EAGER: Creating Gene Network Prediction Tools Applicable To Plants and Animals

EAGER是一个高风险高回报的项目，通过开创性的工具为科学界提供了巨大的潜在利益，可以对非模型动物系统的基因组数据进行功能分析。 这项工作将连接来自动物和植物社区的研究人员，建立协同研究互动和合作。生物学研究的所有领域的进展都依赖于我们理解基因功能的能力，基因如何在遗传网络中共同工作，以及这些基因和遗传网络是如何进化的。为了获得这种理解，研究人员传统上依赖于使用遗传学，生物化学和分子生物学技术直接测试基因功能和相互作用。然而，我们现在能够以超过我们执行这些功能测试的能力的速度获得序列数据。换句话说，我们现在可以更快地获得序列数据，而不是弄清楚它的含义！此外，越来越多的序列数据是为尚未开发出复杂遗传工具的生物体生成的，这意味着我们必须找到一种方法来预测这些基因序列的功能，而不依赖于其他序列的先验知识，也不假设类似的基因在不同的生物体中以相同的方式工作。为了解决这个问题，该项目将开发和验证新的计算工具，这些工具将允许使用来自缺乏完全测序基因组的生物体的新序列数据来预测基因功能，而不依赖于先前生成的关于其基因的生化或遗传功能的数据。该项目将使用昆虫、甲壳类动物和蜘蛛模型实验室生物的基因转录序列数据构建和测试这些工具。这些预测工具将改变现有和即将到来的序列数据在多个NSF支持的学科研究中的使用方式。这项工作将通过增加现有数据的实用性而不是产生新的序列数据，并通过扩展现有的功能分子遗传工具用于新的模式生物，为新兴模式生物的发展做出贡献。最后，该项目将在国家和国际跨学科合作方面为培养早期职业科学家做出重要贡献。

This EAGER is a high risk-high payoff project that offers immense potential benefit to the scientific community by pioneering tools to allow functional analysis of genomic data from non-model animal systems. This work will bridge researchers from the animal and plant communities, building synergistic research interactions and collaborations. Advances in all areas of biological research rely on our ability to understand the functions of genes, how genes work together in genetic networks, and how these genes and genetic networks have evolved. To gain this understanding, researchers have traditionally relied on direct tests of gene function and interaction using the techniques of genetics, biochemistry and molecular biology. However, we are now able to obtain sequence data at a rate that outpaces our ability to perform these functional tests. In other words, we can now get sequence data faster than we canfigure out what it means! Moreover, an increasing amount of sequence data is being generated for organisms where sophisticated genetic tools have not been developed, which means that we must find a way to predict the functions of these gene sequences without relying on prior knowledge of other sequences, and without making assumptions that similar genes work the same way in different organisms. To address this problem, this project will develop and validate novel computational tools that will allow predictions about gene functions using new sequence data from organisms that lack fully sequenced genomes, without relying on previously generated data on the biochemical or genetic functions of their genes. This project will build and test these tools using gene transcript sequence data from insect, crustacean and spider model laboratory organisms. These predictive tool will change how existing and forthcoming sequence data are used in research across multiple NSF-supported disciplines. This work will contribute to the development of emerging model organisms by increasing the utility of existing data, rather than generating new sequence data, and by extending existing functional molecular genetic tools for new model organisms. Finally, this project will make an important contribution to the training of early career scientists in national and international cross-disciplinary collaboration.