Systematic mapping of the prokaryotic noncoding sequence space with a genomic language model

Sun, Hudelmaier, Gilligan and colleagues report that they used a genomic language model fine-tuned on prokaryotic intergenic sequences to learn representations of noncoding elements from DNA sequences, and organized 376 million intergenic regions from 1.3 terapascal-base-pair prokaryotic genomes and metagenomes into 5.14 million clusters. The paper reports that the method nominated 20,149 clusters that may form structural RNAs and that carry candidate protein partners; periodicity in token entropy revealed internal structure, recovering CRISPR, TIGR and VIPR arrays and their corresponding effector proteins from sequence alone. Targeted clustering of sequences adjacent to bridge recombinases identified bridge RNA variants whose guide recognition sequences are longer than those of any characterized homolog. The study also had a team of AI agents mine databases according to researcher-defined criteria, discovering differentiated riboswitches, ribozymes and other structural RNAs. The authors state that the database has been made public.