Gloeckner, F. O. | bioRxiv Science

bioRxiv2021

AGNOSTOS-DB: a resource to unlock the uncharted regions of the coding sequence space

Genomes and metagenomes contain a considerable percentage of genes of unknown function, which are often excluded from downstream analyses limiting our understanding of the studied biological systems. To address this challenge, we developed AGNOSTOS, a combined database-computational workflow resource that unifies the known and unknown coding sequence space of genomes and metagenomes. Here, we present AGNOSTOS-DB, an extensive database of high-quality gene clusters enriched with functional, ecological and phylogenetic information. Moreover, AGNOSTOS allows integrating new data into existing AGNOSTOS-DBs, maximizing the information retrievable for the genes of unknown function. As a proof of concept, we provide a seed database that integrates the predicted genes from marine and human metagenomes, as well as from Bacteria, Archaea, Eukarya and giant viruses environmental and cultivar genomes. The seed database comprises 6,572,081 gene clusters connecting 342 million genes and represents a comprehensive and scalable resource for the inclusion and exploration of the unknown fraction of genomes and metagenomes.

microbiology↗

bioRxiv2021

Mining metagenomes for natural product biosynthetic gene clusters: unlocking new potential with ultrafast techniques

Microorganisms produce an immense variety of natural products through the expression of Biosynthetic Gene Clusters (BGCs): physically clustered genes that encode the enzymes of a specialized metabolic pathway. These natural products cover a wide range of chemical classes (e.g., aminoglycosides, lantibiotics, nonribosomal peptides, oligosaccharides, polyketides, terpenes) that are highly valuable for industrial and medical applications1. Metagenomics, as a culture-independent approach, has greatly enhanced our ability to survey the functional potential of microorganisms and is growing in popularity for the mining of BGCs. However, to effectively exploit metagenomic data to this end, it will be crucial to more efficiently identify these genomic elements in highly complex and ever-increasing volumes of data2. Here, we address this challenge by developing the ultrafast Biosynthetic Gene cluster MEtagenomic eXploration toolbox (BiG-MEx). BiG-MEx rapidly identifies a broad range of BGC protein domains, assess their diversity and novelty, and predicts the abundance profile of natural product BGC classes in metagenomic data. We show the advantages of BiG-MEx compared to standard BGC-mining approaches, and use it to explore the BGC domain and class composition of samples in the TARA Oceans3 and Human Microbiome Project datasets4. In these analyses, we demonstrate BiG-MExs applicability to study the distribution, diversity, and ecological roles of BGCs in metagenomic data, and guide the exploration of natural products with clinical applications.

bioinformatics↗