Title: Genie: An Evolutionary Tree Inference and Analyzing Tool
Date/Time: April 11th, 2017 @ 10:00 AM
Place: 223 Atanasoff Hall
Major Professor: Oliver Eulenstein
Committee Members: Samik Basu, Xiaoqiu Huang
Median tree problems are powerful tools for inferring large-scale phylogenetic trees that hold enormous promise for society at large. Such problems seek a median tree for a given collection of input trees under some problem specific distance. Many algorithms have been introduced to address these intrinsically hard problems. However, there is a significant gap between theoretical work and putting that into practice. Tools which are available are not precisely up to date and do not encompass all the existing median tree methods, the number of which is growing rapidly. Furthermore, the latest advancements including state-of-the-art heuristics are not incorporated in any of the existing software packages. Here we introduce Genie, an evolutionary tree inference tool with a well-designed graphical user interface, built to infer evolutionary trees and perform large-scale phylogenetic analyses. We integrate state-of-the-art heuristics to infer species trees under distance-based cost functions such as Robinson-Foulds, path-difference, and evolutionary process based cost functions such as gene duplications, deep coalescence, and duplication-loss. Moreover, Genie provides an interactive interface integrated with tree visualization to enable analysis of the results.