Biology subjects

Ju, F.

Publications and source records attributed to Ju, F..

At least 19 recordsLinked to original sources

bioRxiv2026

NeuroPilot: Longitudinal intravital neural imaging across scales and modalities through an agentic platform

Longitudinal intravital imaging is essential for understanding how neural populations evolve across behavioral states, yet converting large-scale recordings into biological insight remains a fundamental challenge because neural signals are degraded by noise, non-rigid motion, and heterogeneous recording conditions. Here we introduce NeuroPilot, an agentic framework that transforms neural imaging analysis into a closed-loop, context-aware process, integrating a multimodal large language model with callable computational imaging modules to adaptively coordinate workflows from raw recordings to population-level readouts. Across heterogeneous imaging and experimental regimes, NeuroPilot restores motion-degraded recordings and supports consistent neural signal recovery, detection, and tracking in challenging deep-brain, freely moving, and longitudinal settings. These enable neuronal population tracking across spinal stimulation paradigms, deep-brain imaging, and continuous 24-hour mPFC recordings aligned with spontaneous sleep-wake behavior. By converting heterogeneous intravital recordings into stable population activity maps, NeuroPilot supports behavioral decoding and reveals structured neural dynamics across behavioral states and transitions. NeuroPilot establishes a scalable agentic paradigm for neural imaging analysis, expanding experimental access to the measurement, visualization, and interpretation of neural population activity.

neuroscience↗

bioRxiv2026

Peri-Locus Coeruleus Controls Endothelial Cell Heterogeneity in Enriched Environments via Region-Specific Norepinephrine Signaling

The brain acts as a central integrator that enables organisms to interpret environmental stimuli and coordinate adaptive responses across the body. While enriched environments are known to enhance neural plasticity and cognitive function, their impact on peripheral organ systems remains less understood. Here, we show that enriched environmental conditions activate a peri-locus coeruleus (peri-LC) neuronal ensemble that preserves CD31hiEMCNhi type H endothelium, a specialized bone-associated endothelial subtype, against age-associated decline, thereby enhancing femoral bone density. Chemogenetic activation and ablation experiments further revealed that this peri-LC ensemble is functionally distinct from other sympathetic regulatory brain regions in its control of bone endothelial heterogeneity and skeletal mass. Three-dimensional bone clearing and spatial analyses showed that CD31hiEMCNhi endothelium is closely associated with sympathetic nerve terminals. The peri-LC mediates this process through sympathetic signaling and {beta}2-adrenergic receptors on bone vascular endothelial cells. In vivo imaging further demonstrated that norepinephrine release is spatially restricted, with sprouting endothelial cells near the growth plate exhibiting the highest sensitivity. Together, these findings reveal a refined brain-periphery regulatory logic in which distinct sympathetic brain circuits engage spatially restricted norepinephrine signaling to coordinate organ-specific vascular and skeletal adaptations.

neuroscience↗

bioRxiv2026

Phenotype-Guided In Silico Molecular Generation Using Large Language Models

Complex diseases often emerge from coordinated, system-level cellular state changes that are difficult to address with target centric drug discovery. Phenotypic drug discovery offers a principled alternative but remains constrained by the cost and scalability of pathologically relevant assays. Here we present GEMGen, a large language model-based framework that performs in silico phenotypic drug discovery by generating small molecules directly from transcriptomic representations of cellular states. GEMGen encodes desired phenotypic transitions as text-based representations of up- and down-regulated gene sets, enabling transferable modeling across experimental platforms and data modalities. Trained on large scale chemical perturbation data, GEMGen robustly identifies phenotype-oriented compounds and mechanistically related but structurally distinct candidates across multiple benchmarks. Applied to signatures induced by genetic perturbations, GEMGen produces small molecules that phenocopy gene knockdown effects and identifies chemically novel inhibitors, including previously unreported KEAP1 inhibitors that activate NRF2 signaling. Extending this approach to a disease relevant model of fibrosis, GEMGen generates compounds that reverse profibrotic transcriptional programs and cellular phenotypes. These results establish a scalable framework for translating transcriptomic phenotypes into candidate therapeutic molecules, enabling systems-level exploration of vast chemical space and offering a complementary in silico counterpart to physical phenotypic drug screens.

cell biology↗

bioRxiv2025

Unraveling Candidatus Dermatophostum as a Novel Genus of Polyphosphate-Accumulating Organisms for High-Strength Wastewater Treatment

