Ideas Lab: Collaborative Research: Integrating cross-kingdom lncRNA genetic and functional interactions to build predictive network models

尽管细胞中大多数众所周知的基因都包含通过RNA中间体创造蛋白质作为功能产物的指令，但科学家们正在发现越来越多的基因可以产生对生物体的健康和生命周期至关重要的非蛋白质编码RNA。长非编码RNA(LncRNAs)最近被认为是重要的，而MOST的确切作用仍然知之甚少。对每个lncRNA的详细研究将提供有关可能功能的基本信息，但这将是时间、劳动力和资源密集型的。计算分析可以提供一种有效的方法来同时分析许多不同的分子，根据不同的共同特征对分子进行分类，并使用这些分类和其他信息来预测功能。该项目将开发和使用计算工具来整合先前研究产生的许多不同的数据类型，并预测lncRNAs在植物和动物细胞中的功能。这些预测将允许假设特定的lncRNA的丢失或增加可能如何影响细胞或整个生物体。然后，这些假设将通过实验进行测试，作为评估预测有效性的一种方式。项目资源还将支持公共教育活动，关于这些新兴科学主题的K-12教师培训，以及学生参与现代计算机和分子基因组研究。该项目的一个中心假设是，受来自丰富数据的实验的生理和分子数据的约束，多模式因果网络分析方法可以用于从功能上注释LncRNA，并为未来的实验建立预测模型。该项目的最终目标是开发基于网络的模型，该模型可以基于遗传、分子和基因组相互作用来预测lncRNA的功能，然后可以应用于任何物种的数据集。通过这个奖项，研究人员将构建和测试描述和预测lncRNA功能的网络。来自人类细胞系的公开可用的高通量测序、相关组学数据和基于文献的功能信息将被用于推断全定向基因调控网络(GRN)。机器学习模型将被应用于基于网络拓扑和连通性的基本LncRNA预测。类似的方法将应用于模式植物拟南芥中的现有数据集。这两个网络的预测将通过操纵ncRNA表达和使用纳米串技术分析对网络的影响来测试。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。

Although most well-known genes in cells include instructions for creating proteins as a functional product via an RNA intermediate, scientists are discovering an increasing number of genes that create non-protein coding RNA that are essential for the health and life cycle of organisms. Long non-coding RNAs (lncRNAs) are relatively recently recognized as important, and the exact role of most remains poorly understood. Detailed study of each lncRNA would provide essential information about possible functions, but would be time, labor, and resource intensive. Computational analyses can provide an efficient way to analyze many different molecules simultaneously, classify the molecules based on different shared features, and use these classifications and other information to predict function. This project will develop and use computational tools to integrate many different data types generated by previous studies and predict functions of lncRNAs in plant and animal cells. These predictions will allow for hypotheses about how the loss or increase of specific lncRNAs might impact a cell or whole organism. These hypotheses will then be tested through experimentation as a way to assess the efficacy of the predictions. Project resources will also support public education activities, K-12 teacher training on these emerging scientific topics, and student participation in modern computer and molecular genomics research.A central hypothesis of this project is that a multimodal, causal network analysis approach, constrained by physiological and molecular data from data-rich experiments, can be utilized to functionally annotate lncRNAs and to build a predictive model for future experiments. The ultimate goal of this project is to develop network-based models that can predict lncRNA function based on genetic, molecular, and genomic interactions, which can then be applied to datasets from any species. Through this award, researchers will construct and test networks that depict and predict lncRNA functions. Publicly available high-throughput sequencing from human cell lines, associated -omics data, and literature-based functional information will be used to infer a fully directional gene-regulatory network (GRN). Machine learning models will be applied to predict essential lncRNAs based on network topologies and connectivity. A similar approach will be applied to available datasets in the model plant, Arabidopsis thaliana. Predictions from both networks will be tested by manipulating ncRNA expression and assaying the impact on the network using Nanostring technology.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.