About Workshop
The AI-Powered Nanopore Sequencing & Multi-Omics Analysis workshop is a 3-day hands-on program designed for researchers, bioinformaticians, and genomics professionals to explore the full Nanopore sequencing workflow. Participants will learn to apply AI and deep learning models for basecalling, quality control, genome assembly, variant detection, and epigenetic profiling, including DNA methylation and RNA modifications. The workshop integrates practical exercises with real-world datasets, interactive dashboards, and a mini end-to-end project, enabling participants to generate actionable insights from raw sequencing data. By the end of the program, attendees will gain hands-on experience with state-of-the-art AI tools and bioinformatics pipelines, equipping them to apply AI-driven Nanopore sequencing methods in research, metagenomics, and multi-omics analysis.
Aim
To equip participants with the knowledge and hands-on experience to apply AI-driven workflows for Nanopore sequencing, generating actionable insights in genomics, metagenomics, and epigenetic research.
What Participants Will Learn
- Understand the fundamentals of Nanopore sequencing and data formats (POD5/FAST5).
- Apply AI and deep learning for basecalling, quality control, and methylation-aware signal interpretation.
- Conduct genome assembly, polishing, and metagenomic classification using AI-assisted tools.
- Perform variant calling and epigenetic analysis including DNA methylation and RNA modifications.
- Build interactive dashboards for visualization of sequencing metrics, assembly, and genomic insights.
- Complete an end-to-end mini-project integrating all steps of the Nanopore-AI workflow.
Structure
📅 Day 1: Foundations of Nanopore Sequencing and AI-Based Basecalling
- Overview of Nanopore sequencing: technology, raw signal generation, and sequencing chemistry (R10.4)
- Understanding POD5/FAST5 files and conversion to FASTQ reads
- AI and deep learning in basecalling: transformers, hybrid models, and methylation-aware basecallers (Bonito, Dorado, Remora)
- Adaptive sampling for targeted sequencing and dynamic read enrichment
- Quality control of Nanopore reads: read length, N50, quality scores, signal-level QC, and reproducibility
🛠️ Hands-on:
- AI-based basecalling using Dorado/Bonito
- Real-time QC dashboard creation (Python + Streamlit)
- Interpretation of sequencing metrics and error profiles
- Targeted read enrichment simulation using adaptive sampling
🧰 Tools Covered:
- Dorado, Bonito, NanoPlot, PycoQC, Python (Pandas, Plotly, Streamlit), Google Colab
📅 Day 2: AI-Assisted Genome Assembly, Polishing, and Real-Time Metagenomics
- Long-read genome assembly and error characteristics of Nanopore data
- AI-assisted polishing and consensus correction (Medaka, DeepConsensus)
- Hybrid assembly integration: Nanopore + Illumina reads for high-quality genomes
- Metagenomic classification: strain-level resolution, pathogen detection, and microbial community profiling
- Real-time data pipelines for adaptive microbiome analysis
- Visualization of assembly quality and taxonomic composition using interactive dashboards
🛠️ Hands-on:
- Genome assembly and AI-based polishing using Medaka
- Comparative metagenomic classification using Kraken2 + Bracken
- Taxonomic visualization using Krona
- Assembly quality scoring and dashboard visualization
🧰 Tools Covered:
- Medaka, Kraken2, Bracken, Krona, Python (Scikit-learn, Pandas, Plotly), Google Colab
📅 Day 3: AI-Based Variant Calling, Epigenetic Profiling, and Multi-Omics Integration
- Detection of genomic variants: SNVs, indels, structural variants using deep learning (Clair3, DeepVariant adaptation)
- AI-based DNA methylation analysis and phasing (Dorado methylation models, Modkit)
- Multi-modal variant detection integrating basecall signals, methylation, and coverage
- RNA modifications (m6A) detection from direct RNA sequencing (emerging trend)
- Population genomics and disease association applications using Nanopore-derived variants
- Explainable AI approaches for variant and epigenetic prediction
- Reproducibility, FAIR data practices, and pipeline standardization
🛠️ Hands-on:
- Variant calling using Clair3 and DeepVariant
- DNA methylation detection and allele-specific analysis
- Multi-sample methylation comparison for differential analysis
- Mini-project: End-to-end AI pipeline — raw signals → basecalling → assembly → variant & methylation analysis → interactive dashboard
🧰 Tools Covered:
- Clair3, Dorado methylation models, Modkit, IGV, Python (Pandas, Plotly, Streamlit), Google Colab
Important Dates
Registration Ends
2 : 00 PM
Workshop Dates
2026-06-02
3 : 00 PM
3 : 00 PM
What You Will Gain
Outcomes
- Perform AI-based basecalling, QC, and methylation-aware analysis.
- Execute AI-assisted genome assembly, polishing, and metagenomic classification.
- Conduct variant calling and epigenetic profiling including methylation and RNA modifications.
- Integrate sequencing data into interactive dashboards for analysis and reporting.
- Apply knowledge to research projects and real-world datasets, generating reproducible and FAIR-compliant results.
- Gain practical experience with state-of-the-art AI and bioinformatics tools.
Who Should Attend
- PhD scholars, researchers, and academicians in genomics, bioinformatics, or molecular biology.
- Industry professionals in precision medicine, microbiome research, and AI-assisted biotechnology.
- Bioinformatics enthusiasts seeking hands-on AI applications in sequencing and multi-omics analysis.
- Participants with basic familiarity in Python/data analysis (helpful but not mandatory).