用于解释DNA序列。Ezer实验室已经开发了一种人工智能，用于基于DNA序列(通过图灵研究所的研究拨款获得的未发表的初步结果)注释拟南芥中的新调节区。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。这位博士生将改进这个人工智能模型，然后将其应用于甘蓝，最近已经提供了大量的RNA-seq数据集。同时，学生将开发和实施启动子进化的各种革命性数学模型。他们将把注释后的调控序列与进化模型所做的预测进行比较。这将有助于我们确定哪种进化模型最能解释甘蓝调控序列的进化。由于大多数模式都是在非植物物种中发展出来的，而且没有多倍化，这将为揭示生物界之间调控序列进化的异同提供重要的线索。这项工作还将展示如何将数学模型与人工智能结合使用，以达到对作物系统进化的实际理解。

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 The Ezer lab has developed an AI for annotating new regulatory regions inArabidopsis, based on DNA sequence (unpublished preliminary results acquired through a TuringInstitute research grant).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.