Data from: Triplet MaxCut: a new toolkit for rooted supertree

The rapid increase of molecular, as well as other types, of available classification data has created the need to combine this data into a unified hypothesis.
Supertree methods are essential when amalgamating phylogenetic information from various, possibly conflicting, sources into a single tree. The goal of a supertree algorithm is to satisfy maximally each such source of information in the output tree.
Triplets, rooted trees over three leaves, are the minimal piece of such information when dealing with rooted trees. Due to its fundamental role in phylogenetics, extensive effort has been dedicated to several aspects regarding triplets’ research.
We have devised a new tool, Triplet MaxCut (TMC), performing various operations in rooted supertree, principally amalgamating rooted trees based on amalgamating rooted triplets. The utility and efficiency of the algorithm is demonstrated by both simulation study and four real data supertree inputs.

随着可获取的分子分类数据及其他类型分类数据的快速增长，将此类数据整合为统一假说的需求应运而生。
超树方法（Supertree Methods）在将来自多个可能存在冲突的信息源的系统发育信息整合为单棵系统发育树的过程中不可或缺，超树算法的目标是在输出的系统发育树中最大限度地契合每一个信息源的内容。
三联体（Triplets），即三叶有根树，是处理有根树时这类系统发育信息的最小基本单元。鉴于三联体在系统发育学中的基础性作用，学界已针对三联体研究的多个方面投入了大量研究精力。
我们研发了一款全新工具——三联体最大割（Triplet MaxCut，TMC），可在有根超树场景下执行多种操作，核心功能为基于整合有根三联体实现有根树的合并。
本算法的实用性与效率，通过模拟实验与四组真实数据的超树整合任务得到了验证。