Barido-Sottani Joëlle, Bošková Veronika, Plessis Louis Du, Kühnert Denise, Magnus Carsten, Mitov Venelin, Müller Nicola F, PecErska Julija, Rasmussen David A, Zhang Chi, Drummond Alexei J, Heath Tracy A, Pybus Oliver G, Vaughan Timothy G, Stadler Tanja
Department of Biosystems Science and Engineering, ETH Zürich, Mattenstrasse 26, 4058 Basel, Switzerland.
Swiss Institute of Bioinformatics (SIB), Quartier Sorge - Batiment Genopode, 1015 Lausanne, Switzerland.
Syst Biol. 2018 Jan 1;67(1):170-174. doi: 10.1093/sysbio/syx060.
Phylogenetics and phylodynamics are central topics in modern evolutionary biology. Phylogenetic methods reconstruct the evolutionary relationships among organisms, whereas phylodynamic approaches reveal the underlying diversification processes that lead to the observed relationships. These two fields have many practical applications in disciplines as diverse as epidemiology, developmental biology, palaeontology, ecology, and linguistics. The combination of increasingly large genetic data sets and increases in computing power is facilitating the development of more sophisticated phylogenetic and phylodynamic methods. Big data sets allow us to answer complex questions. However, since the required analyses are highly specific to the particular data set and question, a black-box method is not sufficient anymore. Instead, biologists are required to be actively involved with modeling decisions during data analysis. The modular design of the Bayesian phylogenetic software package BEAST 2 enables, and in fact enforces, this involvement. At the same time, the modular design enables computational biology groups to develop new methods at a rapid rate. A thorough understanding of the models and algorithms used by inference software is a critical prerequisite for successful hypothesis formulation and assessment. In particular, there is a need for more readily available resources aimed at helping interested scientists equip themselves with the skills to confidently use cutting-edge phylogenetic analysis software. These resources will also benefit researchers who do not have access to similar courses or training at their home institutions. Here, we introduce the "Taming the Beast" (https://taming-the-beast.github.io/) resource, which was developed as part of a workshop series bearing the same name, to facilitate the usage of the Bayesian phylogenetic software package BEAST 2.
系统发育学和系统动力学是现代进化生物学的核心主题。系统发育方法重建生物体之间的进化关系,而系统动力学方法揭示导致观察到的关系的潜在多样化过程。这两个领域在流行病学、发育生物学、古生物学、生态学和语言学等不同学科中有许多实际应用。越来越大的遗传数据集与计算能力的提升相结合,正在推动更复杂的系统发育和系统动力学方法的发展。大数据集使我们能够回答复杂的问题。然而,由于所需的分析高度特定于特定的数据集和问题,黑箱方法已不再足够。相反,生物学家在数据分析过程中需要积极参与建模决策。贝叶斯系统发育软件包BEAST 2的模块化设计实现并实际上强制了这种参与。同时,模块化设计使计算生物学团队能够快速开发新方法。深入理解推理软件使用的模型和算法是成功提出假设和评估的关键前提。特别是,需要有更多易于获取的资源,以帮助感兴趣的科学家掌握自信使用前沿系统发育分析软件的技能。这些资源也将使那些在其所在机构无法获得类似课程或培训的研究人员受益。在这里,我们介绍“驯服BEAST”(https://taming-the-beast.github.io/)资源,它是作为同名研讨会系列的一部分开发的,以促进贝叶斯系统发育软件包BEAST 2的使用。