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treePL:用于大型系统发育树的惩罚似然法分歧时间估计。

treePL: divergence time estimation using penalized likelihood for large phylogenies.

机构信息

Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA.

出版信息

Bioinformatics. 2012 Oct 15;28(20):2689-90. doi: 10.1093/bioinformatics/bts492. Epub 2012 Aug 20.

Abstract

UNLABELLED

Ever larger phylogenies are being constructed due to the explosion of genetic data and development of high-performance phylogenetic reconstruction algorithms. However, most methods for calculating divergence times are limited to datasets that are orders of magnitude smaller than recently published large phylogenies. Here, we present an algorithm and implementation of a divergence time method using penalized likelihood that can handle datasets of thousands of taxa. We implement a method that combines the standard derivative-based optimization with a stochastic simulated annealing approach to overcome optimization challenges. We compare this approach with existing software including r8s, PATHd8 and BEAST.

AVAILABILITY

Source code, example files, binaries and documentation for treePL are available at https://github.com/blackrim/treePL.

摘要

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由于遗传数据的爆炸式增长和高性能系统发育重建算法的发展,越来越大的系统发育树正在被构建。然而,大多数计算分歧时间的方法都局限于比最近发表的大型系统发育树小几个数量级的数据集。在这里,我们提出了一种使用罚似然法计算分歧时间的算法和实现,该方法可以处理数千个分类单元的数据集。我们实现了一种将基于导数的标准优化与随机模拟退火方法相结合的方法,以克服优化挑战。我们将这种方法与包括 r8s、PATHd8 和 BEAST 在内的现有软件进行了比较。

可利用性

treePL 的源代码、示例文件、二进制文件和文档可在 https://github.com/blackrim/treePL 上获得。

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