Advanced ARG Detection: Bioinformatics Tools for Research
Detect, Analyze, and Track Resistance Genes with Advanced Bioinformatics Tools
About This Course
This workshop introduces advanced ARG detection workflows using bioinformatics tools such as BLAST, CARD, ResFinder, AMRFinderPlus, MEGARes, Abricate, and RGI. Participants will learn how to process sequence data, detect resistance genes, interpret identity/coverage thresholds, compare resistome profiles, and generate research-ready reports. The focus is dry-lab, hands-on analysis for microbiology, genomics, and public health research.
Aim
This workshop aims to train participants in advanced antibiotic resistance gene (ARG) detection using modern bioinformatics tools and databases. It focuses on identifying, annotating, and interpreting ARGs from genomic and metagenomic datasets. Participants will learn how to compare resistance profiles across samples and connect ARG findings with microbial ecology, public health, and research applications. The program bridges AMR biology, genomics, and computational analysis.
Workshop Objectives
- Understand ARG types, resistance mechanisms, and resistome concepts.
- Learn bioinformatics workflows for ARG detection from sequence data.
- Use ARG databases and tools such as CARD, ResFinder, AMRFinderPlus, and MEGARes.
- Interpret alignment outputs, gene annotations, identity, coverage, and confidence scores.
- Compare ARG profiles across samples for research and surveillance insights.
Workshop Structure
Day 1: Introduction to Antibiotic Resistance and ARGs
- Antimicrobial Resistance Overview: Mechanisms of resistance, clinical and environmental relevance
- ARGs: Types, functional classes, and common examples (β-lactamases, efflux pumps, aminoglycoside-modifying enzymes)
- Databases for ARGs: CARD, ResFinder, ARG-ANNOT, NCBI AMR database
- Sequence Data Preparation: Collecting bacterial genomes, metagenomic samples, and FASTA/FASTQ formats
- Quality Control: Read trimming, filtering, and assembly basics
- Tools: Python, Biopython, FASTQC, Trimmomatic
- Mini Task: Explore ARG databases and extract sequences for analysis
Day 2: Detection and Annotation of ARGs
- ARG Detection Approaches: BLAST-based, HMM-based, and machine learning approaches
- Functional Annotation: Mapping ARGs to resistance mechanisms, classes, and antibiotics
- Sequence Alignment and Similarity Search: BLAST, DIAMOND, and hidden Markov models (HMMs)
- ARG Abundance and Distribution Analysis: Quantification in genomes/metagenomes
- Tools: BLAST, DIAMOND, HMMER, CARD/RGI tool, Python (pandas, seaborn for visualization)
- Mini Task: Detect ARGs in a bacterial genome or metagenome dataset and generate resistance profiles
Day 3: Characterization, Visualization, and Reporting
- Characterization of ARGs: Phylogenetic context, mobile genetic elements, co-occurrence networks
- Predicting Phenotypic Resistance: Linking genotype to antibiotic susceptibility
- Visualization of ARG Distribution: Heatmaps, barplots, network diagrams
- Reproducibility & Reporting: Workflow documentation, figures for publication, interpretation of resistance patterns
Who Should Enrol?
- Undergraduate/postgraduate degree in Microbiology, Biotechnology, Bioinformatics, Genomics, Computational Biology, or related fields.
- Professionals in clinical microbiology, diagnostics, infectious disease research, public health, or environmental monitoring.
- Data scientists and AI/ML engineers interested in applying analytics to AMR and microbial genomics datasets.
- Individuals with a keen interest in antimicrobial resistance, resistome analysis, and bioinformatics research.
Important Dates
Registration Ends
May 6, 2026
IST 7:00 PM
Workshop Dates
May 6, 2026 – May 8, 2026
IST 8:00 PM
Workshop Outcomes
Participants will be able to:
- Detect ARGs from genomic or metagenomic datasets.
- Use major ARG databases and annotation tools confidently.
- Interpret ARG outputs using identity, coverage, and resistance class information.
- Compare resistome profiles across clinical, environmental, or research samples.
- Prepare research-ready ARG detection summaries and reports.
Meet Your Mentor(s)
Prof. Kumud Malhotra
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 . . .
Fee Structure
Student Fee
₹2499 | $65
Ph.D. Scholar / Researcher Fee
₹3499 | $75
Academician / Faculty Fee
₹4499 | $85
Industry Professional Fee
₹5499 | $95
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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