EAGER: Accelerating Synthetic Biology Discovery through Integrated Curation

综合设计的系统在可持续农业、制造业、传感器开发、国防和医学等领域都有应用。然而，由于文献研究所需的时间和复制现有但记录不充分的工作所需的时间，合成生物学的进展和有用性受到了阻碍。已经尝试使用机器学习和其他方法从出版物中提取信息。虽然这些事后整理的方法取得了一些成功，但为这些出版物制作的注释极易出错。该项目建议从后临时管理转向综合管理，以创建真正的数字出版物。该项目有可能更新科学出版程序，从而减少文献搜索所需的工作量。这将增加知识的重复使用，减少重复劳动。该项目建立在NSF资助的合成生物学知识系统(SBKS)项目的基础上，该项目努力通过将来自部件存储库的数据与从文献中提取的信息整合到一个统一的知识系统中来应对这些挑战。然而，这种形式的临时馆藏需要在出版后由不同于原作者的馆长从手稿和补充文本文件中提取知识。为了处理大量数据，机器被用来搜索自由文本，并试图识别关键字并根据上下文得出它们的含义。这种方法测试了自然语言处理技术的局限性。此外，它留下了只有原始作者才能消除歧义的模棱两可的实体。例如，酵母可以指许多不同的酵母菌株。此外，SBKS项目还提取了出版物中作为补充信息提供的序列。然而，即使提供了这些序列，它们的注释通常也很差、不完整，并且以非机器可读的格式提供。总而言之，SBKS项目表明，通过事后管理来重建这一重要的设计信息非常嘈杂，而且容易出错。该项目有两个主要研究目标：1)建立一个综合管理框架；2)开发一个利用界面提供的管理数据的搜索框架。在完成这些目标后，该项目将使作者更容易按照公平(可查找、可访问、可互操作和可重复使用)的原则提交基因设计信息，从而使研究人员能够搜索序列和相关的基因设计。最后，为了增加这些目标的影响力，我们还将开发教育材料，并参加关键研讨会和会议的外展活动。这一奖项反映了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。

Synthetically designed systems have applications in areas such as sustainable agriculture, manufacturing, sensor development, defense, and medicine. However, the progress and usefulness of synthetic biology has been impeded by the time required for literature studies and the replication of existing but poorly documented work. Attempts have been made to use machine learning and other methods to extract information from publications. While these post-hoc curation methods have had some success, the annotations produced for these publications are extremely error prone. This project proposes to move away from post-hoc and towards integrated curation to create truly digital publications. This project has the potential to update the scientific publishing process and as a result reduce the effort required for literature searches. This would increase knowledge reuse and reduce duplication of efforts. This project builds upon the NSF-funded Synthetic Biology Knowledge System (SBKS) project, which endeavored to address these challenges by integrating data from parts repositories with information extracted from literature into a unified knowledge system. However, this form of post-hoc curation requires the extraction of knowledge from manuscript and supplemental text files after publication by curators separate from the original authors. To handle large amounts of data, machines are used to scour free text and attempt to recognize key words and work out their meaning from context. This approach tests the limits of natural language processing techniques. Additionally, it leaves ambiguous entities that only the original authors might disambiguate. For example, yeast may refer to many different strains of yeast. Furthermore, the SBKS project also extracted sequences provided as supplemental information in publications. However, these sequences, even when they are provided, are typically poorly annotated, incomplete, and provided in non-machine-readable formats. Taken together, the SBKS project demonstrated that reconstruction of this important design information through post-hoc curation is extremely noisy and error prone. This project has two main research aims: 1) the creation of an integrated curation framework and 2) the development of a search framework that takes advantage of the curated data provided by the interface. Upon completion of these aims, this project will have made it easier for authors to submit genetic design information in accordance with the FAIR (findable, accessible, interoperable, and reusable) principles, and thus, enabling the ability of researchers to search for sequences and related genetic designs. Finally, to increase the impact of these aims, we will also develop educational materials and participate in outreach events at key workshops and conferences.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.