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 The Ezer lab has developed an AI for annotating new regulatory regions inArabidopsis, based on DNA sequence (unpublished preliminary results acquired through a TuringInstitute research grant).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

甘蓝是与研究良好的plantarabidopsis最密切相关的农作物物种。有趣的是，由于多倍体化（整个基因组重复），它们通常具有多个拟南芥基因的副本。该项目将使用允许与替代进化模型进行定量比较的方法进行调节性DNA序列如何发展。人工智能算法用于注释文本和图像，最近应用了图像来解释DNA序列。 Ezer Lab开发了一种基于DNA序列（通过TuringInstitute Research Grant获得的未发表的初步结果），开发了一个用于注释新的监管区域inarabidopsis的AI。该数据在已有RNA-Seq数据的大型数据库上进行了培训。博士学位学生将完善此AI模型，然后将其应用于胸前，并在最近提供了广泛的RNA-Seqdatasets。同时，学生将开发和实施启动子进化的各种进化数学模型。他们将将带注释的调节序列与进化模型的预测进行比较。这将有助于我们确定哪种进化模型可以最能解释铜管中调节序列的演变。由于大多数该模型是在非植物物种中开发的，并且在没有多倍化化的情况下，这将使跨生物学界的调节序列进化之间的相似性和差异很重要。这项工作还将展示如何将数学模型与AI结合起来，以对作物系统的演变进行实际理解。