Identifying Sequences for Microbial Communities Using Long -mer Sequence Signatures.

Comparing metagenomic samples is crucial for understanding microbial communities. For different groups of microbial communities, such as human gut metagenomic samples from patients with a certain disease and healthy controls, identifying sequences offers essential information for potential biomarker discovery. A sequence that is present, or rich, in one group, but absent, or scarce, in another group is considered "" in our study. Our main purpose is to discover sequence regions between control and case groups as disease-associated markers. We developed a long -mer ( ≥ 30 bps)-based computational pipeline to detect sequences at strain resolution free from reference sequences, sequence alignments, and metagenome-wide assembly. We called our method MetaGO: oligonucleotide analysis for metagenomic samples. An open-source pipeline on was developed with parallel computing. We applied MetaGO to one simulated and three real metagenomic datasets to evaluate the discriminative capability of identified markers. In the simulated dataset, 99.11% of logical -mers covered 98.89% regions from the disease-associated strain. In addition, 97.90% of numerical -mers covered 99.61 and 96.39% of differentially abundant genome and regions between two groups, respectively. For a large-scale metagenomic liver cirrhosis (LC)-associated dataset, we identified 37,647 mer features. Any one of the features can predict disease status of the training samples with the average of sensitivity and specificity higher than 0.8. The random forests classification using the top 10 features yielded a higher AUC (from ∼0.8 to ∼0.9) than that of previous studies. All mers were present in LC patients, but not healthy controls. All the assembled 11 sequences can be mapped to two strains of : UTDB1-3 and DSM2008. The experiments on the other two real datasets related to Inflammatory Bowel Disease and Type 2 Diabetes in Women consistently demonstrated that MetaGO achieved better prediction accuracy with fewer features compared to previous studies. The experiments showed that MetaGO is a powerful tool for identifying -mers, which would be clinically applicable for disease prediction. MetaGO is available at https://github.com/VVsmileyx/MetaGO.