Coevolutionary mining of prokaryotic non-coding elements with a genome language model
David LiiD, Garyk BrixiiD, Alexandra KimiD, Mateus Bernabe FiamenghiiD, Claudia L. DriscolliD, Simone A. EvansiD, Linyi GaoiD, Natalia IvanovaiD, Nikos C. KyrpidesiD, Karl A. DeisserothiD, Max E. WilkinsoniD, Michael Andrew FischbachiD, Brian HieiD
Abstract
Microbial genomes encode compact molecular machines and diverse non-coding RNAs (ncRNAs) essential to gene regulation, pathogenesis, and many foundational biotechnologies. However, annotation remains largely protein-centric and homology-driven. Here, we introduce Minerva, a framework for coevolutionary mining that uses genome language models to predict local interactions directly from sequence as two-dimensional maps. Introducing two complementary techniques, categorical Jacobian fingerprinting and interaction heads, we demonstrate fast, accurate, alignment-free prediction of ncRNA base-pairing, monomeric protein contacts, and repetitive sequence motifs. Applied to 150 bacterial genomes, Minerva recovers known systems and predicts that 84.3% of predicted intergenic base-pairing falls outside of known annotations. In Pseudomonas, we find that the widespread TwoAYGGAY ncRNA family carries large secondary-structure extensions and is often flanked by short upstream repetitive motifs and larger downstream genomic repeats. Interpreting the coevolution maps, we find that Minerva emergently detects open-reading-frame (ORF) signatures at the DNA level despite never being trained to do so. In prophages within these genomes, we discover that Unknown Group 27 (UG27) reverse transcriptase systems encode arrays of structurally conserved yet sequence-diverse ncRNAs that template complementary DNA (cDNA) hairpin products. Together, these results establish coevolutionary mining as a scalable route to genome annotation and biological discovery across the rapidly expanding microbial universe.
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In-depth summary
Claude Opus 5.5 · xhigh effort · 8 Nov 2026 · from the full text of version 2 (24 Sept 2026) · CC BY 4.0
Minerva extracts RNA base-pairing, protein-contact and repeat maps from a genome language model without alignments, flagging unannotated structured loci and a phage ncRNA array reverse-transcribed into short cDNAs.
Discovery of non-coding elements has leaned on protein-centric homology and on multiple sequence alignments, which are hard to build for divergent structured RNAs. Minerva predicts pairwise interactions from sequence in a single forward pass of a genome language model, with no query or alignment.
Key results
ShownLightweight interaction heads on Minerva-MLM attention beat scalar categorical Jacobian methods in AUPRC on all three benchmarks and run about five orders of magnitude faster, which makes genome-wide scanning practical.
Interaction heads exceed all other methods on more than 84% of tested Rfam families. They take 79 ms per sequence, versus over 1.5 hours for the accelerated mask-based Jacobian and 6 hours for the original. They beat RiNALMo on isolated RNA; ESM-2 beats both genome models on protein contacts. · Minerva accurately predicts pairwise…; Evaluation metrics and model comparisons; Computational efficiency evaluation
ShownFamily-specific fine-tuning sharpens RNA structure predictions at a locus (recovering the DRT2 pseudoknot). Codon periodicity appears in the base model and helps repair mis-tokenized ORFs.
Fine-tuning on 13,034 DRT2 loci recovered the pseudoknot, and the signal appeared with as few as 200 loci. For several mis-tokenized loci, extending the protein to the nearest in-frame start gave full-length ORFs matching RefSeq proteins with 100% identity by BLASTp. · Fine-tuning improves interaction predictions and…; Family-specific fine-tuning of Minerva-MLM; Fine-tuning data-efficiency analysis; Mistokenized ORF analysis
ShownA scan of 150 bacterial genomes predicts tens of thousands of multi-hairpin loci. 69.5% of the 2+ hairpin loci are outside existing annotations (41.1% for the 4+ hairpin loci). In five genomes tested against HMM-generated null genomes, these loci are strongly enriched.
57,787 loci have at least two neighboring hairpins, 40,175 (69.5%) of them unannotated. 9,044 loci have at least four hairpins, 41.1% unannotated. In five randomly chosen genomes versus HMM-generated nulls, the 2+ hairpin loci are enriched 14-fold (FDR 0.07) and the 4+ hairpin loci 132-fold (FDR 0.008). The scan took 100 minutes on one H100 GPU. · Genome-scale coevolutionary mining reveals…; Genome selection, annotation, and scanning; Multi-hairpin enrichment analyses
ShownTwoAYGGAY RNAs in Pseudomonadota carry a larger secondary structure than the Rfam model describes. They occur in highly variable copy number, often as divergent doublets, with flanking short repeats.
