.png){kind=icon}
Return
LAMBDA: a prophage detection benchmark for genomic language models
.png){kind=icon}
OA
AI
DOI：10.1093/nargab/lqag103.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Journal
N
IF:
2.8
Papers:
264
Citations:
0
Organization
Cited Papers
VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses.png)
MICROBIOME
OAAI
MICROBIOMEIF12.7
OAAIShareSaveVirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic dataMICROBIOMEOAAI
MICROBIOMEIF12.7
OAAIShareSaveDEPhT: a novel approach for efficient prophage discovery and precise extractionNUCLEIC ACIDS RESEARCHOAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAIShareSaveTaxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networksNATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAIShareSaveVIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequencesMICROBIOMEOAAI
MICROBIOMEIF12.7
OAAIShareSaveShareSaveShareSave
View more