Identifying biosynthetic gene clusters

A non-transitory computer-readable storage medium containing computer program code comprising instructions that, when executed by a processor, causes the processor to:

• identify, in a genome sequence, a set of domains, each identified domain corresponding to a set of domain identifiers;
• apply a shallow neural network block to each set of domain identifiers to produce a set of vectors, each vector corresponding to a set of domain identifiers;
• apply a recurrent neural network (RNN) block to the set of vectors to produce a biosynthetic gene cluster (BGC) class score for each domain, wherein the RNN block was trained by:
• identifying a set of positive vectors representing known BGCs;
• synthesizing a set of negative vectors unlikely to represent BGCs;
• applying the RNN block to the positive and negative sets of vectors to generate predictions of whether each vector is a positive or negative vector;
• and
• updating weights of the RNN block based on the predictions;
• select candidate BGCs by averaging BGC class scores across genes within a domain and comparing the average BGC class scores to a threshold;
• predict a molecular activity of biosynthetic products derived from the selected BGCs;
• and
• provide for display, on a user interface, the candidate BGCs and predicted molecular activity.