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产生连续的序列数据流，此类数据要求在数据生成的同时开展系统发育（phylogenetic）分析。 当前用于贝叶斯系统发育推断的软件包无法在新序列产生后快速将其纳入分析，因此在动态追踪演化进程的场景中实用性欠佳。此类局限可通过应用一类名为序列蒙特卡洛（sequential Monte Carlo, SMC）的贝叶斯统计推断算法开展在线推断来解决——在线推断可持续纳入新数据，以更新后验概率分布的估计值。 本文对多种不同的在线系统发育序列蒙特卡洛（online phylogenetic sequential Monte Carlo, OPSMC）算法进行了描述与评估。研究发现，采用与贝叶斯先验密度相近的分布来生成新系统发育树的提议策略效果欠佳，因此我们开发了引导式提议策略，可使提议密度与后验分布更好地匹配。此外，研究还表明，最简单的引导式提议策略在部分场景中会出现异常行为，导致分析结果不佳，而通过对提议密度施加升温操作即可解决该问题。 研究结果表明，相较于MrBayes中实现的、应用广泛的基于马尔可夫链蒙特卡洛（Markov Chain Monte Carlo, MCMC）的算法，随着序列不断流入，计算一系列系统发育后验分布所需的总时长可通过OPSMC得到显著缩短，且不会造成精度的明显损失。