Dermatophilaceae polyphosphate-accumulating organisms (PAOs), formerly classified as Tetrasphaera PAOs, play pivotal roles in enhanced biological phosphorus removal (EBPR). However, their phylogenetic diversity, ecological preferences, and metabolic traits remain poorly characterized, and a robust marker gene for their classification is lacking. Here, we performed an extensive phylogenomic and metabolic analysis of Dermatophilaceae PAOs utilizing 46 newly recovered metagenome-assembled genomes (MAGs) from a laboratory-scale EBPR reactor treating high-strength wastewater and full-scale wastewater treatment plants. These analyses revealed a previously uncharacterized PAO genus, named Candidatus Dermatophostum, which is adapted to high-phosphorus environments. Its representative species, Ca. D. ammonifactor, was enriched in the EBPR reactor and its PAO phenotype was confirmed by polyphosphate staining and fluorescence in situ hybridization. Genomic, transcriptomic, and protein structure analyses revealed its specialized metabolic capabilities for phosphate metabolism, glycogen synthesis and dissimilatory nitrate reduction to ammonium. Moreover, Ca. Dermatophostum was found to be widely distributed across WWPTs worldwide, underscoring both its ecological importance and its potential role in mitigating nitrous oxide (N2O) emissions. Finally, we propose a ppk1-based classification framework that resolves Dermatophilaceae PAOs into six distinct clades, consistent with whole-genome phylogeny, and demonstrates that ppk1 can serve as a reliable marker gene for tracking these populations. Together, these findings expand the ecological and functional understanding of Dermatophilaceae PAOs and highlight their promise for advancing sustainable wastewater treatment and resource recovery.

microbiology↗

bioRxiv2025

Expanding the RNA Virus Universe by Scalable Structure-Guided Discovery

The discovery of RNA viruses from metatranscriptomic data remains challenging due to their extreme sequence divergence and frequent lack of conserved motifs. We present Rider, a lightweight two-stage framework that couples fast, structure-informed sequence screening with targeted structural validation. Stage 1 uses a compact 35M-parameter protein language model to prioritize RdRp-like fragments at whole-sample scale, achieving over 44x higher end-to-end screening throughput on commodity hardware. Stage 2 applies structure prediction and Foldseek-based alignment against a dedicated RdRp structure resource ([~]200k ESMFold-predicted structures), providing orthogonal evidence for remote homologs. Applied to >10,000 metatranscriptomes spanning marine, freshwater, soil and host-associated microbiomes, Rider matches or outperforms leading tools (e.g., LucaProt, PalmScan) and additionally recovers divergent and truncated sequences. Multiple orthogonal indicators, including structure consistency and low DNA read mapping to corresponding contigs, support genuine RNA origin. In a human IBD cohort, Rider agrees with state-of-the-art calls for clinically relevant RNA viruses while extending discovery to divergent lineages. Rider turns structure-guided homology search into a practical, scalable pipeline for RNA virome discovery. HighlightO_LIA two-stage framework enables structure-guided RNA virus discovery at sample scale, achieving up to 44-fold higher throughput on standard computing hardware. C_LIO_LIThe method matches or surpasses LucaProt and PalmScan across >10,000 metatranscriptomes from diverse environments, while recovering RdRp fragments missed by existing tools. C_LIO_LIStructural validation using [~]200,000 ESMFold-predicted RdRp models and Foldseek alignment supports the detection of remote homologs with high confidence. C_LIO_LIOrthogonal evidence, including low DNA read mapping, strand-specific expression, and ORF metrics, confirms RNA origin and reduces false positives.. C_LIO_LIOpen-source code and an openly released RdRp structure database enable scalable, reproducible RNA virome discovery in environmental and clinical settings. C_LI

