More than ever, today we are left with the abundance of molecular data outpaced by the advancements of the phylogenomic methods. Especially in the case of presence of many genes over a set of species under the phylogeny question, more sophisticated methods than the crude way of concatenation is needed. In this letter, by placing the continuous-time Markov chain (CTMC) on the species set, I present a novel model for inferring the phylogeny, obtaining the network graph, or drawing the proximity conclusions. The rate of transition between states is calculated based on the binary character paths between each two species. This is the base for the pairwise distances between species. Next to its generic use, the formulation of the model allows the site-wise phylogenetic inference and a mathematically justified method of combining these information to form as big as the whole genome phylogenetic inference. Although based on the characters or traits, this model is inherently a distance method but its advantage to other methods of the same class is its ability to incorporate the information of all the other species in forming the pairwise distance between two them.
翻译:今天,由于植物基因学方法的进步,我们比以往更加需要大量的分子数据。 特别是,在植物基因问题下,在一系列物种上存在许多基因的情况下,需要比粗略的凝聚方式更复杂的方法。 在本信里,通过将连续时间的Markov链(CTMC)放在物种组中,我提出了一个新的模型,用以推断植物基因,获取网络图,或绘制近距离结论。 国家间的转变速度根据两种物种之间的二元字符路径计算。 这是物种间对齐距离的基础。除了其通用用途外,模型的配方还允许根据地点的植物基因推断法和一种数学上合理的方法,将这些信息合并成与整个基因组的植物遗传学推断法一样大。 尽管基于特性或特征,这一模型本质上是一种距离方法,但它对同类物种的其他方法的优势在于它能够将所有其他物种的信息纳入到它们之间的对齐距离。