The Minerva-extended covariance model found 16,086 matches in 1,148 of 1,324 Pseudomonas strains. Divergent orientation accounted for 68.4% of doublets (2,164 of 3,166), a 2.7-fold enrichment over the 25% expected under independent strand assignment (χ² p < 10^-300). Doublet formation was enriched against an empirical null (p < 10^-4). Mean upstream 5' repeat motif count was 6.18, versus 2.84 in random regions. · Structure extension and repeat association in…; TwoAYGGAY genomic architecture analyses; TwoAYGGAY repeat analyses
ShownUG27 reverse transcriptase systems in prophages encode arrays of sequence-diverse but structurally conserved ncRNA units. In heterologous E. coli expression, the central hairpins of these units are reverse transcribed into short cDNAs, and the accessory proteins were required in the two systems tested.
A covariance model found 82,314 units across 14,118 loci, with lengths concentrated near 149 and 167 nt. All five tested systems gave 50-100-nt ssDNA products mapping to the central hairpins. Mutating the RT active site ablated products. Deleting either accessory ORF, tested in two systems (Blautia hansenii and Bacteroides sp. 2-1-16), abolished cDNA production. · Minerva reveals a variable ncRNA array…; UG27 systems reverse transcribe ncRNA hairpins…; UG27 ncRNA covariance model construction and…
Methods, data and reproducibility
The authors continued pretraining the gLM2 genome language model into Minerva-MLM and extracted pairwise maps with categorical Jacobian fingerprinting and attention-based logistic-regression interaction heads. Predictions were checked on three benchmarks, a 150-genome scan, covariance-model case studies and sequencing of E. coli-expressed UG27 systems.
- Protein monomer-contact benchmark from PDB structures (trRosetta training set subset), prokaryotic proteins only · Bacteria and Archaea · n = 118 proteins (23 train, 95 evaluation)
- Rfam-derived RNA base-pairing benchmark (bacterial families) · Bacteria · n = 680 curated families; 673 in common comparison set (27 train, 646 held-out)
- Repetitive-motif benchmark (CRISPR, type III toxin-antitoxin, REP, SsnA-associated, TIGR-Tas loci) · Bacteria · n = 50 loci (20 train, 30 held-out)
- Continued pretraining corpora (OMG and GlobDB release r226) for Minerva-MLM · n = about a trillion tokens
- Genome scan set: hCom2 gut bacteria, pathogens and select agents, model organisms · Bacteria · n = 150 genomes
- Complete Pseudomonas genomes for TwoAYGGAY analysis (Pseudomonas Genome Database r22.1) · Pseudomonas · n = 1,324 strains
- DRT2 loci for fine-tuning and cryo-EM reference structures · Bacteria · n = 13,034 loci · PDB 9C0I, 9C0J
- UG27 loci from JGI IMG, UHGV, MetaVR and CGVR; five hCom2 UG27 systems expressed in E. coli BL21(DE3) · Bacteriophage/prophage in bacteria; E. coli expression host · n = 14,708 deduplicated loci; 5 systems tested
- Methods & tools
- Minerva-MLM, gLM2, ESM-2, RiNALMo, Infernal, mettannotator, AlphaFold 3, IQ-TREE, MMseqs2, HMMER3, ViennaRNA, UMAP
- Software & environment
- Named versions include mettannotator v1.5.0, Bakta v1.11.4, Nextflow v25.10.4 with Singularity, IQ-TREE v3.1.1, cutadapt v5.2, samtools v1.23.1, pysam v0.24.0, logomaker v0.8.7, InterProScan 5.78-109.0.
- Assay conditions & compute
- Genome scan: 100 minutes on one H100 GPU plus 31 minutes to write maps, 18.3 GB storage. Fine-tuning also compared on a free Colab NVIDIA T4 GPU.
- Reproducibility gaps
- Hugging Face checkpoints (Minerva-MLM, Minerva-MLM-8k) are named but no URL is given. No accession for the sequencing data is given; genome lists and supplementary tables are not in the text.
Limitations (6)
- Minerva-MLM has a short context window, which limits detection of long-range interactions. It predicts protein contacts less accurately than specialized protein models, and RNA-protein interactions remain hard to predict. · stated by the authors
- Coding regions are represented as amino acids, so overlapping nucleotide-level features such as structured RNAs inside coding sequences cannot be modeled at the same time. · stated by the authors
- Fine-tuning strengthened RNA secondary-structure signals but weakened repeat and ORF signals in the DRT2, TwoAYGGAY and UG27 case studies. · stated by the authors
- The benchmarks are built from previously characterized interactions and are biased toward known systems. The authors therefore evaluate only zero-shot scoring and lightweight probes. · stated by the authors
- Experimental validation covers only UG27 cDNA production, in five systems heterologously expressed in E. coli. The biological role of the cDNAs, and the TwoAYGGAY extensions, are supported computationally only. · noted in this summary
- The benchmarks were designed by the authors and the repetitive-motif set is small (50 loci, 30 held-out), so per-class estimates are coarse. · noted in this summary
Who should read:Read this if you mine bacterial or phage genomes for non-coding RNAs and repeats and want an alignment-free, language-model-based way to prioritize loci and seed covariance models.
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