本项目计划构建基于自然向量的病毒基因组数据库, 并用它快速检测来自新病毒的威胁。.1．确定病毒的自然向量表示和基因组空间。考虑病毒基因组序列中的核甘酸的数量以及分布情况，将每个序列表示成一个数值向量（自然向量）。可以证明，序列和自然向量之间是一一对应。两个病毒之间的生物距离可以用它们所对应的自然向量的几何距离来刻画。自然向量所构成的空间可以看成是一个多维欧式空间的子空间，即基因组空间。.2．建立病毒基因组数据库。存储所有已知病毒基因组序列的自然向量。与现有的数据库不同，本数据库将支持对所有已知的病毒进行同时的比较分析。现有方法中，只有自然向量法可以完成这个任务。.3．新病毒威胁预警。在获得新病毒序列信息基础上，快速计算出其相应的自然向量并与我们数据库中的自然向量作对比。距离相近的自然向量所对应的病毒可能具有类似性质特点。通过分析与新病毒相近的已知病毒的毒性和传播方式，对新病毒作出预警报告。

In this proposal, we construct the natural vectors for DNA/genome sequences. The parameters used here are based on the numbers and distributions of nucleotides in the sequence, which is a natural way to describe these sequences. To each DNA sequence we associate a natural sequence of parameters, called a natural vector, describing the numbers and distributions of nucleotides in the sequence. We show that the correspondence between a natural vector and a DNA sequence is one-to-one. A natural distance between two DNA sequences is the distance between their corresponding natural vectors. This creates a genome space with biological distance, which allows us to do phylogenetic analysis in the most natural and easiest manner. . The classification model based on permanent process is proposed to do clustering and classification of the natural vectors. Unlike many research works in the literature of classification problems, this classification model assumes only exchangeability instead of independence on observations. It has a mathematical framework allowing the existence of previously unobserved classes. Regardless of the number of classes or the dimension of the feature variables, the proposed model may require only 2-3 parameters for fitting the covariance structure within clusters. Due to the flexibility of the underlying permanent process, the model works well even if the class occupies non-convex, disjoint regions, or regions overlapped with other classes in the feature space. It is ideal for the classification and clustering problems in the natural vector genome space. . The first goal of this project is to construct and maintain a database of "natural vector bank" to store all natural vectors of virus genome sequences. Then we need to do clustering on those natural vectors. The HRV example in this proposal shows the clustering results based on natural vectors are biologically meaningful. Considering the exponentially increasing size of known genome database, the natural vector method is the only feasible approach so far to cluster the genome space. In a pilot study of 27,643 genome sequences, it takes only a couple of hours using natural vector method to compute all the pairwise distances, while it will take four years using the classical multiple alignment methods.. The ultimate goal of this project is to detect threats from new viruses. Having obtained the natural vector corresponding to the new virus, we can locate the new virus in the genome space. The closer distance from a virus to a known cluster represents the more similarities between them. The similarities indicate that the new virus and other viruses in the closest cluster have the similar functions or behavior patterns.