bioinformatics↗

bioRxiv2025

FlexRibbon: Joint Sequence and Structure Pretraining for Protein Modeling

AO_SCPLOWBSTRACTC_SCPLOWProtein foundation models have advanced rapidly, with most approaches falling into two dominant paradigms. Sequence-based language models (e.g., ESM-2) capture sequence semantics at scale, and a number of recent works incorporate structural signals into sequence encoders. MSA-based predictors (e.g., AlphaFold 2/3) achieve accurate folding by exploiting evolutionary couplings, but their reliance on homologous sequences makes them less reliable in highly mutated or alignment-sparse regimes. We present FlexRibbon{ddagger}, a pretrained protein model that jointly learns from amino acid sequences and three-dimensional structures. Our pretraining strategy combines masked language modeling with diffusion-based denoising, enabling bidirectional sequence-structure learning without requiring MSAs. Trained on both experimentally resolved structures and AlphaFold 2 predictions, FlexRibbon captures global folds as well as flexible conformations critical for biological function. Evaluated across diverse tasks spanning interface design, intermolecular interaction prediction, and protein function prediction, FlexRibbon establishes new state-of-the-art performance on 12 different tasks, with particularly strong gains in mutation-rich settings where MSA-based methods often struggle.

molecular biology↗

bioRxiv2025

Grassland Ecological Compensation Policy On Pastoral Production Efficiency--Evidence From Pastoral China

Grassland Ecological Compensation Policy(GECP) in the protection of grassland ecological environment at the same time also promote the transformation and upgrading of animal husbandry, pastoral production efficiency is one of the important indicators to evaluate the sustainable development of animal husbandry, but also has a direct impact on the economic benefits of the herders. Based on field questionnaire data from 468 herding households in the pastoral areas of Inner Mongolia Autonomous Region of China, this study used two-stage semi-parametric DEA model and mediated effect model to measure herding households pastoral production efficiency and analyze the effect and influence mechanism of GECP on herding households pastoral production efficiency, respectively.The results of the study show that: (1) the overall pastoral production efficiency of herding households in Inner Mongolia is low, with a mean value of 0.43, which is mainly caused by low pure technical efficiency. (2) GECP, the number of years of education of the household head, whether or not the head of the household receives technical training, the percentage of income from herding, the number of herding machines, the evaluation of the herding socialized services and whether or not to rent pasture have a significant positive effect on the herding efficiency of herding households. (3) the realization of appropriate scale management by herding households plays a part of the mediating effect in the process of affecting herding households pastoral production efficiency. The realization of moderate scale operation by herders plays a partial intermediary effect in the process of GECP affecting herders pastoral production efficiency, and the direct effect of this policy on the pastoral production efficiency of large-scale herders is significantly larger than that of small-scale herders. Based on the above conclusions, this paper puts forward the suggestions of improving the supporting policies of ecological bonus, demonstrating and popularizing the breeding technology, and guiding the moderate scale management.

ecology↗

bioRxiv2024

Directing cellular transitions on gene graph-enhanced cell state manifold

A select few genes act as pivotal drivers in the process of cell state transitions. However, finding key genes involved in different transitions is challenging. To address this problem, we present CellNavi, a deep learning-based framework designed to predict genes that drive cell state transitions. CellNavi builds a driver gene predictor upon a cell state manifold, which captures the intrinsic features of cells by learning from large-scale, high-dimensional transcriptomics data and integrating gene graphs with directional connections. Our analysis shows that CellNavi can accurately predict driver genes for transitions induced by genetic, chemical, and cytokine perturbations across diverse cell types, conditions, and studies. By leveraging a biologically meaningful cell state manifold, it is proficient in tasks involving critical transitions such as cellular differentiation, disease progression, and drug response. CellNavi represents a substantial advancement in driver gene prediction and cell state manipulation, opening new avenues in disease biology and therapeutic discovery.

cell biology↗

bioRxiv2024

Metabolic Segregation and Characteristic Genes of the Key Functional Populations in Anaerobic Digestion Consortia

Enrichment experiment integrated with genome-centric meta-omics analysis revealed that metabolic specificity, rather than metabolic flexibility, governs the anaerobic digestion (AD) ecosystem. This study provides new insights into interspecies electron transfer in the AD process, highlighting a segregation in the metabolism of H2 and formate. Our findings indicate that H2 serves as the primary electron sink for recycling redox cofactors, NAD and oxidized ferredoxin (Fdox), during primary fermentation. In contrast, formate predominates in the co- occurring secondary fermentation, particularly under conditions of elevated H2 concentrations. Notably, no evidence of biochemical interconversion between H2 and formate was identified in the primary fermenting bacteria or in syntrophs enriched in this study. This segregation of H2 and formate metabolism likely benefit the anaerobic oxidation of butyrate and propionate with a higher tolerance to H2 accumulation. Furthermore, this study underscores the functional partitioning among microbial populations within key niches of the AD bioprocess: primary fermentation, secondary fermentation (syntrophic acetogenesis), hydrogenotrophic methanogenesis, and acetoclastic methanogenesis. Through genome-centric analysis of the AD microbiome, we identified several key functional gene clusters, highlighting the potential for improving genome-centric genotype-phenotype correlations, which is particularly valuable for strict anaerobes that remain challenging to isolate and characterize in pure culture.

