Corpusome, a cross-body-site human microbiome corpus for representation learning

Corpusome, a new human microbiome database, was recently made available for machine-learning research. This resource addresses the challenge of limited cross-body-site representation and cross-study generalization in existing models of the human microbiome. The problem stems from a lack of a harmonized multi-body-site corpus that incorporates technical metadata necessary for effective modeling.

Corpusome aims to overcome these limitations by releasing a harmonized two-tier cross-body-site human microbiome corpus. This collection contains 187,546 samples, integrating standardized profiles from curatedMetagenomicData, the American Gut Project, and the EBI MGnify platform. The database follows a two-tier design that maintains both functional depth and cross-body-site breadth.

The shotgun tier of Corpusome consists of 22,588 samples from 93 studies, providing species- and pathway-level profiles. Meanwhile, the 16S tier includes 164,958 samples from the complete pull of 708 MGnify studies, offering genus-level profiles that extend coverage to oral, skin, respiratory, and urogenital sites. The database spans six body sites and two modalities, with harmonized metadata designed for batch-aware modeling.

One of the key strengths of Corpusome is its ability to highlight body-site signal that exceeds technical/source variance. In the 16S tier, this improvement is approximately 2.4-fold. This enhanced representation of body-site-specific information is crucial for developing accurate and generalizable machine-learning models of the human microbiome.