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ARGs: ML-Powered Detection, Network Analysis, and Interactive Visualization

Master Antibiotic Resistance Genes (ARGs) Analysis with ML, Network Visualization, and Interactive Bioinformatics Workflows

1–3 Jun 20265:30 PM IST3 Days (1.5 Hours Per Day)Certificate
The live sessions are over · get the full recording

About This Course

This 3-day intensive workshop provides a hands-on, research-oriented learning experience on Antibiotic Resistance Genes (ARGs) using modern bioinformatics tools and workflows. Participants will learn to detect, annotate, visualize, and analyze ARGs from genomic and metagenomic datasets, integrating machine learning-based phenotype prediction, network analysis, and interactive visualization dashboards. The workshop combines practical exercises, real-world datasets, and mini-projects that prepare learners for academic research, public health surveillance, and industry applications. All hands-on exercises are fully compatible with Google Colab, enabling participants to execute workflows without high-performance local infrastructure.

Aim

To equip participants with end-to-end computational and analytical skills for studying Antibiotic Resistance Genes (ARGs), enabling them to detect, quantify, visualize, and interpret resistance patterns in both genomic and metagenomic contexts using state-of-the-art bioinformatics tools and machine learning approaches.

Workshop Objectives

  • Understand mechanisms of antimicrobial resistance and the biological significance of ARGs.
  • Explore and extract ARG sequences from public databases (CARD, ResFinder, ARG-ANNOT, NCBI AMR).
  • Perform quality control and preprocessing of genomic and metagenomic datasets.
  • Detect and annotate ARGs using BLAST, DIAMOND, HMMER, and ML-based approaches.
  • Quantify ARG abundance and distribution across genomes and metagenomes.
  • Predict phenotypic antibiotic resistance from ARG profiles using machine learning models.
  • Construct co-occurrence networks and visualize ARG interactions using Python and Cytoscape.
  • Create interactive dashboards and publication-ready figures for data interpretation and reporting.
  • Design and execute a mini-project applying an end-to-end ARG workflow from sequence data to report generation.
Next: Day-by-day →

Workshop Structure

Day 1: Introduction to Antibiotic Resistance & Data Preparation

  • Antimicrobial Resistance Overview: Mechanisms, clinical and environmental relevance, One Health perspective
  • ARG Fundamentals: Types, functional classes, key examples (β-lactamases, efflux pumps, aminoglycoside-modifying enzymes)
  • ARG Databases: CARD, ResFinder, ARG-ANNOT, NCBI AMR database
  • Metagenomic & Genomic Data Handling: Collect bacterial genomes, metagenomic datasets; FASTA/FASTQ formats
  • Quality Control & Preprocessing: Read trimming, filtering, assembly basics
  • Tools: Python, Biopython, FASTQC, Trimmomatic, Google Colab
  • Hands-On: Explore ARG databases and extract sequences; Perform QC on sample genomes/metagenomes in Google Colab

Day 2: Detection, Annotation & ML-Powered Prediction

  • ARG Detection Approaches: BLAST-based, HMM-based, and ML-based approaches
  • Functional Annotation: Map ARGs to resistance mechanisms, antibiotic classes, and drugs
  • Sequence Alignment & Similarity Search: BLAST, DIAMOND, HMMER
  • ARG Abundance & Distribution Analysis: Quantification in genomes/metagenomes
  • Predictive Phenotyping: ML-based genotype → phenotype prediction
  • Tools: BLAST, DIAMOND, HMMER, CARD/RGI, Python (pandas, seaborn, scikit-learn), Google Colab
  • Hands-On: Detect ARGs in genome/metagenomic datasets; Generate ARG resistance profiles; Apply ML models to predict phenotypic resistance; Visualize ARG abundance with heatmaps and barplots

Day 3: Characterization, Network Analysis, Visualization & Mini Project

  • ARG Characterization: Phylogenetic context, mobile genetic elements, co-occurrence networks
  • Network Analysis: Build ARG co-occurrence networks using NetworkX or Cytoscape
  • Advanced Visualization: Heatmaps, barplots, network diagrams, interactive dashboards (Plotly/Dash or Python notebooks)
  • Reproducibility & Reporting: Workflow documentation, figures for publication, interpretation of resistance patterns
  • Case Studies: Clinical isolates, immunology, environmental microbiomes
  • Tools: Python, NetworkX, Plotly/Dash, matplotlib, seaborn, Cytoscape, Google Colab
  • Hands-On / Mini Project: Full ARG analysis workflow: detection → ML-based prediction → network visualization → interactive dashboard; Generate publication-ready figures and a report

Who Should Enrol?

  • PhD scholars, researchers, and academicians in microbiology, bioinformatics, genomics, or related life sciences.
  • Industry professionals working in clinical microbiology, biotechnology, pharmaceutical research, or public health surveillance.
  • Graduate and postgraduate students with foundational knowledge in molecular biology, genetics, or microbiology, who want to gain practical bioinformatics and ARG analysis skills.
Next: Dates →

Important Dates

Registration Ends

June 1, 2026
IST 4:30 PM IST

Workshop Dates

June 1, 2026 – June 3, 2026
IST 5:30 PM IST

Workshop Outcomes

  • Execute end-to-end ARG detection, annotation, and analysis workflows.
  • Apply machine learning techniques to predict resistance phenotypes from ARG profiles.
  • Perform network-based and phylogenetic characterization of ARGs.
  • Generate interactive visualizations, heatmaps, barplots, and dashboards using Colab-compatible tools.
  • Handle real-world genomic and metagenomic datasets efficiently.
  • Produce reproducible, publication-ready reports and communicate ARG analysis findings.
  • Gain hands-on expertise in current bioinformatics tools and trending computational methods in antimicrobial resistance research.
Next: Mentor →

Meet Your Mentor(s)

Mentor Photo

Nurchu shirisha

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Next: Fees →

Recording fee

Participants in India

₹2,499

Participants outside India

$50

  • All session recordings
  • Study material
  • Certificate after completion
  • 1-year access

Get the recording

The live sessions of this workshop are over, so the live fees no longer apply. The recordings are being uploaded. They appear in your dashboard as soon as they are ready, and we will e-mail you then.

Next: FAQ →

Frequently asked questions

Will I get the recording?

Yes. Paid participants get the session recordings and study material in their NanoSchool dashboard (My registrations → Recorded Lectures / Study Material).

What if I miss a live session?

Watch the recording in your dashboard and send your questions to the mentor through the support desk.

Do I get a certificate?

Yes, an e-certificate of participation after the workshop, with a QR code anyone can verify and an "Add to LinkedIn" button.

Which fee category applies to me?

Choose the category that matches your current role: Student, Ph.D. Scholar / Researcher, Academician / Faculty or Industry Professional.

How can I pay?

By card, UPI or PayPal on the payment page; international bank transfer is also available there.

What is the refund policy?

See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy

Need Help?

We’re here for you!


(+91) 120-4781-217
 WhatsApp +91 99581 61117

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