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Metagenomics is a culture-independent method for studying the microbes inhabiting a particular environment. Comparing the composition of samples (functionally/taxonomically), either from a longitudinal study or cross-sectional studies, can provide clues into how the microbiota has adapted to the environment. However, a recurring challenge, especially when comparing results between independent studies, is that key metadata about the sample and molecular methods used to extract and sequence the genetic material are often missing from sequence records, making it difficult to account for confounding factors. Nevertheless, these missing metadata may be found in the narrative of publications describing the research. Here, we describe a machine learning framework that automatically extracts essential metadata for a wide range of metagenomics studies from the literature contained in Europe PMC. This framework has enabled the extraction of metadata from 114,099 publications in Europe PMC, including 19,900 publications describing metagenomics studies in European Nucleotide Archive (ENA) and MGnify. Using this framework, a new metagenomics annotations pipeline was developed and integrated into Europe PMC to regularly enrich up-to-date ENA and MGnify metagenomics studies with metadata extracted from research articles. These metadata are now available for researchers to explore and retrieve in the MGnify and Europe PMC websites, as well as Europe PMC annotations API. [ABSTRACT FROM AUTHOR]

The HoloFood project used a hologenomic approach to understand the impact of host-microbiota interactions on salmon and chicken production by analysing multiomic data, phenotypic characteristics, and associated metadata in response to novel feeds. The project's raw data, derived analyses, and metadata are deposited in public, open archives (BioSamples, European Nucleotide Archive, MetaboLights, and MGnify), so making use of these diverse data types may require access to multiple resources. This is especially complex where analysis pipelines produce derived outputs such as functional profiles or genome catalogues. The HoloFood Data Portal is a web resource that simplifies access to the project datasets. For example, users can conveniently access multiomic datasets derived from the same individual or retrieve host phenotypic data with a linked gut microbiome sample. Project-specific metagenome-assembled genome and viral catalogues are also provided, linking to broader datasets in MGnify. The portal stores only data necessary to provide these relationships, with possible linking to the underlying repositories. The portal showcases a model approach for how future multiomics datasets can be made available. Database URL:  https://www.holofooddata.org., (© The Author(s) 2025. Published by Oxford University Press.)