TRTech-PGR: Connecting sequences to functions within and between species through computational modeling and experimental studies

TRTech-PGR：通过计算模型和实验研究将序列与物种内部和物种之间的功能连接起来

• 批准号: 2107215
• 负责人: Shin-Han Shiu
• 金额: $ 140万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107215&HistoricalAwards=false
• 关键词: TRTech; PGR; Connecting; sequences; functions

Life as we know it would be impossible without plants. They are a source of food, oxygen, timber, fiber, and medicine. Therefore, improving plant traits, such as yield, nutritional quality, and resilience, is crucial for sustainable production of plant products. Key to our ability to improve plants is a thorough understanding of how plant DNA controls traits. For example, corn DNA contains ~2 billion letters, and different sets of these letters affect different plant traits. But we have limited knowledge about which letters matter and how they control traits. When we do have a good understanding of the connection between DNA and traits, such understanding is limited to a handful of model plants chosen for their relative ease of study. Thus, to have more complete knowledge of how plants work, we will connect DNA sequences with traits they control using an Artificial Intelligence-based approach, machine learning where computers are used to uncover hidden patterns from a wide range of biological data. In addition, we will apply transfer learning to translate knowledge from one plant species to another so we can later transfer what we know about model plants to other species. The outcome of the project will be computer programs that can predict the connections between DNA sequence and traits and transfer information across species. Using these programs, scientists can better understand how plants work and this knowledge can ultimately be used to create more productive and resilient plants.The rapid growth in omics data has led to discoveries transforming plant science. However, as more genomes become available, connecting sequences to their functions globally remains challenging. Thus, our first goal is to build and validate computational models that can predict sequence functions. The second project goal is to develop and apply transfer learning to address sequence-to-function problems across species and environments. To achieve the first goal, existing multi-omics and phenotype data from four model species–Arabidopsis, maize, rice, and tomato—will be integrated with machine learning to address two sequence-to-function problems: predictions of biological process functions such as enzyme or signaling pathway membership, and physiological and morphological phenotypes. These prediction models will be dissected using model interpretation methods to provide mechanistic insights through understanding why and how the models work. To achieve our second goal, using the same data from target model species and addressing the same focal problems, transfer learning strategies will be developed and optimized to assess how knowledge can be best transferred across species and environments. There is relatively abundant experimental data available for the four models we will focus on, and by holding out different amounts and types of data, a wide range of “data-poor” scenarios can be recreated and evaluated. For both project goals, the predictions will be validated with holdout experimental data independent from data used for modeling and new data from genetic experiments conducted for this project.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.

我们所知道的生命没有植物是不可能的。它们是食物、氧气、木材、纤维和药物的来源。因此，改善植物性状，如产量、营养品质和恢复力，对于植物产品的可持续生产至关重要。我们改良植物的关键在于彻底了解植物DNA如何控制性状。例如，玉米DNA包含约20亿个字母，这些字母的不同集合会影响不同的植物性状。但我们对哪些字母重要以及它们如何控制性状的知识有限。当我们对DNA和性状之间的联系有了很好的理解时，这种理解仅限于少数几种相对容易研究的模式植物。因此，为了更完整地了解植物如何工作，我们将使用基于人工智能的方法将DNA序列与它们控制的性状联系起来，机器学习使用计算机从广泛的生物数据中发现隐藏的模式。此外，我们将应用迁移学习将知识从一个植物物种翻译到另一个物种，这样我们以后就可以将我们对模型植物的了解转移到其他物种。该项目的成果将是计算机程序，可以预测DNA序列和性状之间的联系，并在物种之间传递信息。利用这些程序，科学家可以更好地了解植物是如何工作的，这些知识最终可以用来创造更高产和更有弹性的植物。组学数据的快速增长导致了改变植物科学的发现。然而，随着越来越多的基因组变得可用，将序列与其功能联系起来仍然具有挑战性。因此，我们的第一个目标是建立和验证可以预测序列功能的计算模型。第二个项目目标是开发和应用迁移学习来解决跨物种和环境的序列到功能问题。为了实现第一个目标，现有的多组学和表型数据从四个模型物种-拟南芥，玉米，水稻和番茄-将与机器学习集成，以解决两个序列功能问题：生物过程功能的预测，如酶或信号通路成员，以及生理和形态表型。这些预测模型将使用模型解释方法进行剖析，以通过理解模型为什么以及如何工作来提供机制见解。为了实现我们的第二个目标，使用来自目标模型物种的相同数据并解决相同的焦点问题，将开发和优化迁移学习策略，以评估如何在物种和环境之间最好地转移知识。我们将重点讨论的四个模型有相对丰富的实验数据，通过提供不同数量和类型的数据，可以重新创建和评估各种“数据贫乏”的场景。对于这两个项目的目标，预测将验证与holdout实验数据独立的数据用于建模和新的数据，从遗传实验进行了这个项目。这个奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

项目成果

Plant science corpus

植物科学语料库

• DOI: 10.5281/zenodo.10022686
• 发表时间: 2023
• 期刊: Zenodo
• 作者: Shiu, Shin-Han

Selection-enriched genomic loci (SEGL) reveals genetic loci for environmental adaptation and photosynthetic productivity in Chlamydomonas reinhardtii

• DOI: 10.1016/j.algal.2022.102709
• 发表时间: 2022-04-21
• 期刊: ALGAL RESEARCH-BIOMASS BIOFUELS AND BIOPRODUCTS
• 影响因子: 5.1
• 作者: Lucker，Ben F.;Temple，Joshua A.;Kramer，David M.
• 通讯作者: Kramer，David M.

Plant Science Knowledge Graph Corpus: a gold standard entity and relation corpus for the molecular plant sciences

植物科学知识图谱语料库：分子植物科学的黄金标准实体和关系语料库

• DOI: 10.1093/insilicoplants/diad021
• 发表时间: 2023
• 期刊: in silico Plants
• 影响因子: 3.1
• 作者: Lotreck, Serena;Segura Abá, Kenia;Lehti-Shiu, Melissa D.;Seeger, Abigail;Brown, Brianna N. I.;Ranaweera, Thilanka;Schumacher, Ally;Ghassemi, Mohammad;Shiu, Shin-Han;Marshall-Colon, ed., Amy
• 通讯作者: Marshall-Colon, ed., Amy

Challenges and opportunities to build quantitative self-confidence in biologists

• DOI: 10.1093/biosci/biad015
• 发表时间: 2023-04-29
• 期刊: BIOSCIENCE
• 影响因子: 10.1
• 作者: Cuddington，Kim;Abbott，Karen C.;White，Easton R.
• 通讯作者: White，Easton R.

Supervised capacity preserving mapping: a clustering guided visualization method for scRNA-seq data.

保存映射的监督能力：用于SCRNA-SEQ数据的聚类指导性可视化方法。

• DOI: 10.1093/bioinformatics/btac131
• 发表时间: 2022-04-28
• 期刊: Bioinformatics (Oxford, England)