Abstract: The present disclosure relates to computer-based methods and systems for identifying non-embedded genes associated with biosynthetic gene clusters (BGCs), including non-embedded target genes (nETaGs) that are homologs of potential therapeutic targets, using comparative genomics techniques. Methods for identifying genes associated with (but not embedded within) biosynthetic gene clusters and applications thereof are described, including predicting the function of secondary metabolites based on the co-occurrence and/or co-evolution of genes encoding for secondary metabolites with biosynthetic gene clusters or their core enzymes, and prediction of biosynthetic gene clusters that produce secondary metabolites having an activity of interest.

Abstract: The present disclosure relates to computer-based methods and systems for identifying genes associated with biosynthetic gene clusters (BGCs), including embedded target genes (ETaGs) that are homologs of potential therapeutic targets, using comparative genomics techniques and machine learning models.

Abstract: The present disclosure relates to computer-implemented methods and systems for identifying biosynthetic gene clusters (BGCs) that encode pathways for the production of secondary metabolites. Secondary metabolites that target genes or gene products that are homologous to, e.g., human genes or gene products may have utility as potential drug compounds.

Abstract: Computational methods for identification of biosynthetic gene clusters (BGCs) that is independent of prior identification of a core synthase are described. The disclosed methods also facilitate linking BGCs to their potential downstream targets. The approach integrates information from co-evolution, co-occurrence, co-regulation, co-localization, and functional enrichment to group functionally-related genes across a gene network, delineate BGCs, and propose targets for the putative secondary metabolites.

Abstract: The present disclosure relates to methods and systems for identifying genes associated with biosynthetic gene clusters (BGCs), including embedded target genes (ETaGs) that are homologs of potential therapeutic targets. The methods and systems described herein apply a comparative genomics and manual review or machine-learning model to analyze grid representations such as heat maps, which assess across a plurality of diverse genomes distribution of orthologs (e.g., bidirectional best hits) of a plurality of query genes that are co-localized with an anchor gene (e.g., a core synthase gene) of a BGC in a query genome.