作者: August Guang , Felipe Zapata , Mark Howison , Charles E. Lawrence , Casey W. Dunn
DOI: 10.1016/J.TREE.2015.12.007
关键词: Phylogenetic tree 、 Inference 、 Bioinformatics 、 Biology 、 Generative model 、 Component (UML) 、 Machine learning 、 Tree (data structure) 、 Identification (biology) 、 Molecular phylogenetics 、 Artificial intelligence 、 Phylogenetics
摘要: Molecular phylogenetics is the study of evolutionary relationships between biological sequences, often to infer organisms. These studies require many analysis components, including sequence assembly, identification homologous gene tree inference, and species inference. At present, each component usually treated as a single step in linear analysis, where output passed input next point estimate. Here we outline generative model that helps clarify assumptions are implicit phylogenetic workflows, focusing on assumption low relative entropy. This perspective unifies currently disparate advances, will help investigators evaluate which steps would benefit most from additional computation future methods development.