Data from: Effective online Bayesian phylogenetics via sequential Monte Carlo with guided proposals

Modern infectious disease outbreak surveillance produces continuous streams of sequence data which require phylogenetic analysis as data arrives. Current software packages for Bayesian phylogenetic inference are unable to quickly incorporate new sequences as they become available, making them less useful for dynamically unfolding evolutionary stories. This limitation can be addressed by applying a class of Bayesian statistical inference algorithms called sequential Monte Carlo (SMC) to conduct online inference, wherein new data can be continuously incorporated to update the estimate of the posterior probability distribution. In this paper we describe and evaluate several different online phylogenetic sequential Monte Carlo (OPSMC) algorithms. We show that proposing new phylogenies with a density similar to the Bayesian prior suffers from poor performance, and we develop guided proposals that better match the proposal density to the posterior. Furthermore, we show that the simplest guided proposals can exhibit pathological behavior in some situations, leading to poor results, and that the situation can be resolved by heating the proposal density. The results demonstrate that relative to the widely-used MCMC-based algorithm implemented in MrBayes, the total time required to compute a series of phylogenetic posteriors as sequences arrive can be significantly reduced by the use of OPSMC, without incurring a significant loss in accuracy.现代传染病暴发监测会生成持续流式序列数据，这类数据要求在数据接收的同时开展系统发育分析（phylogenetic analysis）。 当前用于贝叶斯系统发育推断（Bayesian phylogenetic inference）的软件包无法在新序列可用时快速将其纳入分析，因此在追踪动态演化过程时实用性受限。该局限可通过应用一类名为序贯蒙特卡洛（sequential Monte Carlo, SMC）的贝叶斯统计推断算法开展在线推断（online inference）来解决——在线推断可持续纳入新数据以更新后验概率分布（posterior probability distribution）的估计结果。本文对多种不同的在线系统发育序贯蒙特卡洛（online phylogenetic sequential Monte Carlo, OPSMC）算法进行了描述与评估。 研究发现，以与贝叶斯先验（Bayesian prior）相近的密度生成新系统发育树的方式性能欠佳，据此我们开发了引导式提议分布，可使提议密度更好地匹配后验分布。此外，研究表明最简单的引导式提议分布在部分场景下会出现异常行为，导致分析结果不佳，而该问题可通过对提议密度进行升温操作得以解决。 研究结果显示，相较于MrBayes中实现的广泛应用的基于马尔可夫链蒙特卡洛（Markov Chain Monte Carlo, MCMC）的算法，通过使用OPSMC，在序列陆续到达时计算一系列系统发育后验分布所需的总时间可大幅缩短，且不会造成显著的精度损失。