Microorganisms are responsible for the conversion and breakdown of organic compounds and contaminants in bioreactors designed for the treatment of different types of waste. Organized in highly complex communities, they are the heart of every wastewater treatment plant and solid residue landfill. The detailed characterization of these communities and their taxonomic classification based on phylogenetic data are of great utility in monitoring the responses of the system to changing operational parameters and the development and optimization of favorable conditions within the bioreactors these communities inhabit. Until recently, only a fraction of the microbial diversity could be assessed, limited by the available sequencing technology, which was not suited for a high-throughput implementation. With the introduction of the recent next-generation sequencing (NGS) methods, an enormous advance has taken place allowing researchers in microbial ecology to generate large amounts of phylogenetic data in a short time and at relatively low costs. In this review, we present and discuss how the increase in available information has influenced recent research and the results available phylogenetic data has produced in the field of wastewater treatment. Furthermore, we introduce the data processing of NGS-based experiments, which has become more complex as the millions of sequences that a single sample can yield require the effective use of computational resources and human bioinformatics skills. To address this part of modern microbial ecology, the most popular sequencing techniques, as well as data analysis workflows, are outlined in this review article.

微生物负责设计用于处理不同类型废物的生物反应器中有机化合物和污染物的转化和分解。它们在高度复杂的社区中组织，是每个废水处理厂和固体残渣填埋场的心脏。这些群落的详细特征及其基于系统发育数据的分类学分类，在监测系统对变化的运行参数的响应以及这些群落所居住的生物反应器内有利条件的发展和优化方面，具有很大的实用性。直到最近，由于可用的测序技术的限制，只能评估微生物多样性的一小部分，该技术不适合高通量实施。随着最近的下一代测序（NGS）方法的引入，微生物生态学领域的研究人员取得了巨大进步，从而使研究人员能够在短时间内以相对较低的成本生成大量的系统发育数据。在这篇综述中，我们介绍并讨论可用信息的增加如何影响最近的研究，以及在废水处理领域产生的可用系统发育数据的结果。此外，我们介绍了基于NGS的实验的数据处理，由于单个样本可以生成数百万个序列需要有效利用计算资源和人类生物信息学技能，因此数据处理变得更加复杂。为了解决现代微生物生态学的这一部分，最流行的测序技术以及数据分析工作流程，