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sMap: Evolution of independent, dependent and conditioned discrete characters in a Bayesian framework
Methods in Ecology and Evolution ( IF 6.6 ) Pub Date : 2020-12-13 , DOI: 10.1111/2041-210x.13540
Giorgio Bianchini 1 , Patricia Sánchez‐Baracaldo 1
Affiliation  

  1. Trait evolution analyses enable the comparison of characters amongst different species—a useful technique when inferring ancestral phenotypes based on a phylogeny of living taxa. The evolution of discrete characters can be mapped on the branches of a phylogenetic tree using stochastic mapping. Here we present sMap, a new program to perform stochastic mapping analyses.
  2. Key features characterising sMap are: a wide variety of models and prior distributions; the ability to use a posterior distribution of trees and to compute marginal likelihoods to perform model selection analyses; and the implementation of three kinds of characters: ‘independent’ characters, which do not interact with each other; ‘dependent’ characters, which co‐evolve at the same time; and ‘conditioned’ characters, whose state is determined by the state of other characters.
  3. Here we present two examples that show how sMap can be used to perform stochastic mapping analyses, produce robust results and answer new kinds of questions. sMap is freely available and distributed under a GPL licence, in a command‐line and Graphical User Interface version; a detailed user manual with examples and tutorials is also provided.
  4. The wide variety of algorithms implemented in sMap enables accurate analyses of the evolution of discrete characters. Conditioned characters can be used to study the interaction of simple traits to produce complex phenotypes. As a multiplatform and open‐source project, sMap can be used on a variety of systems and situations, such as academic research and teaching.


中文翻译:

sMap:贝叶斯框架中独立,从属和条件离散字符的演变

  1. 性状进化分析可以比较不同物种之间的特征,这是一种根据活的生物分类系统来推断祖先表型的有用技术。可以使用随机映射将离散字符的演变映射到系统树的分支上。在这里,我们介绍sMap,这是一个执行随机映射分析的新程序。
  2. sMap的主要特征是:多种模型和先验分布;使用树的后验分布并计算边际可能性进行模型选择分析的能力;以及三种字符的实现:相互不交互的“独立”字符;相互依赖的“从属”角色;和“条件”字符,其状态由其他字符的状态决定。
  3. 在这里,我们提供了两个示例,这些示例显示了如何使用sMap来执行随机映射分析,产生可靠的结果并回答新的问题。sMap可通过GPL许可证免费获得并以命令行和图形用户界面版本发布;还提供了包含示例和教程的详细用户手册。
  4. sMap中实现的各种算法可以准确分析离散字符的演变。条件字符可用于研究简单性状之间的相互作用以产生复杂的表型。作为多平台和开源项目,sMap可用于各种系统和情况,例如学术研究和教学。
更新日期:2020-12-13
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