Prediction and analysis of Metagenomic operons via MetaRon: a Pipeline for Prediction of Metagenomic OpeRons

Abstract Background: Efficient regulation of bacterial genes against the environmental stimulus results in unique operonic organizations. Lack of complete reference and functional information makes metagenomic operon prediction challenging and therefore opens new perspectives on the interpretation of the host-microbe interactions. Methods: Here we present MetaRon (pipeline for the prediction of Metagenomic operons), an open-source pipeline explicitly designed for the metagenomic shotgun sequencing data. It recreates the operonic structure without functional information. MetaRon identifies closely packed co-directional gene clusters with a promoter upstream and downstream of the first and last gene, respectively. Promoter prediction marks the transcriptional unit boundary (TUB) of closely packed co-directional gene clusters.Results: Escherichia coli (E. coli) K-12 MG1655 presents a gold standard for operon prediction. Therefore, MetaRon was initially implemented on two simulated illumina datasets: (1) E. coli MG1655 genome (2) a mixture of E. coli MG1655, Mycobacterium tuberculosis H37Rv and Bacillus subtilis str. 168 genomes. Operons were predicted in the single genome and mixture of genomes with a sensitivity of 97.8% and 93.7%, respectively. In the next phase, operons predicted from E. coli c20 draft genome isolated from chicken gut metagenome achieved a sensitivity of 94.1%. Lastly, the application of MetaRon on 145 paired-end gut metagenome samples identified 1,232,407 unique operons. Conclusion: MetaRon removes two notable limitations of existing methods: (1) dependency on functional information, and (2) liberates the users from enormous metagenomic data management. Current study showed the idea of using operons as subset to represent the whole-metagenome in terms of secondary metabolites and demonstrated its effectiveness in explaining the occurrence of a disease condition. This will significantly reduce the hefty whole-metagenome data to a small more precise data set. Furthermore, metabolic pathways from the operonic sequences were identified in association with the occurrence of type 2 diabetes (T2D). Presumably, this is the first organized effort to predict metagenomic operons and perform a detailed analysis in association with a disease, in this case T2D. The application of MetaRon to metagenome data at diverse scale will be beneficial to understand the gene regulation and therapeutic metagenomics.