DNA Large Language Models (DNA-LLMs): Leveraging AI and NLP for Genomic Sequence Analysis
Treat DNA like language—unlock function with AI.
Skills you will gain:
About Program:
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).
Aim: 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.
Program Objectives:
- 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.
What you will learn?
Day 1: DNA-LLMs & NLP in Genomics
- What DNA-LLMs are, how they work; NLP in genomics; recent advances (GENA-LM, Caduceus, DeepBind).
- DNA as “language”: tokenization/sequence encoding; transformer backbone; transfer learning & pretrained models.
- Applications: predicting regulatory elements; functional prediction of variants (pathogenicity); key tools & databases (GENA-LM, Caduceus, DeepBind).
- Hands-on: run GENA-LM on a sequence to predict regulatory regions; notebook demo on a small genomic dataset.
- Outcomes: grasp DNA-LLM/NLP principles for genomic data + practical experience with sequence analysis.
Day 2: Advanced Uses & Implementation
- Applications: infer gene regulatory networks; classify variant impact (benign/pathogenic/VUS); integrate with bioinformatics pipelines (GATK, VCF).
- AI-based annotation: non-coding genome annotation with GENA-LM; variant impact prediction via Caduceus/other DL models; real cases (cancer, neurodegeneration).
- Implementation: preprocessing & tokenizing sequences; training DNA-LLMs on custom datasets; evaluation metrics (precision, recall, F1).
- Hands-on: use GENA-LM/Caduceus to score variants in a provided dataset; analyze/visualize outputs (variant classes, regulatory region calls).
- Wrap-up: future of DNA-LLMs for complex traits/diseases + ethical considerations in AI/genetics.
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- 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.
Career Supporting Skills
Program Outcomes
- 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