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，是朝着实现这一长期目标迈出的一步，是开发用于阐明细菌DNA中潜在结构特征的技术，这些特征可用于鉴定和区分微生物有机体，包括基因组尚未完全测序的生物体，使用短片段DNA。一个平行的目标是实现作为计算生物学家的研究独立性。后一个目标将通过Steven Hinrichs医学博士实验室的课程和培训来实现。由于生物恐怖主义构成的威胁，对生物材料快速鉴定测试的迫切需求已经加剧。快速识别事实和攻击方式对于及时的治疗干预至关重要。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传感能力。鉴定技术将使用三种工具来开发：平均互信息（AMI）配置文件，它反映了沿DNA序列的碱基之间的统计关系，由申请人和同事开发的聚类分析技术，用于鉴定基因组特定的三核苷酸聚类模式，以及用于鉴定感兴趣的多核苷酸序列的解析技术。利用监督分类和非监督分类对AMI剖面进行分解和系数分析，确定AMI剖面中具有判别能力的分量。聚类策略将通过将技术中的参数与已知的生物行为相关联来改进。特征三核苷酸和多核苷酸聚类模式将被识别为感兴趣的生物体。不同的分类将被组合成一个树形结构的测试，用于医学兴趣的细菌模型面板。

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.