AmpliconZilla and EMM-GNN, A framework for gaining ecological insights from microbiome studies скачать в хорошем качестве

AmpliconZilla and EMM-GNN, A framework for gaining ecological insights from microbiome studies

The AmpliconZilla is a meta-analysis pipeline written to facilitate analyses of short read amplicons from public repositories. The Elastic Microbial Manifold Graph Neural Network (EMM-GNN) pipeline represents a transformative shift from descriptive taxonomics to structural microbial mechanics, prioritizing the topological stability of ecosystems over simple differential abundance. By treating taxa as neural nodes and biological interactions as a sparse weight matrix derived from the Graph Laplacian, the pipeline utilizes a Physically-Constrained Spectral GNN to extract the Systemic Rigidity (λ2​)of the microbial manifold. This global metric provides a definitive readout of ecosystem resilience, which is further refined through a Generative Perturbation Audit. This audit replaces stochastic backpropagation with an in-silicodropout process to calculate the Structural Elasticity (Ev​) of individual taxa, distinguishing "Reinforcing Anchors" from opportunistic members based on their necessity to the manifold's stability. The analytical framework extends into a dynamic "Risk Map" by tracking Manifold Velocity (Vi​), a Euclidean trajectory of taxonomic migration, and calculating the Spectral Gap (λ3​−λ2​) to prove functional modularity. To assess the risk of systemic collapse, the pipeline integrates Expansion Cheeger Bounds, which identify connectivity bottlenecks, and the Master Stability Sync Ratio, which defines the coupling strength required for the community to maintain biological synchrony. Ultimately, this comprehensive audit distinguishes between universal drivers and context-dependent members, allowing researchers to pinpoint the "Edge of Chaos" where dysbiotic shifts, such as high fluoride exposure or metabolic stress, trigger fragmented manifold collapse.