Identifying biosynthetic gene clusters

Abstract

A BGC prediction system identifies candidate biosynthetic gene clusters (BGCs) within genomes using machine-learned models, such as a shallow neural network and recurrent neural network (RNN). A set of domains within a genome sequence are identified, each domain corresponds to a set of domain identifiers. A shallow neural network block is applied to each set of domain identifiers to produce a set of vectors. An RNN block is applied to the set of vectors to produce a BGC class score for each domain. The RNN block was trained using an identified set of positive vectors, which represents known BGCs, and a synthesized set of negative vectors, which is unlikely to represent BGCs. Candidate BGCs are selected by averaging BGC class scores across genes within a domain and comparing the average BGC class scores to a threshold. The candidate BGCs are provided for display on a user interface.