Cutting-edge deep-learning based tools for metagenomic research

2025/02/19 by Eli Levy Karin, Martin Steinegger · 1 voice
Biochemistry, Genetics and Molecular Biology · #Gene expression and cancer classification

paper · pdf · doi:10.1093/nsr/nwaf056

Abstract

Recent years have seen incredible progress in the development of deep-learning (DL) tools for the analysis of biological data, with the most prominent example being AlphaFold2 for accurate protein structure prediction. DL-based tools are especially useful for identifying patterns and connections within sparsely labeled datasets. This makes them essential for the analysis of metagenomic data, which is mostly unannotated and bears little sequence similarity to known genes and proteins. In this review, we chose to present 12 tools which we deem as offering novel capabilities for metagenomic analysis by utilizing interesting DL techniques. This review is thus intended to be a solid starting point for any data scientist looking to apply advanced methods to explore metagenomic datasets. For each DL-based tool, we present its computational principles, followed by relevant examples of its application where possible and a note on its limitations.

Citations

• Logan: Planetary-Scale Genome Assembly Surveys Life’s Diversity

Discussions

• In our latest review, we explore 12 deep-learning tools for metagenomic analysis, covering their strengths, limitations, and key applications. We hope it serves as both a resource and inspiration for [bsky, 106 points, 2 comments]

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