Collaborative Research: RESEARCH-PGR: Predicting Phenotype from Molecular Profiles with Deep Learning: Topological Data Analysis to Address a Grand Challenge in the Plant Sciences

合作研究：RESEARCH-PGR：利用深度学习从分子概况预测表型：拓扑数据分析应对植物科学的重大挑战

• 批准号: 2310357
• 负责人: Arjun Krishnan
• 金额: $ 33.39万
• 依托单位: University of Colorado at Denver
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310357&HistoricalAwards=false
• 关键词: Collaborative; Research; RESEARCH; PGR; Predicting

Organisms are a consequence of information embedded in their genome expressed through molecular processes. Sequencing technologies allow biologists to extract nearly all information content from the genome. However, measuring what an organism is has not advanced as far as genomic sequencing: unlike the genome, it is not yet possible to measure the totality of information embedded in the organismal form. If all the information that is contained within organisms could be extracted, a model could be developed that would address one of the Grand Challenges in biology, the ability to predict what an organism is from its genomic information. In this project, mathematical approaches that have not been fully explored in biology will be used to extract information in data by measuring its structure. This field of mathematics has a motto: that all shape is data, and all data have shape. By measuring the shapes and gene expression patterns of leaves, the project will treat them as data from which embedded information can be extracted. Deep learning methods will then be used to predict the shapes of leaves from their gene expression profiles. As part of the connection between the project and its impact to society, students from both the U.S. and México will help analyze the data through Plants&Python, a bilingual, freely available curriculum initiated as a means to bring together plant biologists who have never coded and data scientists new to plant science, with groups that comprise U.S. agriculture. Using X-ray Computed Tomography (CT) to measure plant morphology and transcriptome profiling (RNA-seq) to measure gene expression, the project will use the Euler Characteristic Transform (ECT) and the Mapper algorithm, two Topological Data Analysis (TDA) techniques, to extract the total information embedded in the leaf morphology of Arabidopsis accessions with contrasting developmental reproducibility. The ECT is mathematically proven to distinguish any object from any other, and the Mapper algorithm is used to visualize underlying data structures as a graph. Specific aims include: 1) using the ECT to measure the total information embedded in leaf shape and benchmarking against traditional methods to see how much “hidden” phenotypic information is revealed when measured comprehensively; 2) generating RNA-Seq gene expression profiles from identical leaves, visualizing the underlying data structure as a Mapper graph; the same will be done for phenotypic data as measured by the ECT; and, 3) predicting the precise leaf shape features associated with gene expression signatures using deep learning. By converting underlying molecular and phenotypic data structures into node embeddings, an encoder-decoder neural network will align molecular and phenotypic Mapper graphs. The result will be a mapping of gene expression profiles to features of leaf shape as predicted using deep learning methods on underlying data structures. All project outcomes will be made publicly available through long term data repositories.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.

生物体是通过分子过程表达在其基因组中的信息的结果，测序技术使生物学家能够从基因组中提取几乎所有信息内容。然而，测量生物体的性质还没有像基因组测序那样先进：与基因组不同，它是一种生物体。目前还不可能测量生物体内包含的全部信息。如果可以提取生物体中包含的所有信息，则可以开发一个模型来解决生物学中的重大挑战之一，即预测信息的能力。生物体来自其基因组信息。在这个项目中，生物学中尚未充分探索的数学方法将用于通过测量数据的结构来提取数据中的信息。这个数学领域有一个座右铭：所有形状都是数据，并且所有数据都通过测量形状而具有形状。和叶子的基因表达模式，该项目将把它们视为可以提取嵌入信息的数据，然后使用深度学习方法根据其基因表达谱预测叶子的形状，作为该项目与叶子之间联系的一部分。它对社会的影响，来自美国和美国的学生墨西哥将通过 Plants&Python 帮助分析数据，这是一门免费的双语课程，旨在将从未编码过的植物生物学家和刚接触植物科学的数据科学家与美国农业团体（使用 X 射线计算机断层扫描）聚集在一起。 CT）测量植物形态，转录组分析（RNA-seq）测量基因表达，该项目将使用欧拉特征变换（ECT）和Mapper算法这两种拓扑数据分析（TDA）技术，提取拟南芥种质叶子形态中嵌入的全部信息，并具有对比的发育再现性。ECT 在数学上被证明可以将任何对象与其他对象区分开来，并且 Mapper 算法用于将基础数据结构可视化为图形。具体目标包括： 1) 使用 ECT 测量叶片形状中嵌入的总信息，并与传统方法进行基准比较，以了解综合测量时揭示了多少“隐藏”的表型信息；2) 从相同的叶片生成 RNA-Seq 基因表达谱，将底层数据结构可视化为映射器图；对于 ECT 测量的表型数据也将进行同样的操作；3) 通过转换底层分子和表型数据来预测与基因表达特征相关的精确叶子形状特征。将结构嵌入到节点嵌入中，编码器-解码器神经网络将对齐分子和表型映射器图，结果将是使用基础数据的深度学习方法预测的基因表达谱到叶子形状特征的映射。所有项目成果将通过长期数据存储库公开。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。