genomics↗

bioRxiv2024

Metatranscriptomic Compendium of 55900 RNA Viruses Deciphers Human Health Implication and Ecological roles of RNA Virome in Global Wastewater Treatment Plants

RNA viruses are widely recognized for their roles in causing human diseases and shaping Earths biodiversity. Wastewater treatment plants (WWTPs) are eco-friendly biotechnological systems where the roles of RNA viruses in process engineering and sanitation remain unclear. This study analyzed RNA sequencing dataset (> 3.8 Tb) from global WWTPs to examine the diversity, host associations, and auxiliary metabolic functions of RNA viruses. We identified 11,414 RNA virus operational taxonomic units (vOTUs), expanding the known diversity of RNA viruses in WWTPs by 67%. The RNA viral community in WWTPs was dominated by prokaryotic viruses, including both established RNA phage lineages and novel clades with broad ecological distributions, highlighting their underestimated diversity and broad niche breadths. Notably, a vOTU from the base-Howeltoviricetes phage clade was associated with the pathogenic bacterium Aliarcobacter cryaerophilus, suggesting potential applications in RNA phage therapy. Furthermore, the examined distribution and fate of human RNA viruses emphasized the utility of quantitative metatranscriptomics-based wastewater surveillance for public health monitoring. The discovery of auxiliary metabolic genes encoded by RNA viruses further revealed their involvement in critical host metabolic pathways such as translation and cellular respiration. These findings underscore the multifaceted roles of RNA viruses in the critical engineered systems.

microbiology↗

bioRxiv2024

Comparative Metagenomics Provide Mechanistic Insights into the Biodegradation of the Non-hydrolysable Plastic Polyvinyl Chloride in Gut Microbiota of Insect Larvae

Using microbiomes to mitigate global plastic pollution is of paramount importance. Insect microbiomes have garnered emerging interest for their ability to biodegrade non-hydrolysable plastic polymers. The larvae of Spodoptera frugiperda, a globally prevalent migratory crop pest, are accidentally discovered to consume polyvinyl chloride (PVC) plastic films, highlighting the role of their gut microbiome. Following the migration of S. frugiperd in China, this study displays a comprehensive geographical profile of its larval gut microbiota and a significant shift after PVC feeding. Using comparative metagenomics we revealed the functional redundancy within the larval gut microbiomes of two distinct insects after PVC ingestion, we discovered a surprisingly potent PVC-dechlorinating activity of an NADH peroxidase (6.48 mg/L chlorine produced in 96 hours with NADH as a cofactor) encoded by Enterococcus casseliflavus EMBL-3. These findings open a new avenue for understanding plastic biodegradation mechanism and enable the development of biotechnologies to mitigate global plastic pollution.

microbiology↗

bioRxiv2024

Future Climate Scenarios Aggravate Health Risks of Soil Microbiome by Reshaping Resistome and Pathogenome

How climate change affects soil antibiotic resistome (i.e., the collection of antibiotic resistance genes) is a critical question for environmental and human health. By examining the dynamics of soil resisomes in a six-year (2014-2019) climate change experiment, this study provides explicit insights into the risk of antibiotic resistance in cropland and grassland microbiomes under future climate scenarios. Extreme summers (+2.2{degrees}C during 2018-2019) significantly shifted the resistomic composition, resulting in a prominent increase in abundance of ARGs (copy/cell) conferring resistance to novobiocin (52.7%-72.8%), tetracycline (32.5%-53.0%) and vancomycin (31.5%-62.9%). Importantly, simulated warming (+0.6{degrees}C) significantly increased the proportion of mobilizable ARGs, possibly resulting from the SOS response of soil microbes stimulated by warming-induced drought. In contrast, extreme summers decreased the mobility potential by dramatically filtering the hosts (e.g.,{gamma} -Proteobacteria) of mobilizable ARGs. Climate warming and extreme summers also offer a worrisome competitive advantage for specific soil-dwelling antibiotic-resistant phytopathogens (Clavibacter michiganensis and Rhodococcus fascians) and human pathogens (e.g., Staphylococcus aureus and Mycobacterium tuberculosis), which escalates the risk of outbreaks for specific plant and human infectious diseases. Overall, our findings emphasize the urgent need for continuous monitoring of soil ARGs and pathogens under the on-going global change. Such efforts are crucial for safeguarding human health and ensuring the sustainability of modern agriculture within a global One-Health framework. SynopsisClimate warming increases the mobility potential of soil antibiotic resistance genes, and both climate warming and extreme summers escalate the risk of outbreaks for specific plant and human infectious diseases.

