Understanding the regulatory grammar of sepsis-causing Staphylococcus aureus bacteria using contexualised DNA language models.

ABSTRACT: To efficiently and accurately understand, detect, and treat sepsis, we require a deep understanding of how its underlying biological systems work. Conventional methods struggle to provide a detailed view of both the functional and regulatory elements orchestrating a system-wide response to pathogenic infection. Our approach was to use a streamlined process: First, we used multi-omics integration to create a functional-omics signature at the single molecule level, employing sparse Partial Least Squares Discriminant Analysis (sPLSDA). Then, we turned to genome Natural Language Processing (genomeNLP) to identify correlated features and discover regulatory motifs in bacterial promoters. This novel method utilised specialised language models trained on DNA sequences. Our approach is distinct fr

SUBMITTER: Chen T 

PROVIDER: S-EPMC12521479 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature