LAMBDA: a prophage detection benchmark for genomic language models
Year: 2026
Type: article
Abstract: Transformer-based genomic sequence models represent an emerging frontier in computational biology. Yet, their embeddings have not yet shown the same level of predictive power as natural and protein language models, highlighting a gap between current implementations and theoretical promise. Existing benchmarks for DNA language models primarily focus on classifying regulatory elements in eukaryotic genomes, leaving open the fundamental question of whether these models learn sequence-level features across whole genomes. We introduce LAMBDA, a benchmark designed to rigorously evaluate genome language model embeddings through phage-bacteria sequence discrimination across four categories of increasing complexity: probing tasks, fine-tuning assessments, diagnostic tests, and genome-wide prophage detection. Our comprehensive analysis of current genomic language models provides insight into the importance of training data selection relative to model size, the need for domain-specific training, and the capabilities and limitations of genomic language models for detecting prophage sequences. This benchmark represents a challenging genomic annotation task in the bacterial domain and addresses a key computational problem with direct relevance to microbiology and medicine.
Source: NAR Genomics and Bioinformatics
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,766
Funders: National Science Foundation, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, University of Utah, National Centre for Supercomputing Applications, National Institutes of Health, U.S. National Library of Medicine, Office of Advanced Cyberinfrastructure, National Center for Advancing Translational Sciences, National Center for Complementary and Integrative Health, NHLBI Division of Intramural Research
Awards: G1353381076, G1453181110, G1503004319, Track 4: Advanced CI Coordination Ecosystem: Monitoring and Measurement Services, G1919374385, G2274303172, G298330452, G34258105, G3562386289, Track 1: ACCESS Resource Allocations Marketplace and Platform Services (RAMPS), G4010157413, G4275568664, URoL:EN: Understanding the rule of life facilitating the proliferation of toxic cyanobacterial benthic mats in flowing freshwaters, G4403015908, ACO: An Open CI Ecosystem to Advance Scientific Discovery (OpenCI), Track 3: COre National Ecosystem for CyberinfrasTructure (CONECT), G5156331529, G5367127336, G5464919783, Antibody targeting of the virome, G7526614437, G8108599699, Track 2: Customized Multi-tier Assistance, Training, and Computational Help (MATCH) for End User ACCESS to CI, G8499740153, G9000173977
OpenAlex ID: W7211940468