microbiology↗

bioRxiv2023

Accurate RNA velocity estimation based on multibatch network reveals complex lineage in batch scRNA-seq data

RNA Velocity, as an extension of trajectory inference, is an effective method for understanding cell development using single-cell RNA sequencing (scRNA-seq) experiments. Nevertheless, existing RNA velocity methods are limited by the batch effect because they cannot directly correct for batch effects in the input data, which comprises spliced and unspliced matrices in a proportional relationship. This limitation can lead to incorrect velocity graphs. This paper introduces VeloVGI, which addresses this issue innovatively in two key ways. Firstly, it employs an optimal transport (OT) and mutual nearest neighbor (MNN) approach to construct neighbors in batch data. This strategy overcomes the limitations of existing methods that are affected by the batch effect. Secondly, VeloVGI improves upon VeloVIs velocity estimation by incorporating the graph structure into the encoder for more effective feature extraction. The effectiveness of VeloVGI was demonstrated in various scenarios, including the mouse spinal cord and olfactory bulb, as well as on several public datasets. The results showed that VeloVGI outperformed other methods in terms of metric performance. Significance StatementRNA Velocity is an effective method for understanding cell development using single-cell RNA sequencing (scRNA-seq) experiments. This paper introduces VeloVGI, which addresses this batch effect issue for existing RNA velocity methods. The effectiveness of VeloVGI was demonstrated in various scenarios, including the mouse spinal cord and olfactory bulb, as well as on several public datasets. The results showed that VeloVGI outperformed other methods in terms of metric performance.

bioinformatics↗

bioRxiv2023

Accelerating protein engineering with fitness landscape modeling and reinforcement learning

Protein engineering holds significant promise for designing proteins with customized functions, yet the vast landscape of potential mutations versus limited lab capacity constrains the discovery of optimal sequences. To address this, we present the {micro}Protein framework, which accelerates protein engineering by combining {micro}Former, a deep learning model for accurate mutational effect prediction, with {micro}Search, a reinforcement learning algorithm designed to efficiently navigate the protein fitness landscape using {micro}Former as an oracle. {micro}Protein leverages single mutation data to predict optimal sequences with complex, multi-amino acid mutations through its modeling of epistatic interactions and a multi-step search strategy. Except from state-of-the-art performance on benchmark datasets, {micro}Protein identified high-gain-of-function multi-point mutants for the enzyme {beta}-lactamase, surpassing the highest known activity level, in wet-lab, trained solely on single mutation data. These results demonstrate {micro}Proteins capability to discover impactful mutations across vast protein sequence space, offering a robust, efficient approach for protein optimization.

synthetic biology↗

bioRxiv2023

Uninheritable but widespread bacterial symbiont mediates insecticide detoxification of an agricultural invasive pest Spodoptera frugiperda

Identifying insecticide resistance-related insect bacterial symbiont is a vital compass for promoting symbiont-based insecticide resistance management and insecticide-free insect pest control. Known insecticide-resistance-related symbionts lack universality and genetic manipulation, as either not widespread or unculturable. Here, we discovered a widespread symbiont Enterococcus casseliflavus of a significant invasive insect pest, Spodoptera frugiperda. This symbiont enhances host insecticide resistance to chlorantraniliprole by amide bond breaking and dehalogenation-related insecticide degradation, suggesting its great potential for Lepidoptera pest insecticide resistance management. Complying with the increase in exposure risk of chlorantraniliprole, the E. casseliflavus of insects symbionts rather than mammals or environmental strains were notably enriched with putative chlorantraniliprole degradation genes. We also found that E. casseliflavus can transmit horizontally with high efficiency (100%) through cross-diet and cannibalism rather than vertical transmission from mother insect to offspring. Moreover, the widespread infection of E. casseliflavus in the field populations not only implies that an underlying symbiont-host co-evolution process driven by insecticide pressure might be underway, but also provides a novel therapeutic target of agricultural pests based on symbiont-targeted insect control (STIC).

