School of Mathematical and Statistical Sciences, Arizona State University, Tempe, 85287, USA.
BMC Bioinformatics. 2011 Apr 21;12:111. doi: 10.1186/1471-2105-12-111.
MixtureTree v1.0 is a Linux based program (written in C++) which implements an algorithm based on mixture models for reconstructing phylogeny from binary sequence data, such as single-nucleotide polymorphisms (SNPs). In addition to the mixture algorithm with three different optimization options, the program also implements a bootstrap procedure with majority-rule consensus.
The MixtureTree program written in C++ is a Linux based package. The User's Guide and source codes will be available at http://math.asu.edu/~scchen/MixtureTree.html
The efficiency of the mixture algorithm is relatively higher than some classical methods, such as Neighbor-Joining method, Maximum Parsimony method and Maximum Likelihood method. The shortcoming of the mixture tree algorithms, for example timing consuming, can be improved by implementing other revised Expectation-Maximization(EM) algorithms instead of the traditional EM algorithm.
MixtureTree v1.0 是一个基于 Linux 的程序（用 C++编写），它实现了一种基于混合模型的算法，用于从二进制序列数据（如单核苷酸多态性（SNPs））中重建系统发育。除了具有三种不同优化选项的混合算法外，该程序还实现了基于多数规则共识的自举程序。
用 C++编写的 MixtureTree 程序是一个基于 Linux 的软件包。用户指南和源代码将可在 http://math.asu.edu/~scchen/MixtureTree.html 获得。
混合算法的效率相对高于一些经典方法，如邻接法、最大简约法和最大似然法。混合树算法的缺点，例如耗时，可以通过实现其他修订的期望最大化（EM）算法来改进，而不是使用传统的 EM 算法。
