EAGER: Computational Rapid Identification and Putative Characterization of Understudied Microbial Community Gene Products

宏基因组测序项目产生了数千到数百万个未表征的微生物基因，这些基因在所有研究领域几乎完全被忽视。 解决这一问题将从根本上改变科学家探索微生物群落或新微生物分离株的方式，从而解释它们的遗传物质及其功能。 这种潜在的高回报与高风险相平衡，因为微生物群落信息以前没有被挖掘以解决这个问题。 在缺乏更广泛的初步数据或一年或多年的事先验证的情况下，这就需要应用以前从未尝试过的方法来优先考虑和表征靶微生物基因。 最后，虽然这里应用于基因功能预测的下游方法将适用于真核模型系统，但这将需要在一个全新的领域（不依赖培养的原核生物）和多学科交叉（计算基因功能预测，数据集成和网络挖掘与微生物群落研究和微生物学）中应用。目前的技术产生新的核苷酸序列信息的速度，大大超过了我们的能力，以功能上表征这些序列。 在新测序的原核生物基因组和群落中，三分之一到更典型的四分之三以上的蛋白质不能进行功能表征。 宏基因组测序的增加导致数百万个最近鉴定的、完全未表征的微生物基因，这代表了对高效的计算基因优先级排序和表征系统的显著需求。 该项目将首先利用宏基因组序列进行新的努力，优先考虑未表征的基因进行进一步研究，以打破目前针对来自研究良好的基因家族的基因的方法。 第二，整合，基于网络的方法将被用来加速和自动化的假定功能的分配，用于高优先级基因靶点的后续验证。 这两种新方法都将作为免费提供的有文档记录的软件实施，并与试点数据集一起沿着分发给更广泛的研究界。 一名博士后研究员，一名研究生和一名本科生将在两年的项目期间接受综合实验和计算方法的尖端培训。

Metagenomic sequencing projects generate thousands to millions of uncharacterized microbial genes that are almost completely ignored in all fields of research. Addressing this problem will fundamentally transform how scientists exploring microbial communities or new microbial isolates will interpret their genetic material and the function of that material. This potentially high payoff is balanced by a high risk in that microbial community information has not previously been mined in order to address this issue. In the absence of more extensive preliminary data, or one or more years of prior validation, this necessitates the application of previously untried approaches to prioritize and characterize the targeted microbial genes. Lastly, while the downstream methods to be applied here for gene function prediction will be adapted from eukaryotic model systems, this will require both application in a completely new area (culture-independent prokaryotes) and the intersection of multiple disciplines (computational gene function prediction, data integration, and network mining with microbial community studies and microbiology). Current technologies generate novel nucleotide sequence information at a rate that greatly outpaces our capability to functionally characterize those sequences. From one third to more typically over three quarters of proteins in newly-sequenced prokaryotic genomes and communities cannot be functionally characterized. The increase in metagenomic sequencing results in millions of recently identified, completely uncharacterized microbial genes representing a significant need for efficient computational gene prioritization and characterization systems. This project will first leverage metagenomic sequences in a novel effort to prioritize the uncharacterized genes for further study in order to break from current approaches targeting genes from well-studied gene families. Second, integrative, network-based approaches will be used to accelerate and automate the assignment of putative function for subsequent validation in high-priority gene targets. Both new approaches will be implemented as freely available, documented software and distributed to the broader research community along with pilot datasets. A postdoctoral fellow, a graduate student and undergraduate students will receive cutting edge training in integrative experimental and computational approaches during the two-year project.