Identification of Biological Materials of Unknown Origin

申请人的长期目标是理解和阐明DNA内的结构和组织 序列并揭示它们与生物功能的关系。本申请的目的是 实现这一长期目标的一个步骤，是发展技术来阐明神秘的结构， 细菌DNA中可用于鉴定和区分微生物有机体的特征， 包括基因组尚未完全测序的生物，使用DNA的短片段。一 平行的目标是实现调查的独立性作为一个计算生物学家。后一个目标将是 通过课程和S博士实验室的培训完成。Hinrichs，医学博士迫切需要 生物材料的快速鉴定测试已经加强，因为生物材料造成的威胁， 恐怖主义快速识别事实和发作方式对于及时治疗至关重要 干预根据不完整或可能损坏的短序列识别细菌的能力 序列允许危险检测、自动化和低成本分布式感测能力。的 识别技术将使用三种工具来开发：平均互信息（AMI）剖面 它反映了沿着DNA序列的碱基之间的统计关系，这是一种聚类分析技术 由申请人及其同事开发的鉴定基因组特异性三核苷酸聚类模式的方法， 和用于鉴定感兴趣的多核苷酸序列的解析技术。AMI的组成部分 通过对轮廓的分解和分析，识别出具有区分能力的轮廓 使用监督和非监督分类的系数。集群战略将得到完善 通过将该技术中的参数与已知的生物学行为相关联。签名三核苷酸和 将鉴定感兴趣的生物体的多核苷酸聚类模式。不同的分类将 可以组合成用于医学感兴趣的细菌的模型组的树结构测试。

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 Dr. S. Hinrichs, M.D. The urgent need for rapid identification tests for biological materials has intensified because of the threat posed by bio- terrorism. 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.