C-B.16: ANETO: A Host-Agnostic Platform for Microbiome and Multi-Omics Discovery
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Keywords
microbiota, machine learning, multi-omics
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Host-associated microbiomes shape health, productivity and resilience, yet their functional integration with host biology remains challenging, particularly in under-represented agricultural, environmental and aquaculture species. We present ANETO, Aviwell's Discovery Platform, a host-agnostic framework designed to transform metagenomic and multi-omics data into interpretable biological hypotheses and intervention strategies.
ANETO combines standardized bioinformatics workflows, curated microbial genome resources and AI-driven discovery of host–microbiome interactions. Aviwell has generated sequencing data supporting a collection of more than 580,000 microbial genomes, expanding taxonomic resolution for microbiomes from uncommon hosts, including avian and aquatic species. To evaluate metagenomic profiling in poultry-relevant contexts while addressing the limitations of human-centred benchmarks, we built synthetic chicken caecal microbiome datasets. Across multiple community compositions, ANETO outperformed MetaPhlAn and METEOR, achieving F1 median scores at least 10% higher while improving both taxonomic accuracy and quantitative recovery of microbial community structure.
Beyond microbial composition, ANETO integrates molecular and host gene-expression measurements using machine learning-based network inference to model multimodal host–microbiome interactions, including gut–brain-axis-relevant associations. Interactive exploration of these networks enables prioritization of links between phenotypes, microbial communities, molecules and host genes. Resulting associations showed a two-fold enrichment for genes belonging to the same metabolic pathways or functional ontologies compared with random associations, supporting biological coherence.
ANETO provides a scalable framework for converting microbiome and multi-omics data into actionable insights. It has supported applications including feed-conversion improvement, welfare-related traits, pathogen resilience and sustainable aquaculture, while enabling biomarker discovery, postbiotic identification and microbiome-informed intervention design.
Co-authors: Erwann Chinal, Alexandre Fourment, Reda Mekdad, Arnaud Di Franco
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