MICROPHERRET: MICRObial PHEnotypic tRait ClassifieR using Machine lEarning Techniques.

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 per

SUBMITTER: Bizzotto E 

PROVIDER: S-EPMC11308548 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature