Advances and future directions in identifying specific taxa from microbial meta-omics data: from pipeline to deep learning.

Abstract

Molecular profiling enabled by meta-omics technologies has significantly expanded our knowledge of microbial catalog across diverse environments. Increasing attention has now been focused on identifying ecologically significant taxa, particularly keystone that stabilize communities, rare taxa that underpin functional redundancy, and indicators that reflect environmental gradients. However, current pipeline methods remain limited in deciphering complex ecological relationships and modeling the evolution of community dynamics. As a transformative computational tool, deep learning (DL) offers novel strategies to address these challenges through autonomous feature extraction, nonlinear interaction modeling, and integration of multi-modal data sets. Nevertheless, there are still obstacles to the widespread adoption of DL for collaborative identification of specific microbial taxa, primarily including the intrinsic heterogeneity and imbalance of data sets, the difficulty of model generalization across diverse ecosystems, and the limited ecological interpretability of model outputs. This review summarizes existing research advances and proposes to build a unified DL framework for multi-modal data, exploring its implementation pathways, challenges, and potential coping strategies. The envisioned framework establishes a multi-task learning architecture for unified identification of keystone, rare, and indicator taxa, incorporating domain knowledge through ecological constraint layers and explainable AI modules, while providing flexible implementation pathways for heterogeneous data integration and model customization across microbial ecosystems. This framework has the potential to form a closed-loop verification in combination with synthetic microbial community experiments, reshape the paradigm of microbial community research, and promote the transition from empirical classification to mechanistic ecological cognition.