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Background Metagenomics is gaining attention as a powerful tool for identifying how agricultural management practices influence human and animal health, especially in terms of potential to contribute to the spread of antibiotic resistance. However, the ability to compare the distribution and prevalence of antibiotic resistance genes (ARGs) across multiple studies and environments is currently impossible without a complete re-analysis of published datasets. This challenge must be addressed for metagenomics to realize its potential for helping guide effective policy and practice measures relevant to agricultural ecosystems, for example, identifying critical control points for mitigating the spread of antibiotic resistance. Results Here we introduce AgroSeek, a centralized web-based system that provides computational tools for analysis and comparison of metagenomic data sets tailored specifically to researchers and other users in the agricultural sector interested in tracking and mitigating the spread of ARGs. AgroSeek draws from rich, user-provided metagenomic data and metadata to facilitate analysis, comparison, and prediction in a user-friendly fashion. Further, AgroSeek draws from publicly-contributed data sets to provide a point of comparison and context for data analysis. To incorporate metadata into our analysis and comparison procedures, we provide flexible metadata templates, including user-customized metadata attributes to facilitate data sharing, while maintaining the metadata in a comparable fashion for the broader user community and to support large-scale comparative and predictive analysis. Conclusion AgroSeek provides an easy-to-use tool for environmental metagenomic analysis and comparison, based on both gene annotations and associated metadata, with this initial demonstration focusing on control of antibiotic resistance in agricultural ecosystems. Agroseek creates a space for metagenomic data sharing and collaboration to assist policy makers, stakeholders, and the public in decision-making. AgroSeek is publicly-available at https://agroseek.cs.vt.edu/.

Prokaryotic genomes evolve via horizontal gene transfer (HGT), mutations, and rearrangements. A noteworthy part of the HGT process is facilitated by genomic islands (GIs). While previous computational biology research has focused on developing tools to detect GIs in prokaryotic genomes, there has been little research investigating GI patterns and biological connections across species. We have pursued the novel idea of connecting GIs across prokaryotic and phage genomes via patterns of protein families. Such patterns are sequences of protein families frequently present in the genomes of multiple species. We combined the large data set from the IslandViewer4 database with protein families from Pfam while implementing a comprehensive strategy to identify patterns making use of HMMER, BLAST, and MUSCLE. we also implemented Python programs that link the analysis into a single pipeline. Research results demonstrated that related GIs often exist in species that are evolutionarily unrelated and in multiple bacterial phyla. Analysis of the discovered patterns led to the identification of biological connections among prokaryotes and phages. These connections suggest broad HGT connections across the bacterial kingdom and its associated phages. The discovered patterns and connections could provide the basis for additional analysis on HGT breadth and the patterns in pathogenic GIs.

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