DNA Large Language Models (DNA-LLMs): Leveraging AI and NLP for Genomic Sequence Analysis

Genomic sequencing generates massive, context-rich strings of nucleotides. DNA-LLMs adapt the breakthroughs of language modeling—tokenization, context windows, attention—to capture regulatory grammar and long-range dependencies in DNA. When coupled with transfer learning and multi-task heads, these models enable accurate prediction of regulatory elements, variant effects, and non-coding function.

This workshop translates the theory into practice. You’ll learn data prep (windowing, k-mer tokenization, masking), model usage (inference, fine-tuning), evaluation (precision/recall/F1/AUROC), and interpretation (attribution maps, motif recovery). Hands-on labs use open models/tools to annotate sequences, prioritize variants, and integrate outputs with common pipelines (GATK/VCF).

Equip participants with a working understanding of DNA-LLMs and NLP for genomics—how transformers treat DNA as a language, how tokenization/encoding works for sequences, and where transfer learning adds value. Build practical skills to annotate regulatory elements and assess variant impact using modern pretrained models and notebooks. Connect AI outputs with standard bioinformatics artifacts (FASTA/VCF/GFF) and QC metrics. Prepare attendees to integrate DNA-LLMs into research/clinical pipelines responsibly and reproducibly.

• Explain DNA-LLM fundamentals: tokenization, encoding, transformers, transfer learning.
• Run inference to predict regulatory elements and variant impact on provided sequences.
• Evaluate models with domain-appropriate metrics and perform basic error analysis.
• Integrate outputs with FASTA/VCF/GFF and established tools (e.g., GATK).
• Apply interpretability (saliency/attribution) to generate biologically meaningful insights.

• Undergraduate/postgraduate degree in Microbiology, Biotechnology, Bioinformatics, Computational Biology, Environmental Science, or related fields.
• Professionals in healthcare, pharma, diagnostics, food safety, or environmental sectors.
• Data scientists and AI/ML engineers interested in applying their skills in biological and healthcare domains.
• Individuals with a keen interest in the convergence of life sciences and artificial intelligence.

• Understand DNA-LLM concepts and genomic NLP workflows
• Perform sequence annotation & variant prioritization with pretrained models
• Evaluate results with rigorous metrics; generate interpretable attributions
• Connect AI outputs to clinical/research pipelines (FASTA/VCF/GFF)
• Produce a reproducible notebook and mini-report on findings