Integrated Biological Sequence Data Management

密歇根大学获得资金，用于开发一个全面的系统，该系统可以支持对生物序列的复杂声明和高效查询，称为SEQ。其方法是使用复杂而强大的方法来扩展关系数据库引擎，以对序列进行查询。扩展关系数据库引擎，而不是构建独立的序列查询处理工具，是一项更具挑战性的任务，但这是必不可少的，因为它允许最终用户将SQL引擎已经提供的分析工具的功能与查询序列的附加能力结合起来。这种方法的一个关键方面是拥有一个干净的查询代数，它提供了一组强大的生物序列查询功能，并且可以在扩展的关系模型的框架内进行调整。SEQ将通过扩展现有的Postgres数据库引擎来实现。研究人员(计算机科学家和生物医学研究人员)之间的合作还将促进SEQ系统在一个项目中的实际部署，该项目将分析各种基因组，寻找与眼睛发育和视觉功能必不可少的基因相关的转录调控元件。这一建议的关键智力贡献在于开发了一种声明性查询工具，用于在关系框架中管理生物序列数据集。这项工作自然需要设计和实现跨越关系数据库引擎大部分层的方法，包括查询代数、查询语言、查询处理算法、数据存储方法和查询优化方法。SEQ项目将在这些数据库管理方面为序列查询处理带来新的计算机科学方法。当前的初步原型清楚地展示了SEQ方法中这些方面的巨大功能和性能优势。除了SEQ将对计算机科学研究做出贡献外，该项目还将直接协助分析对杆状感光细胞发育和功能至关重要的转录因子的下游靶标。这一提议的更广泛的影响是使生命科学家能够使用声明性和高效的查询方法来查询和管理序列数据，并允许使用传统的关系查询来处理复杂的序列查询。该项目将使用ECL许可证产生一个免费的开源OSI认证的SEQ系统版本。这一版本将允许整个生命科学界利用这些强大的查询方法。我们注意到，许多生物模型数据库开始使用关系数据库(通常是postgres)来管理序列数据；例如，请参阅GMOD使用的CHADO模式。序列分析工具非常适用于这种更广泛的用户范围，因为系统本质上将向Postgres添加复杂的序列查询功能(具有高效的性能)。这项提议的更广泛影响包括加强密歇根大学新生的生物信息学课程。该项目将导致计算机科学博士生在生命科学研究方面的交叉培训，并包括至少一名女性博士的培训。该项目将允许EECS系和密歇根大学凯洛格眼科中心的本科生和研究生、博士后工作人员和教员在跨越计算机科学和遗传学学科的方法方面促进密切互动。

The University of Michigan is awarded funds to develop a comprehensive system that can support complex declarative and efficient querying on biological sequences, called SEQ. The approach is to extend a relational database engine with sophisticated and powerful methods for querying on sequences. Extending a relational database engine, rather than build a stand-alone sequence query processing tool, is a much more challenging task, but is essential as it allows the end-user to combine the power of analytical facilities already provided by SQL engines with the added ability to query sequences. A crucial aspect of this approach is to have a clean query algebra that provides a powerful set of biological sequence querying features, and can be accommodated within the framework of an extended relational model. SEQ will be implemented by extending the existing Postgres database engine. The collaboration between the investigators (computer scientists and biomedical researchers) will also facilitate actual deployment of the SEQ system in a project that will analyze various genomes for transcriptional regulatory elements related to genes essential for eye development and visual function. The key intellectual contribution of this proposal is in the development of a declarative querying tool for managing biological sequence data sets in a relational framework. This effort naturally requires designing and implementing methods that span most of the layers of a relational database engine, including query algebra, query language, query processing algorithms, data storage methods, and query optimization methods. The SEQ project will lead to new computer science methods for sequence query processing in each of these database management aspects. A current preliminary prototype clearly demonstrates the tremendous functionality and performance benefits of these aspects in the SEQ approach. In addition to the contributions that SEQ will make to computer science research, the project will also directly assist in the analysis of downstream targets for a transcription factor critical for rod photoreceptor development and function. The broader impacts of this proposal are in enabling life scientists to query and manage sequence data using declarative and efficient querying methods, and to enable the processing of complex sequence queries with traditional relational querying. The project will result in a free open-source OSI-certified release of the SEQ system using the ECL license. This release will allow the entire life sciences community to leverage these powerful querying methods. We note that a number of model organism databases are starting to use relational databases (often Postgres) for managing sequence data; as an example see the Chado schema used by GMOD. Sequence analysis tools are very applicable to this broader range of users as the system will essentially add complex sequence querying functionality (with efficient performance) to Postgres. The broader impacts of this proposal include enhancing the nascent bioinformatics curriculum at the University of Michigan. The project will result in cross-training computer science PhD students in life sciences research, and include the training of at least one women PhD. This project will allow undergraduate and graduate students, post-doctoral staff, and faculty in the EECS department and the Kellogg's Eye Center at the University of Michigan to foster a close interaction in the methods that span the disciplines of computer science and genetics.