NSF Postdoctoral Fellowship in Biology: Machine Learning and High-throughput Reverse Genetics to Identify and Functionally Characterize Peptides Encoded in Short ORFs of lncRNAs

NSF 生物学博士后奖学金：机器学习和高通量反向遗传学，用于识别和功能表征 lncRNA 短 ORF 中编码的肽

• 批准号: 2305644
• 负责人: Alyssa Kearly
• 金额: $ 24.9万
• 依托单位: Kearly, Alyssa
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2305644&HistoricalAwards=false
• 关键词: NSF; Postdoctoral; Fellowship; Biology; Machine

This action funds an NSF Plant Genome Postdoctoral Research Fellowship in Biology for FY 2023. The fellowship supports a research and training plan in a host laboratory for the Fellow who also presents a plan to broaden participation in biology. The title of the research and training plan for this fellowship to Alyssa Kearly is “Machine learning and high-throughput reverse genetic approaches for the identification and functional characterization of peptides encoded in the short ORFs of lncRNAs.” The host institution for the fellowship is the Boyce Thompson Institute and the sponsoring scientists are Drs. Andrew Nelson and Aleksandra Skirycz.Plants produce a variety of small peptides that serve as communication signals and regulators of critical processes, including plant growth, development, and responses to environmental change. As naturally derived compounds, they represent attractive candidates for the development of safer pesticides, food preservatives, and medicines. Therefore, efforts to comprehensively identify and functionally characterize small peptides could have tremendous impacts not only on the general understanding of plant biology, but also on agriculture and medicine. The improved resources and computational tools for small peptide prediction to be developed through this project will be invaluable to a more complete exploration of these biologically significant peptides. Additionally, the proposed endeavors to generate functional predictions will provide insight into their roles and potential for cross-application. This work will provide training in machine learning, transcriptomics, proteomics, and comparative functional genomics. Throughout the project, the Fellow will engage in undergraduate mentorship opportunities that broaden access to scientific research and workshops that will increase her effectiveness as an educator.Small peptides play critical roles as regulatory and signaling molecules in plant development and stress responses. Although long non-coding RNAs (lncRNAs) are defined by their low protein-coding potential, a subset containing small open reading frames (sORFs) have recently been shown to undergo active translation, introducing lncRNAs as an unexplored source of small peptides. The identification of functional sORFs and detection of their encoded peptides (SEPs) has historically been hindered by biased prediction methods and limitations of experimental techniques. This has led to the persistent misclassification of an unknown number of sORF-containing lncRNAs and the overlooking of the potentially bioactive SEPs encoded therein. This project aims to develop improved computational tools for the prediction of functional sORFs and to make strides in functionally characterizing their peptide products. Through the reanalysis of published ribosomal profiling datasets, the Fellow will build a database of translated lncRNA sORFs across model and crop plant species, to be made accessible to the broader scientific community via TAIR (arabidopsis.org). This database will be used to develop a functional sORF prediction algorithm trained on experimentally validated translated sORFs and unbiased by unvalidated gene annotations. Through the use of high-throughput phenotyping of SEP mutants and proteomic screens to identify interacting proteins, the Fellow will make functional predictions for candidate SEPs and assess their conservation. The results of this project will contribute to the fundamental understanding of plant functional genomics and provide resources for future sORF and SEP research, while offering insight into the biological function of as-of-yet uncharacterized lncRNAs.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.

这一行动为2023财年NSF植物基因组生物学博士后研究奖学金提供了资金。该奖学金支持在东道主实验室为该研究员制定的研究和培训计划，该研究员还提出了扩大生物学参与度的计划。这项研究和培训计划的标题是“机器学习和高通量反向遗传方法，用于识别和鉴定编码在lncRNAs短ORF中的多肽。”该奖学金的主办机构是博伊斯·汤普森研究所，赞助科学家是安德鲁·纳尔逊博士和亚历山大·斯基里茨博士。植物产生各种小肽，作为通讯信号和关键过程的调节器，包括植物的生长、发育和对环境变化的反应。作为天然衍生化合物，它们是开发更安全的杀虫剂、食品防腐剂和药物的有吸引力的候选者。因此，对小肽进行全面鉴定和功能鉴定的努力不仅会对植物生物学的一般理解产生巨大的影响，而且会对农业和医学产生巨大的影响。通过该项目开发的小肽预测的改进资源和计算工具将对更全面地探索这些具有生物意义的多肽具有无价的价值。此外，建议的生成功能预测的努力将提供对它们的角色和跨应用程序的潜力的洞察。这项工作将提供机器学习、转录组学、蛋白质组学和比较功能基因组学方面的培训。在整个项目中，这位研究员将参与本科生指导机会，扩大接触科学研究和研讨会的机会，这将增加她作为一名教育工作者的效率。小肽在植物发育和胁迫反应中扮演着调节和信号分子的关键角色。尽管长的非编码RNA(LncRNAs)是由其较低的蛋白质编码潜力定义的，但包含小开放阅读框架(SORF)的一个子集最近被证明进行了主动翻译，引入了LncRNAs作为一个未知的小肽来源。长期以来，功能sORF的鉴定及其编码肽(SEPs)的检测一直受到有偏见的预测方法和实验技术限制的阻碍。这导致了对数量未知的含有sORF的lncRNAs的持续错误分类，并忽视了其中编码的潜在生物活性SEP。该项目旨在开发改进的计算工具来预测功能性sORF，并在功能表征其多肽产品方面取得进展。通过对已发表的核糖体图谱数据集的重新分析，该研究员将建立一个模式植物和作物物种中翻译的lncRNA sORFs的数据库，供更广泛的科学界通过tair(arabidopsis.org)访问。这个数据库将被用来开发一个功能性的sORF预测算法，该算法在实验验证的翻译的sORF上进行训练，并且不受未经验证的基因注释的影响。通过使用SEP突变体的高通量表型和蛋白质组筛选来鉴定相互作用的蛋白质，该研究员将对候选SEP进行功能预测并评估它们的保守性。该项目的结果将有助于对植物功能基因组学的基本了解，并为未来的sORF和SEP研究提供资源，同时提供对迄今尚未确定的lncRNA生物学功能的洞察。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。