Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA.
Department of Mathematical Sciences, University of Delaware, Newark, DE.
Mol Biol Evol. 2018 Jan 1;35(1):242-246. doi: 10.1093/molbev/msx253.
Phylogenetics has seen a steady increase in data set size and substitution model complexity, which require increasing amounts of computational power to compute likelihoods. This motivates strategies to approximate the likelihood functions for branch length optimization and Bayesian sampling. In this article, we develop an approximation to the 1D likelihood function as parametrized by a single branch length. Our method uses a four-parameter surrogate function abstracted from the simplest phylogenetic likelihood function, the binary symmetric model. We show that it offers a surrogate that can be fit over a variety of branch lengths, that it is applicable to a wide variety of models and trees, and that it can be used effectively as a proposal mechanism for Bayesian sampling. The method is implemented as a stand-alone open-source C library for calling from phylogenetics algorithms; it has proven essential for good performance of our online phylogenetic algorithm sts.
系统发生学的数据集中的信息量和替代模型的复杂度都在不断增加，这需要越来越多的计算能力来计算似然值。这就促使人们寻求策略来近似分支长度优化和贝叶斯采样的似然函数。在本文中，我们开发了一种针对由单个分支长度参数化的 1D 似然函数的逼近方法。我们的方法使用了一种从最简单的系统发生似然函数——二项对称模型中抽象出来的四参数替代函数。我们证明了它可以在各种分支长度上拟合一个替代函数，它适用于广泛的模型和树，并且可以有效地用作贝叶斯采样的提议机制。该方法作为一个独立的开源 C 库实现，可从系统发生算法中调用；对于我们的在线系统发生算法 sts 的良好性能，该方法非常重要。
