Programme Parameters
Advanced Postgrad
3 Days (4.5 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This 3‑day intensive program (1.5 hour lecture per day) delivers a complete introduction to detection and characterization of antibiotic resistance genes (ARGs) using state‑of‑the‑art bioinformatics pipelines. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This 3‑day intensive program (1.5 hour lecture per day) delivers a complete introduction to detection and characterization of antibiotic resistance genes (ARGs) using state‑of‑the‑art bioinformatics pipelines.
📋 Course Objectives
1. Translate bioinformatics theory into practical, reproducible analysis.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in bioinformatics
• R&D engineers and working professionals applying bioinformatics in industry
• Academics and educators building research or teaching capacity in bioinformatics
🚀 Key Learning Outcomes
• A demonstrable bioinformatics project for your research or industry portfolio.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Day 1 – Introduction to Antibiotic Resistance and ARGs
Explore mechanisms of antimicrobial resistance and functional ARG classes • Navigate major ARG databases (CARD, ResFinder, ARG‑ANNOT, NCBI AMR) • Perform sequence quality control with FASTQC & Trimmomatic
Day 2 – Detection and Annotation of ARGs
Apply BLAST, DIAMOND, and HMMER for ARG detection • Utilize CARD/RGI and Python libraries for functional annotation • Quantify ARG abundance across genomes and metagenomes
Day 3 – Characterization, Visualization, and Reporting
Generate phylogenetic and co‑occurrence analyses of ARGs • Create heatmaps, barplots, and network diagrams with Python (seaborn, matplotlib, networkx) • Assemble a reproducible, publication‑ready ARG report
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Biopython |
| Covered Tool / Platform | FASTQC |
| Covered Tool / Platform | Trimmomatic |
| Covered Tool / Platform | BLAST |
| Covered Tool / Platform | DIAMOND |
| Covered Tool / Platform | HMMER |
| Covered Tool / Platform | CARD/RGI |
| Covered Tool / Platform | pandas |
| Covered Tool / Platform | seaborn |
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