Phylo-MCOA: A Fast and Efficient Method to Detect Outlier Genes and Species in Phylogenomics Using Multiple Co-inertia Analysis
Phylo-MCOA: A Fast and Efficient Method to Detect Outlier Genes and Species in Phylogenomics Using Multiple Co-inertia Analysis
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DOI：
10.1093/molbev/msr317
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发表时间：
2012-06-01
影响因子：
10.7
通讯作者：
Aguileta, Gabriela
中科院分区：
文献类型：
--
作者：
de Vienne, Damien M.;Ollier, Sebastien;Aguileta, Gabriela
目前正在定期探索全基因组数据集以推断系统发育树，但不同基因产生的树之间经常存在不一致性。基因组学的一个重要目标是确定哪些个体基因和物种产生相同的系统发育树，从而可能共享相同的进化历史。另一方面，识别哪些基因和物种产生不协调的拓扑结构，从而以不同的方式进化或代表数据中的噪声也是至关重要的。后者是离群基因或物种，它们可以提供关于潜在有趣的生物过程的丰富信息，例如不完全谱系排序，杂交和水平基因转移。在这里，我们提出了一种新的方法来探索基因组树空间和检测离群基因和物种的基础上，多重共惯性分析（MCOA），有效地捕捉和比较的相似性，在系统发育拓扑产生的单个基因。我们的方法允许快速识别离群基因和物种提取的相似性和差异，在成对的距离，在所有的树，同时所有的物种之间。这是通过使用MCOA来实现的，MCOA从各个排序中找到连续的分解轴（即，从距离矩阵导出），其最大化协方差函数。该方法可以作为一组R函数免费使用。源代码和教程可以在http://phylomcoa.cgenomics.org上找到。
Full genome data sets are currently being explored on a regular basis to infer phylogenetic trees, but there are often discordances among the trees produced by different genes. An important goal in phylogenomics is to identify which individual gene and species produce the same phylogenetic tree and are thus likely to share the same evolutionary history. On the other hand, it is also essential to identify which genes and species produce discordant topologies and therefore evolve in a different way or represent noise in the data. The latter are outlier genes or species and they can provide a wealth of information on potentially interesting biological processes, such as incomplete lineage sorting, hybridization, and horizontal gene transfers. Here, we propose a new method to explore the genomic tree space and detect outlier genes and species based on multiple co-inertia analysis (MCOA), which efficiently captures and compares the similarities in the phylogenetic topologies produced by individual genes. Our method allows the rapid identification of outlier genes and species by extracting the similarities and discrepancies, in terms of the pairwise distances, between all the species in all the trees, simultaneously. This is achieved by using MCOA, which finds successive decomposition axes from individual ordinations (i.e., derived from distance matrices) that maximize a covariance function. The method is freely available as a set of R functions. The source code and tutorial can be found online at http://phylomcoa.cgenomics.org.