MICROPHERRET: MICRObial PHEnotypic tRait ClassifieR using Machine lEarning Techniques - 86 models for functional classification of microbial genomes

Aug 3, 2026

MICROPHERRET addresses the bottleneck in functionally characterizing newly reconstructed microbial genomes, where experimental assays lag far behind rapid growth in shotgun-derived MAGs and SAGs. The approach applies supervised machine learning to establish 86 metabolic and ecological functional classes from widely available genome annotations. Validation on independent datasets shows robust performance across complete, fragmented, and incomplete genomes with >70% completeness for most functions. The tool generalizes to metagenomic and single-cell datasets and is demonstrated via applications to biogas microbiome predictions and a refined acetoclastic methanogenesis case.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages20

Reading Time50 min

AccessFree to download

Abstract

Background

Results

Application

Conclusions

Background

Genome-centric metagenomics and MAGs

Ecological relevance of functional roles

Growing datasets and reference initiatives

Challenges in manual functional annotation

Bizzotto et al. Environmental Microbiome (2024) 19:58 Environmental Microbiome [https://doi.org/10.1186/s40793-024-00600-6](https://doi.org/10.1186/s40793-024-00600-6) RESEARCH Open Access MICROPHERRET: MICRObial PHEnotypic tRait ClassifieR using Machine lEarning Techniques Edoardo Bizzotto 1†, Sofia Fraulini 1†, Guido Zampieri 1*, Esteban Orellana 1, Laura Treu 1 and Stefano Campanaro 1* Abstract 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. Keywords Functional classification, Machine learning, Microbial genome, Metagenome, Methanogenesis †Edoardo Bizzotto and Sofia Fraulini contributed equally to this work. *Correspondence: Guido Zampieri [guido.zampieri@unipd.it](guido.zampieri@unipd.it)[ ](guido.zampieri@unipd.it)Stefano Campanaro [stefano.campanaro@unipd.it](stefano.campanaro@unipd.it) 1Department of Biology, University of Padova, Padova 35131, Italy Background Genome-centric metagenomics allows the reconstruction of draft genomes of single microorganisms called metagenome-assembled genomes (MAGs) [1]. Analysis of diverse MAGs from both phylogenetic and functional perspectives enables a comprehensive exploration of the taxonomic composition and functional processes within the sampled microbial community [2, 3]. From an ecological standpoint, metagenomics studies offer invaluable insights into ecosystem functions and responses to environmental dynamics [4]. Associations of microorganisms to specific functional roles are pivotal for © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

FAQs

What applications are demonstrated in the study?

The algorithms are applied to the Biogas Microbiome database with predictions consistent with biological knowledge, and a case study on acetoclastic methanogenesis shows model refinement or expansion for novel functions of interest.

Aug 3, 2026

Upload by Theodora