The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagenomic studies (10,803 samples). When naively transferred across studies, ML models lost accuracy and disease specificity, which could however be resolved by a novel training set augmentation strategy. This reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de .

Metagenomics has become a prominent approach for exploring the role of the gut microbiota in human health. However, the temporal variability of the healthy gut microbiome has not yet been studied in depth using metagenomics and little is known about the effects of different sampling and preservation approaches. We performed metagenomic analysis on fecal samples from seven subjects collected over a period of up to two years to investigate temporal variability and assess preservation-induced variation, specifically, fresh frozen compared to RNALater. We also monitored short-term disturbances caused by antibiotic treatment and bowel cleansing in one subject.

Gene content differences in human gut microbes can lead to inter-individual phenotypic variations such as digestive capacity. It is unclear whether gene content variation is caused by differences in microbial species composition or by the presence of different strains of the same species; the extent of gene content variation in the latter is unknown. Unlike pan-genome studies of cultivable strains, the use of metagenomic data can provide an unbiased view of structural variation of gut bacterial strains by measuring them in their natural habitats, the gut of each individual in this case, representing native boundaries between gut bacterial populations. We analyzed publicly available metagenomics data from fecal samples to characterize inter-individual variation in gut bacterial species.