自然基因组重排距离的计算
Computing the rearrangement distance of natural genomes
摘要：
在过去的25年里，基因组距离的计算一直是计算比较基因组学中非常活跃的领域。实质性的成果包括Hannenhalli和Pevzner在1995年提出的反演距离的多项式时间可计算性，以及Yancopoulos等人在2005年提出的双重切割和连接（DCJ）距离。然而，这两个结果都依赖于这样的假设，即比较中的基因组包含相同的一组独特标记（同线基因组区域，有时也称为基因）。2015年，Shao、Lin和Moret放宽了这一条件，允许在分析中使用重复标记。这个广义版本的基因组距离问题是NP难的，他们给出了一个ILP解决方案，该解决方案足够有效，可以应用于现实世界的数据集。他们的方法的一个限制是，它只能应用于平衡的基因组，具有相同数量的任何标记的重复。因此，它仍然需要对输入数据进行精细的预处理，其中必须去除不平衡标记的过量拷贝。在本文中，我们提出了一个算法解决自然基因组的基因组距离问题，其中任何标记可能会出现任意次数。我们的方法是基于一个新的图形数据结构，多关系图，它允许一个优雅的扩展的ILP由邵，林和莫雷特计数运行的标记，在一个基因组相对于其他的，需要插入或删除，分别。通过这种扩展，以前对基因组构型的限制被解除，首次实现了不妥协的重排分析。任何标记序列都可以直接用于距离计算。我们的方法的评估表明，它可以用来分析基因组多达几万个标记，我们证明了模拟和真实的数据。
英文摘要：
The computation of genomic distances has been a very active field of computational comparative genomics over the last 25 years. Substantial results include the polynomial-time computability of the inversion distance by Hannenhalli and Pevzner in 1995 and the introduction of the double-cut and join (DCJ) distance by Yancopoulos et al. in 2005. Both results, however, rely on the assumption that the genomes under comparison contain the same set of unique markers (syntenic genomic regions, sometimes also referred to as genes). In 2015, Shao, Lin and Moret relax this condition by allowing for duplicate markers in the analysis. This generalized version of the genomic distance problem is NP-hard, and they give an ILP solution that is efficient enough to be applied to real-world datasets. A restriction of their approach is that it can be applied only to balanced genomes, that have equal numbers of duplicates of any marker. Therefore it still needs a delicate preprocessing of the input data in which excessive copies of unbalanced markers have to be removed. In this paper we present an algorithm solving the genomic distance problem for natural genomes, in which any marker may occur an arbitrary number of times. Our method is based on a new graph data structure, the multi-relational diagram, that allows an elegant extension of the ILP by Shao, Lin and Moret to count runs of markers that are under- or over-represented in one genome with respect to the other and need to be inserted or deleted, respectively. With this extension, previous restrictions on the genome configurations are lifted, for the first time enabling an uncompromising rearrangement analysis. Any marker sequence can directly be used for the distance calculation. The evaluation of our approach shows that it can be used to analyze genomes with up to a few ten thousand markers, which we demonstrate on simulated and real data.
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