Advanced ARG Detection: Bioinformatics Tools for Research

To provide participants with theoretical knowledge and hands-on bioinformatics skills to detect, analyze, and interpret antibiotic resistance genes (ARGs) from genomic and metagenomic datasets using widely adopted computational tools and databases.

• To understand mechanisms of antimicrobial resistance and functional classes of ARGs
• To explore major ARG databases (CARD, ResFinder, ARG-ANNOT, NCBI AMR)
• To perform quality control and preprocessing of sequencing data
• To detect ARGs using BLAST, DIAMOND, HMMER, and RGI tools
• To annotate and quantify ARGs in genomes and metagenomes
• To characterize ARGs through phylogenetics, co-occurrence analysis, and visualization
• To generate reproducible ARG analysis reports suitable for research and publication

• 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

• 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

• 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
• Tools: Python (pandas, matplotlib, seaborn, networkx), R (optional), CARD/RGI, NCBI tools
• Mini Task: Build a final ARG detection report with resistance profiles, distribution plots, and phylogenetic context

By the end of this 3-day workshop (1.5-hour lecture per day), participants will be able to:

• Explain molecular mechanisms of antibiotic resistance
• Retrieve and curate ARG sequences from public databases
• Perform quality control and preprocessing of sequencing datasets
• Detect and annotate ARGs using computational approaches
• Analyze ARG abundance and distribution patterns
• Interpret genotype-to-phenotype resistance relationships
• Generate publication-ready visualizations and structured resistance reports

• Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
• Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
• University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
• Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
• Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.