Machine learning for metagenomics: methods and tools

TL;DR

This paper reviews how machine learning techniques are applied to various metagenomic analysis problems, highlighting successful methods and discussing future challenges in understanding microbial community interactions.

Contribution

It provides a unified overview of machine learning approaches across key metagenomic problems, summarizing current methods and future directions.

Findings

Identifies prominent machine learning methods for OTU-clustering, binning, and taxonomic profiling.

Highlights the success of ML approaches in gene prediction and comparative metagenomics.

Discusses future challenges in analyzing microbial interactions in complex environments.

Abstract

Owing to the complexity and variability of metagenomic studies, modern machine learning approaches have seen increased usage to answer a variety of question encompassing the full range of metagenomic NGS data analysis. We review here the contribution of machine learning techniques for the field of metagenomics, by presenting known successful approaches in a unified framework. This review focuses on five important metagenomic problems: OTU-clustering, binning, taxonomic profling and assignment, comparative metagenomics and gene prediction. For each of these problems, we identify the most prominent methods, summarize the machine learning approaches used and put them into perspective of similar methods. We conclude our review looking further ahead at the challenge posed by the analysis of interactions within microbial communities and different environments, in a field one could call "integrative metagenomics".