图形基因组标记的基因组间和基因组内变异的研究
An investigation into inter- and intragenomic variations of graphic genomic signatures
摘要：
我们提供了，在一个广泛的数据集，并使用几个不同的距离，确认CGR模式沿基因组DNA序列保存的假设，是不同的DNA序列起源于不同物种的基因组。这一发现支持了基因组序列的CGR可以作为图形基因组签名的理论。特别是，我们比较了来自六种生物基因组的500多个不同的150，000 bp基因组序列的CGR模式，每种生物都属于生命王国之一：H。sapiens，S. cerevisiae，A. thaliana、恶性疟原虫（P. falciparum）、E. coli和P. furiosus。我们还提供了初步的证据，这种方法的适用性密切相关的物种，通过比较H。sapiens（21号染色体）序列和来自P. troglodytes（Animalia; Y染色体）的超过150个基因组序列，也是150，000 bp长，分析的总长度超过1.01亿个碱基对。我们使用六种不同的距离计算这些基因组序列的CGR之间的成对距离，并构建分子距离图，将所有序列可视化为二维或三维空间中的点，以同时显示它们的相互关系。我们的分析证实，来自相同基因组的DNA序列的CGR模式通常在数量上相似，而来自不同物种基因组的DNA序列则不同。我们对评估距离性能的分析使用了三种不同的质量指标，并表明几种距离优于欧几里得距离，到目前为止，欧几里得距离几乎只用于此类研究。特别是，我们表明，对于这个数据集，DSSIM（结构相异指数）和描述符距离（这里介绍）是最好的能够对基因组序列进行分类。
英文摘要：
We provide, on an extensive dataset and using several different distances, confirmation of the hypothesis that CGR patterns are preserved along a genomic DNA sequence, and are different for DNA sequences originating from genomes of different species. This finding lends support to the theory that CGRs of genomic sequences can act as graphic genomic signatures. In particular, we compare the CGR patterns of over five hundred different 150,000 bp genomic sequences originating from the genomes of six organisms, each belonging to one of the kingdoms of life: H. sapiens, S. cerevisiae, A. thaliana, P. falciparum, E. coli, and P. furiosus. We also provide preliminary evidence of this method's applicability to closely related species by comparing H. sapiens (chromosome 21) sequences and over one hundred and fifty genomic sequences, also 150,000 bp long, from P. troglodytes (Animalia; chromosome Y), for a total length of more than 101 million basepairs analyzed. We compute pairwise distances between CGRs of these genomic sequences using six different distances, and construct Molecular Distance Maps that visualize all sequences as points in a two-dimensional or three-dimensional space, to simultaneously display their interrelationships. Our analysis confirms that CGR patterns of DNA sequences from the same genome are in general quantitatively similar, while being different for DNA sequences from genomes of different species. Our analysis of the performance of the assessed distances uses three different quality measures and suggests that several distances outperform the Euclidean distance, which has so far been almost exclusively used for such studies. In particular we show that, for this dataset, DSSIM (Structural Dissimilarity Index) and the descriptor distance (introduced here) are best able to classify genomic sequences.
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