从分型数据进行基于距离的系统发育推断：一个统一的观点
Distance-based phylogenetic inference from typing data: a unifying view
摘要：
分型方法广泛应用于传染病监测、暴发调查和感染自然史研究。它们的使用正在成为标准，特别是随着高吞吐量测序（HTS）的引入。另一方面，生成的数据是海量的，并且已经提出了许多算法用于分型数据的系统发育分析，解决了正确性和可扩展性问题。大多数基于距离的系统发生树推断算法都遵循最近对连接方案。这是层次聚类中使用的方法之一。虽然系统发育推断算法可能看起来相当不同，但它们之间的主要区别在于如何定义聚类邻近度以及使用哪种优化标准。集群邻近度和优化标准通常都依赖于进化模型。在这项工作中，我们审查，我们提供了一个统一的看法，这些算法。这是一个重要的步骤，不仅可以更好地理解这些算法，而且可以识别可能的计算瓶颈和改进，这对于处理大型数据集很重要。
英文摘要：
Typing methods are widely used in the surveillance of infectious diseases, outbreaks investigation and studies of the natural history of an infection. And their use is becoming standard, in particular with the introduction of High Throughput Sequencing (HTS). On the other hand, the data being generated is massive and many algorithms have been proposed for phylogenetic analysis of typing data, addressing both correctness and scalability issues. Most of the distance-based algorithms for inferring phylogenetic trees follow the closest-pair joining scheme. This is one of the approaches used in hierarchical clustering. And although phylogenetic inference algorithms may seem rather different, the main difference among them resides on how one defines cluster proximity and on which optimization criterion is used. Both cluster proximity and optimization criteria rely often on a model of evolution. In this work we review, and we provide an unified view of these algorithms. This is an important step not only to better understand such algorithms, but also to identify possible computational bottlenecks and improvements, important to deal with large data sets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
