Identification of Biological Materials of Unknown Origin

描述（由申请人提供）：申请人的长期目标是理解和阐明DNA序列内的结构和组织，并揭示它们与生物功能的关系。本申请的目的是开发用于阐明细菌DNA中隐藏的结构特征的技术，该技术可用于使用DNA的短片段鉴定和区分微生物有机体，包括其基因组尚未完全测序的有机体，该技术是实现该长期目标的一个步骤。一个平行的目标是实现调查的独立性作为一个计算生物学家。后一个目标将通过课程和在Steven Hinrichs，M.D.实验室的培训来实现。由于生物恐怖主义构成的威胁，对生物材料的快速鉴定测试的迫切需要更加迫切。快速识别事实和攻击模式对于及时进行治疗干预至关重要。基于不完整或可能损坏的序列的短序列识别细菌的能力允许危险检测、自动化和低成本分布式感测能力。将使用三种工具开发鉴定技术：平均互信息（AMI）谱，其反映了DNA序列沿着碱基之间的统计关系;由申请人及其同事开发的聚类分析技术，其鉴定基因组特异性三核苷酸聚类模式;以及用于鉴定感兴趣的多核苷酸序列的解析技术。AMI配置文件，具有歧视性的能力的组件将被确定分解的配置文件和分析的系数，使用监督和非监督分类。将通过将技术中的参数与已知的生物学行为相关联来细化聚类策略。将鉴定目标生物的特征三核苷酸和多核苷酸聚类模式。不同的分类将被组合成一个树状结构的测试，用于医学感兴趣的细菌的模型面板。

DESCRIPTION (provided by applicant): The applicant's long term goal is to understand and elucidate structure and organization within DNA sequences and uncover their relationship to biological functions. The objective of this application, which is a step toward the attainment of this long term goal, is to develop techniques for elucidating occult structural features in bacterial DNA which can be used for identification and differentiation of microbial organisms, including organisms whose genome has not been completely sequenced, using short fragments of DNA. A parallel goal is to achieve investigative independence as a computational biologist. The latter goal will be accomplished through coursework and training in the laboratories of Steven Hinrichs, M.D. The urgent need for rapid identification tests for biological materials has intensified because of the threat posed by bioterrorism. Rapid identification of both the fact and the mode of attack is essential for timely therapeutic intervention. The ability to identify bacteria based on short sequences of incomplete or possibly corrupt sequences allows for hazard detection, automation, and low cost distributed sensing capability. The identification techniques will be developed using three tools: the average mutual information (AMI) profile which reflects statistical relationships between bases along the DNA sequence, a cluster analysis technique developed by the applicant and co-workers which identifies genome specific trinucleotide clustering patterns, and a parsing technique for identification of polynucleotide sequences of interest. Components of the AMI profile which possess discriminatory capabilities will be identified by decomposing the profile and analyzing the coefficients using both supervised and unsupervised classification. The clustering strategy will be refined by correlating parameters in the technique with known biological behavior. Signature trinucleotide and polynucleotide clustering patterns will be identified for organisms of interest. The different classifications will be combined into a tree structured test for a model panel of bacteria of medical interest.