microbiology↗

bioRxiv2023

Highly accurate estimation of cell type abundance in bulk tissues based on single-cell reference and domain adaptive matching

Accurately identifying the cellular composition of complex tissues is critical for understanding disease pathogenesis, early diagnosis, and prevention. However, current methods for deconvoluting bulk RNA sequencing (RNA-seq) typically rely on matched single-cell RNA sequencing (scRNA-seq) as a reference, which can be limiting due to differences in sequencing distribution and the potential for invalid information from single-cell references. To overcome these limitations, we introduced SCROAM, a novel computational method that overcomes these challenges. SCROAM transforms scRNA-seq and bulk RNA-seq into a shared feature space, effectively eliminating distributional differences in the latent space. We then generate cell-type-specific expression matrices from scRNA-seq, enabling accurate identification of cell types in bulk tissues. We evaluated the performance of SCROAM by benchmarking it against simulated datasets and human breast cancer and peripheral blood datasets, demonstrating its accuracy and robustness. To further validate SCROAMs performance, we conducted single-cell and bulk RNA-seq experiments on mouse spinal cord tissue and applied SCROAM to identify bulk tissue cell types. Our results indicate that SCROAM is a highly effective tool for identifying similar cell types, surpassing the performance of existing methods. We then performed an integrated analysis of liver cancer and primary glioblastoma to investigate the relationship between cell type composition and clinical outcomes in various tumor types, highlighting the significance of SCROAM for understanding cellular heterogeneity in complex diseases. Overall, our work presents a novel perspective to accurately infer cellular composition and expression in bulk RNA-seq, offering valuable insights into disease pathogenesis and potential therapeutic strategies.

bioinformatics↗

bioRxiv2023

Modeling cross-cell type cis-regulatory patterns via hierarchical deep neural network and gene expression prediction

Linking cis-regulatory sequences to target genes has been a long-standing challenge. In this study, we introduce CREaTor, an attention-based deep neural network designed to model cis-regulatory patterns for genomic elements up to 2Mb from target genes. Coupled with a training strategy that predicts gene expression from flanking candidate cis-regulatory elements (cCREs), CREaTor can model cell type-specific cis-regulatory patterns in new cell types without prior knowledge of cCRE-gene interactions or additional training. The zero-shot modeling capability, combined with the use of RNA-seq and ChIP-seq data only, allows for the readily generalization of CREaTor to a broad range of cell types. Evaluation reveals that CREaTor outperforms existing methods in capturing cCRE-gene interactions across various distance ranges in held-out cell types. Further analysis indicates that the superior performance of CREaTor can be attributed to its capacity to model regulatory interactions at multiple levels, including the higher-order genome organizations that govern cCRE activities as well as cCRE-gene interactions. Collectively, our findings highlight CREaTor as a powerful tool for systematically investigating cis-regulatory programs across various cell types, both in normal developmental processes and disease-associated contexts.

genetics↗

bioRxiv2022

FunGeneTyper: An Expandable Deep Learning-based Framework for Ultra-accurate Classification and Discovery of Functional Protein-Coding Genes

High-throughput DNA sequencing technologies open the gate to tremendous (meta)genomic data from yet-to-be-explored microbial dark matter. However, accurately assigning protein functions to new gene sequences remains challenging. To this end, we developed FunGeneTyper, an expandable deep learning-based framework with models, structured databases and tools for ultra-accurate (>0.99) and fine-grained classification and discovery of antibiotic resistance genes (ARGs) and virulence factor or toxin genes. Specifically, this new framework achieves superior performance in discovering new ARGs from human gut (accuracy: 0.8512; and F1-score: 0.6948), wastewater (0.7273; 0.6072), and soil (0.8269; 0.5445) samples, beating the state-of-the-art bioinformatics tools and protein sequence-based (F1-score: 0.0556-0.5065) and domain-based (F1-score: 0.2630-0.5224) alignment approaches. We empowered the generalized application of the framework by implementing a lightweight, privacy-preserving and plug-and-play neural network module shareable among global developers and users. The FunGeneTyper* is released to promote the monitoring of key functional genes and discovery of precious enzymatic resources from diverse microbiomes.

bioinformatics↗