Reconstruction of Ancestral Genomes in Presence of Gene Gain and Loss
Reconstruction of Ancestral Genomes in Presence of Gene Gain and Loss
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DOI：
10.1089/cmb.2015.0160
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发表时间：
2016-03-01
影响因子：
1.7
通讯作者：
Alekseyev, Max A.
中科院分区：
文献类型：
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作者：
Avdeyev, Pavel;Jiang, Shuai;Alekseyev, Max A.
由于大多数戏剧性的基因组变化是由基因组重排以及基因复制和得失事件引起的，因此了解它们的机制并重建给定基因组的祖先基因组变得至关重要。这个问题被证明是NP完全的，即使在最简单的三个基因组的情况下，因此需要启发式的而不是精确的算法解决方案。与此同时，更多的输入基因组实际上可能会简化实践中的问题，正如早先用MGRA说明的那样，MGRA是一种用于重建多个基因组的祖先基因组的最先进的软件工具。MGRA和其他类似工具的关键障碍之一是当同一断点区域在进化过程中被几个不同的基因组重排打破时，断点重复使用的存在。此外，这种工具通常仅限于由相同基因组成的基因组，每个基因在每个基因组中都有一个拷贝。这一限制使得这些工具不适用于许多生物数据集，并且降低了在不同数据集中重建祖先的分辨率。我们通过将MGRA算法扩展到具有不同基因含量的基因组来解决这些不足。开发的下一代工具MGRA2可以处理基因得失事件，并分享了MGRA在有限断点重复使用的情况下唯一重建祖先基因组的能力。此外，MGRA2使用了许多新的启发式算法来应对更高的断点重用和MGRA无法访问的过程数据集。在实际实验中，与其他祖先基因组重建工具相比，MGRA2在模拟和真实基因组重建方面表现出了更好的性能。
Since most dramatic genomic changes are caused by genome rearrangements as well as gene duplications and gain/loss events, it becomes crucial to understand their mechanisms and reconstruct ancestral genomes of the given genomes. This problem was shown to be NP-complete even in the "simplest" case of three genomes, thus calling for heuristic rather than exact algorithmic solutions. At the same time, a larger number of input genomes may actually simplify the problem in practice as it was earlier illustrated with MGRA, a state-of-the-art software tool for reconstruction of ancestral genomes of multiple genomes. One of the key obstacles for MGRA and other similar tools is presence of breakpoint reuses when the same breakpoint region is broken by several different genome rearrangements in the course of evolution. Furthermore, such tools are often limited to genomes composed of the same genes with each gene present in a single copy in every genome. This limitation makes these tools inapplicable for many biological datasets and degrades the resolution of ancestral reconstructions in diverse datasets. We address these deficiencies by extending the MGRA algorithm to genomes with unequal gene contents. The developed next-generation tool MGRA2 can handle gene gain/loss events and shares the ability of MGRA to reconstruct ancestral genomes uniquely in the case of limited breakpoint reuse. Furthermore, MGRA2 employs a number of novel heuristics to cope with higher breakpoint reuse and process datasets inaccessible for MGRA. In practical experiments, MGRA2 shows superior performance for simulated and real genomes as compared to other ancestral genome reconstruction tools.