Testing the limits of short-reads metagenomic classifications programs in wastewater treating microbial communities.

IF 3.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Leandro Di Gloria, Lorenzo Casbarra, Tommaso Lotti, Matteo Ramazzotti
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Abstract

Biological wastewater treatment processes, such as activated sludge (AS) and aerobic granular sludge (AGS), have proven to be crucial systems for achieving both efficient waste purification and the recovery of valuable resources like poly-hydroxy-alkanoates. Gaining a deeper understanding of the microbial communities underpinning these technologies would enable their optimization, ultimately reducing costs and increasing efficiency. To support this research, we quantitatively compared classification methods differing in read length (raw reads, contigs and MAGs), overall search approach (Kaiju, Kraken2, RiboFrame and kMetaShot), as well as source databases to assess the classification performances at both the genus and species levels using an in silico-generated mock community designed to provide a simplified yet comprehensive representation of the complex microbial ecosystems found in AS and AGS. Particular attention was given to the misclassification of eukaryotes as bacteria and vice versa, as well as the occurrence of false negatives. Notably, Kaiju emerged as the most accurate classifier at both the genus and species levels, followed by RiboFrame and kMetaShot. However, our findings highlight the substantial risk of misclassification across all classifiers and databases, which could significantly hinder the advancement of these technologies by introducing noises and mistakes for key microbial clades.

测试短读元基因组分类程序在废水处理微生物群落中的局限性。
生物废水处理工艺,如活性污泥(as)和好氧颗粒污泥(AGS),已被证明是实现高效废物净化和回收有价值资源(如聚羟基烷烃酸酯)的关键系统。深入了解支撑这些技术的微生物群落将使其能够优化,最终降低成本并提高效率。为了支持这一研究,我们定量比较了不同读取长度(raw reads, contigs和MAGs)的分类方法,整体搜索方法(Kaiju, Kraken2, RiboFrame和kMetaShot),以及源数据库,以评估属和种水平的分类性能,使用硅片生成的模拟群落,旨在提供as和AGS中复杂微生物生态系统的简化而全面的表示。特别注意真核生物被错误分类为细菌,反之亦然,以及假阴性的发生。值得注意的是,Kaiju在属和种水平上都是最准确的分类器,其次是RiboFrame和kmetshot。然而,我们的研究结果强调了所有分类器和数据库中错误分类的重大风险,这可能会通过引入关键微生物分支的噪声和错误来显著阻碍这些技术的进步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Scientific Reports
Scientific Reports Natural Science Disciplines-
CiteScore
7.50
自引率
4.30%
发文量
19567
审稿时长
3.9 months
期刊介绍: We publish original research from all areas of the natural sciences, psychology, medicine and engineering. You can learn more about what we publish by browsing our specific scientific subject areas below or explore Scientific Reports by browsing all articles and collections. Scientific Reports has a 2-year impact factor: 4.380 (2021), and is the 6th most-cited journal in the world, with more than 540,000 citations in 2020 (Clarivate Analytics, 2021). •Engineering Engineering covers all aspects of engineering, technology, and applied science. It plays a crucial role in the development of technologies to address some of the world''s biggest challenges, helping to save lives and improve the way we live. •Physical sciences Physical sciences are those academic disciplines that aim to uncover the underlying laws of nature — often written in the language of mathematics. It is a collective term for areas of study including astronomy, chemistry, materials science and physics. •Earth and environmental sciences Earth and environmental sciences cover all aspects of Earth and planetary science and broadly encompass solid Earth processes, surface and atmospheric dynamics, Earth system history, climate and climate change, marine and freshwater systems, and ecology. It also considers the interactions between humans and these systems. •Biological sciences Biological sciences encompass all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants. •Health sciences The health sciences study health, disease and healthcare. This field of study aims to develop knowledge, interventions and technology for use in healthcare to improve the treatment of patients.
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