Recent advances in deep learning and language models for studying the microbiome

TL;DR

This paper reviews how recent deep learning and language models, especially large language models, are transforming microbiome research by analyzing complex microbial sequences and ecological data.

Contribution

It provides a comprehensive overview of the application of deep learning and language modeling techniques in microbiome and metagenomics studies, highlighting recent advances and challenges.

Findings

Language models enable new insights into microbial sequences.

Deep learning improves prediction of biosynthetic gene clusters.

Language modeling facilitates integration of microbiome knowledge.

Abstract

Recent advancements in deep learning, particularly large language models (LLMs), made a significant impact on how researchers study microbiome and metagenomics data. Microbial protein and genomic sequences, like natural languages, form a language of life, enabling the adoption of LLMs to extract useful insights from complex microbial ecologies. In this paper, we review applications of deep learning and language models in analyzing microbiome and metagenomics data. We focus on problem formulations, necessary datasets, and the integration of language modeling techniques. We provide an extensive overview of protein/genomic language modeling and their contributions to microbiome studies. We also discuss applications such as novel viromics language modeling, biosynthetic gene cluster prediction, and knowledge integration for metagenomics studies.