DNA Large Language Models (DNA-LLMs): Leveraging AI and NLP for Genomic Sequence Analysis
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Course Overview
Program Syllabus
Module 1
About This Course
Genomic sequencing generates massive, context-rich strings of nucleotides. DNA-LLMs adapt the breakthroughs of language modelingātokenization, context windows, attentionāto 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. Youāll 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).
Module 2
Aim
Equip participants with a working understanding of DNA-LLMs and NLP for genomicsāhow 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.
Module 3
Workshop 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.
Module 4
Workshop Structure
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 ālanguageā: 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.
Module 5
Who Should Enrol?
- 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.
Module 6
Important Dates
Module 7
Registration Ends
12/02/2025
IST 7:00 PM
Module 8
Workshop Dates
12/02/2025 ā 12/03/2025
IST 8:00 PM
Module 9
Workshop 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
Module 10
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Meet Your Mentor(s)
Prof. Kumud Malhotra
Professor & Dean
Prof. Kumud Malhotra, Dean of the University Institute of Physical and Life Sciences with 30 years of experience is an academician and administrator and has attained the highest echelons in the educational sector by managing senior positions, like Director, Dean, Managing Editor, or Editor-in-Chief . . .
Module 11
Fee Structure
Module 12
Student Fee
ā¹1499 | $55
Module 13
Ph.D. Scholar / Researcher Fee
ā¹2499 | $65
Module 14
Academician / Faculty Fee
ā¹3499 | $80
Module 15
Industry Professional Fee
ā¹4499 | $90
Module 16
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Module 17
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Module 18
Publication Opportunity
Get published in a prestigious open-access journal.
Module 19
Centre of Excellence
Become part of an elite research community.
Module 20
Networking & Learning
Connect with global researchers and mentors.
Module 21
Global Recognition
Worth ā¹20,000 / $1,000 in academic value.
Module 22
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