Inference of disease-associated microbial gene modules based on metagenomic and metatranscriptomic data

The identification of disease-associated microbial characteristics is crucial for disease diagnosis and therapy. However, the heterogeneity, high dimensionality, and large amounts of microbial data present tremendous challenges for the discovery of key microbial features. In this paper, we present IDAM, a novel computational method for disease-associated gene module inference from metagenomic and metatranscriptomic data. This method integrates gene context conservation (uber-operon) and regulatory mechanisms (gene co-expression patterns) to explore gene modules associated with specific phenotypes using a mathematical graph model, without relying on prior meta-data. We applied IDAM to publicly available datasets from inflammatory bowel disease, melanoma, type 1 diabetes mellitus, and irritable bowel syndrome and demonstrated the superior performance of IDAM in disease-associated characteristics inference compared to popular tools. We also showed high reproducibility of the inferred gene modules of IDAM using independent cohorts with inflammatory bowel disease. We believe that IDAM can be a highly advantageous method for exploring disease-associated microbial characteristics. The source code of IDAM is freely available at https://github.com/OSU-BMBL/IDAM.