MICROPHERRET:利用机器学习技术进行微生病理信道分类。

IF 6.2 2区 环境科学与生态学 Q1 GENETICS & HEREDITY
Edoardo Bizzotto, Sofia Fraulini, Guido Zampieri, Esteban Orellana, Laura Treu, Stefano Campanaro
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引用次数: 0

摘要

背景:近年来,通过霰弹枪测序重建的微生物基因组数量迅速增加,新开发的方法包括元基因组分选和单细胞测序。然而,我们通过实验测定法对这些基因组进行功能表征的能力却低了几个数量级。因此,我们迫切需要开发快速、自动化的微生物基因组功能分类策略:本研究利用一套有监督的机器学习算法,从广泛获得的微生物基因组注释出发,建立了一系列 86 种代谢功能和其他生态功能,如甲烷营养和塑料降解。在独立数据集上进行的测试表明,对于大多数考虑的功能,该算法在完整、片段和不完整基因组中的表现都很稳健,完整度超过 70%。在沼气微生物组数据库中应用该算法得出的预测结果与当前的生物学知识基本一致,并能正确检测出古细菌基因组中与功能相关的细微差别。最后,以乙酰甲烷生成为重点的案例研究表明,所开发的机器学习模型可以通过描述感兴趣的新功能的模型进行完善或扩展:由此产生的工具 MICROPHERRET 共包含 86 个模型,每个测试的功能类别都有一个模型,可应用于高质量微生物基因组以及从元基因组学和单细胞测序中获得的低质量基因组。因此,MICROPHERRET 有助于了解新生成的基因组在其微生态环境中的功能作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MICROPHERRET: MICRObial PHEnotypic tRait ClassifieR using Machine lEarning Techniques.

Background: In recent years, there has been a rapid increase in the number of microbial genomes reconstructed through shotgun sequencing, and obtained by newly developed approaches including metagenomic binning and single-cell sequencing. However, our ability to functionally characterize these genomes by experimental assays is orders of magnitude less efficient. Consequently, there is a pressing need for the development of swift and automated strategies for the functional classification of microbial genomes.

Results: The present work leverages a suite of supervised machine learning algorithms to establish a range of 86 metabolic and other ecological functions, such as methanotrophy and plastic degradation, starting from widely obtainable microbial genome annotations. Tests performed on independent datasets demonstrated robust performance across complete, fragmented, and incomplete genomes above a 70% completeness level for most of the considered functions. Application of the algorithms to the Biogas Microbiome database yielded predictions broadly consistent with current biological knowledge and correctly detecting functionally-related nuances of archaeal genomes. Finally, a case study focused on acetoclastic methanogenesis demonstrated how the developed machine learning models can be refined or expanded with models describing novel functions of interest.

Conclusions: The resulting tool, MICROPHERRET, incorporates a total of 86 models, one for each tested functional class, and can be applied to high-quality microbial genomes as well as to low-quality genomes derived from metagenomics and single-cell sequencing. MICROPHERRET can thus aid in understanding the functional role of newly generated genomes within their micro-ecological context.

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来源期刊
Environmental Microbiome
Environmental Microbiome Immunology and Microbiology-Microbiology
CiteScore
7.40
自引率
2.50%
发文量
55
审稿时长
13 weeks
期刊介绍: Microorganisms, omnipresent across Earth's diverse environments, play a crucial role in adapting to external changes, influencing Earth's systems and cycles, and contributing significantly to agricultural practices. Through applied microbiology, they offer solutions to various everyday needs. Environmental Microbiome recognizes the universal presence and significance of microorganisms, inviting submissions that explore the diverse facets of environmental and applied microbiological research.
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