The Marine Fish Database (MFDB) is developed, the first integrative genomics platform specifically designed for marine teleosts, and establishes a scalable framework to accelerate hypothesis-driven discoveries in marine fish biology, evolutionary adaptation, and ecological resilience research.
Abstract
Marine fishes are important contributors to biodiversity conservation and socioeconomic sustainability. While genomic and multi-omics datasets for marine fish species have expanded exponentially in recent years, their systematic integration remains underexplored, hindering comprehensive investigations into gene regulation and biological systems. To bridge this gap, we developed the Marine Fish Database (MFDB; http://marinefishdb.cn), the first integrative genomics platform specifically designed for marine teleosts. MFDB compiles fragmented multi-omics resources across 89 species, encompassing genome assemblies, phylogenomic reconstructions, collinearity maps, pan gene sets, gene architectures, functional annotations, expression profiles, and evolutionary gene family analyses. MFDB further delivers specialized analytical modules, including tissue-specific gene co-expression networks, lineage-defining core gene repertoires, and macrosynteny-driven chromosomal evolution models. The platform features an intuitive and programmable interface, enabling multiscale queries, cross-omics data mining, and dynamic visualization of multidimensional biological interactions. These functionalities collectively empower users to decipher how genomic elements orchestrate phenotypic outcomes through multi-layered regulatory cascades. By integrating rapidly expanding omics datasets with robust analytical pipelines, MFDB establishes a scalable framework to accelerate hypothesis-driven discoveries in marine fish biology, evolutionary adaptation, and ecological resilience research, and further provides an easy-to-operate information platform for the molecular breeding of marine fish species.
This work provides a critical evaluation of the functional gaps between genomic potential and in situ microbial activity and offers a novel synthesis of how multiomics integration and predictive modeling can move beyond species cataloging toward a more robust, evidence‐based framework for environmental sustainability.
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