Understanding the evolution of crops using deep learning and evolutionary models

使用深度学习和进化模型了解作物的进化

• 批准号: 2887535
• 金额: --
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2887535
• 关键词: Understanding; evolution; crops; using; deep

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 The Ezer lab has developed an AI for annotating new regulatory regions inArabidopsis, based on DNA sequence (unpublished preliminary results acquired through a TuringInstitute research grant).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

芸苔属植物是与模式植物拟南芥关系最密切的作物物种。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。该项目将研究芸苔属植物的调控DNA序列是如何进化的，使用的方法将允许与其他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被应用于解释DNA序列。Ezer实验室开发了一种基于DNA序列注释拟南芥新调控区域的人工智能（通过图灵研究所的研究资助获得的未发表的初步结果）。这些数据是在已有RNA-seq数据的大型数据库上训练的。博士生将完善这个人工智能模型，然后将其应用于芸苔属植物，其中大量的rna序列数据集最近已经可用。同时，学生将开发和实现各种启动子进化的数学模型。他们将把标注的调控序列与进化模型的预测结果进行比较。这将有助于我们确定哪种进化模型最能解释芸苔属植物调控序列的进化。由于大多数这些模型都是在非植物物种和没有多倍体化的情况下发展起来的，这将揭示跨生物王国调节序列进化之间的异同。这项工作还将展示如何将数学模型与人工智能结合使用，从而对作物系统的演变产生实际的理解。