Chronograms –phylogenies with branch lengths proportional to time– represent key data on timing of evolutionary events for the study of natural processes in many areas of biological research. Chronograms also provide valuable information that can be used for education, science communication, and conservation policy decisions. Yet, achieving a high-quality reconstruction of a chronogram is a difficult and resource-consuming task. Here we present DateLife, a phylogenetic software implemented as an R package and an R Shiny web application available at www .datelife.org, that provides services for efficient and easy discovery, summary, reuse, and reanalysis of node age data mined from a curated database of expert, peer-reviewed, and openly available chronograms. The main DateLife workflow starts with one or more scientific taxon names provided by a user. Names are processed and standardized to a unified taxonomy, allowing DateLife to run a name match across its local chronogram database that is curated from Open Tree of Life’s phylogenetic repository, and extract all chronograms that contain at least two queried taxon names, along with their metadata. Finally, node ages from matching chronograms are mapped using the congruification algorithm to corresponding nodes on a tree topology, either extracted from Open Tree of Life’s synthetic phylogeny or one provided by the user. Congruified node ages are used as sec- ondary calibrations to date the chosen topology, with or without initial branch lengths, using different phylogenetic dating methods such as BLADJ, treePL, PATHd8 and MrBayes. We performed a cross-validation test to compare node ages resulting from a DateLife analysis (i.e, phylogenetic dating using secondary calibrations) to those from the original chronograms (i.e, obtained with primary calibrations), and found that DateLife’s node age estimates are consistent with the age estimates from the original chronograms, with the largest variation in ages occurring around topologically deeper nodes. Because the results from any software for scientific analysis can only be as good as the data used as input, we highlight the importance of considering the results of a DateLife analysis in the context of the input chronograms. DateLife can help to increase awareness of the existing disparities among alternative hypotheses of dates for the same diversification events, and to support exploration of the effect of alternative chronogram hypotheses on downstream analyses, providing a framework for a more informed interpretation of evolutionary results.

中文翻译：

---

DateLife：利用数据库和分析工具揭示过时的生命之树


时序图（分支长度与时间成正比的系统发育）代表了生物研究许多领域的自然过程研究中进化事件时间的关键数据。计时图还提供可用于教育、科学传播和保护政策决策的宝贵信息。然而，实现计时图的高质量重建是一项困难且消耗资源的任务。在这里，我们介绍 DateLife，这是一个以 R 包和 R Shiny Web 应用程序实现的系统发育软件，可在 www.datelife.org 上获取，它提供了高效、轻松地发现、总结、重用和重新分析从精选的节点年龄数据中挖掘的服务。专家、同行评审和公开可用的计时表数据库。主要的 DateLife 工作流程从用户提供的一个或多个科学分类单元名称开始。名称经过处理并标准化为统一的分类法，允许 DateLife 在其本地计时图数据库（从 Open Tree of Life 的系统发育存储库中管理）中运行名称匹配，并提取包含至少两个查询的分类单元名称的所有计时图及其元数据。最后，使用一致算法将来自匹配计时图的节点年龄映射到树拓扑上的相应节点，该树拓扑要么从开放生命之树的合成系统发育中提取，要么由用户提供。一致的节点年龄被用作二次校准，以使用不同的系统发育测年方法（例如 BLADJ、treePL、PATHd8 和 MrBayes）来确定所选拓扑的日期（无论有或没有初始分支长度）。我们进行了交叉验证测试，将 DateLife 分析（即使用二次校准的系统发育测年）得出的节点年龄与原始计时图（即，e，通过初级校准获得），并发现 DateLife 的节点年龄估计与原始计时图的年龄估计一致，最大的年龄变化发生在拓扑较深的节点周围。由于任何科学分析软件的结果只能与用作输入的数据一样好，因此我们强调在输入计时图的背景下考虑 DateLife 分析结果的重要性。 DateLife 可以帮助提高人们对同一多样化事件的日期替代假设之间现有差异的认识，并支持探索替代计时假设对下游分析的影响，为更明智地解释进化结果提供一个框架。