Fast and accurate distance‐based phylogenetic placement using divide and conquer

Phylogenetic placement of query samples on an existing phylogeny is increasingly used in molecular ecology, including sample identification and microbiome environmental sampling. As the size of available reference trees used in these analyses continues to grow, there is a growing need for methods that place sequences on ultra-large trees with high accuracy. Distance-based placement methods have recently emerged as a path to provide such scalability while allowing flexibility to analyse both assembled and unassembled environmental samples. In this study, we introduce a distance-based phylogenetic placement method, APPLES-2, that is more accurate and scalable than existing distance-based methods and even some of the leading maximum-likelihood methods. This scalability is owed to a divide-and-conquer technique that limits distance calculation and phylogenetic placement to parts of the tree most relevant to each query. The increased scalability and accuracy enable us to study the effectiven..., See README file for details. We include both simulated datasets and real datasets modeifed from the WoL resources. , See the README file for links to tools, referenced here: https://github.com/balabanmetin/apples2-data将查询样本进行系统发育放置（phylogenetic placement）至已有的系统发育树（phylogeny）上的方法，在分子生态学领域的应用日益广泛，涵盖样本鉴定与微生物组环境采样等研究场景。随着此类分析中所用参考树的规模持续扩张，对能够在超大规模树上实现高精度序列放置的方法的需求愈发迫切。基于距离的放置方法近来应运而生，其既具备出色的可扩展性，又可灵活分析组装完成与未组装的环境样本。本研究提出了一种基于距离的系统发育放置方法APPLES-2，其准确性与可扩展性优于现有同类方法，甚至优于部分顶尖的最大似然（maximum likelihood）方法。该高可扩展性得益于分治技术：仅针对与每个查询序列最相关的树的局部区域开展距离计算与系统发育放置。得益于其提升的可扩展性与准确性，本研究得以探究相关方法的有效性……详见README文件以获取完整细节。本研究包含基于WoL资源修改得到的模拟数据集与真实数据集。工具及相关链接详见README文件，可访问：https://github.com/balabanmetin/apples2-data