Scalable Algorithms for Reconstruction of Plant Phylogenies in Conjunction with the NSF (National Science Foundation) iPlant Collaborative

进化关系的知识是生物学的基础，在植物科学中产生了新的见解，从比较基因组学、分子进化到植物发展、适应、物种形成、群落组装和生态系统功能的研究。尽管我们对绿色植物发展史的了解已经大大扩展，但为50万种绿色植物构建一棵全面的生命之树的任务仍然艰巨。我们在分析和合成用于组装树的数据时面临着主要障碍。这提出了一个巨大的挑战，这需要扩大系统发育生物学和计算机科学之间的发展交叉点。在NSF iFactory合作项目中，我们建议将植物和计算机科学家聚集在一起，专注于汇集所有绿色植物系统发育的知识，推断出比现有树木大100倍的全面系统发育，并将植物生命树与其他植物学相结合。TU München的工作与植物生命树一起，将专注于开发新的算法和并行策略，以推断至少由500,000个分类群组成的单基因植物树。可扩展的树重建方法是建议的NSF协作成功的关键组成部分。

Knowledge of evolutionary relationships is fundamental to biology, yielding new insights across the plant sciences, from comparative genomics, molecular evolution, to plant development, the study of adaptation, speciation, community assembly, and ecosystem functioning. Although our knowledge of the phylogeny of green plants has expanded dramatically, the task of assembling a comprehensive tree of life for the half million species of green plants remains daunting. Major barriers confront us in analyzing and synthesizing the data for assembling the tree. This presents a Grand Challenge, which requires expansion of a developing intersection between phylogenetic biology and the computer sciences.Within the NSF iPlant collaborative project we propose to bring plant and computer scientists together to focus on the grand challenge of assembling all knowledge of the phylogeny of green plants and infer a comprehensive phylogeny 100 times larger than existing trees and to integrate the plant tree of life with the rest of botanical sciences. The work at TU München in conjunction with the plant tree of life will focus on developing new algorithms and parallelization strategies to infer single-gene plant trees consisting of at least 500,000 taxa. Scalable methods for tree reconstruction represent a key component for the success of the proposed NSF collaborative.