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Metagenomics, Resistome Analysis, and Horizontal Gene Transfer Course
Original price was: USD $120.00.USD $59.00Current price is: USD $59.00.
The Metagenomics, Resistome Analysis, and Horizontal Gene Transfer Course explores advanced genomic approaches to study microbial communities and antibiotic resistance. Learners will understand metagenomic sequencing, re
AMR research has moved beyond culture-based surveillance. Metagenomics now plays a central role in detecting resistance reservoirs across wastewater, livestock systems, and human microbiomes.
A resistance gene sitting in a chromosome is one thing; a gene associated with plasmids or transposons is another, raising immediate questions about dissemination risk. This field sits at the intersection of:
- Microbial genomics and bioinformatics
- Epidemiology and outbreak intelligence
- Environmental genomics and One Health research
- Computational biology and network analysis
What Participants Will Learn
• Perform quality control with FastQC/Trimmomatic
• Assemble metagenomic reads with MEGAHIT/SPAdes
• Annotate contigs using Prokka and MGnify
• Run DeepARG for gene detection and abundance
• Identify plasmids using PlasFlow
• Profiling transposons with MobileElementFinder
• Integrate ARG and MGE profiles for HGT potential
• Visualize patterns in R (ggplot2) and Python
• Compare environmental and clinical resistomes
• Map HGT networks and interpret diversity plots
Course Structure / Table of Contents
Module 1 — Foundations of Metagenomics, Resistomes, and HGT
- The resistome concept: intrinsic, acquired, and environmental determinants
- Horizontal gene transfer mechanisms: transformation, transduction, and conjugation
- Why MGEs matter in resistance dissemination
- Common research questions in environmental and clinical resistome studies
Module 2 — NGS Data Formats and Quality Preprocessing
- FASTQ structure and metadata-aware handling
- Sequence quality control using FastQC
- Adapter removal and trimming with Trimmomatic
- Preparing data for reproducible metagenomic workflows
Module 3 — Metagenomic Assembly and Contig Annotation
- Principles of assembly in complex microbial communities
- Hands-on assembly using MEGAHIT or SPAdes
- Annotation with Prokka and MGnify-style workflows
- Contig-level interpretation and assembly quality considerations
Module 4 — ARG Databases and Detection Strategies
- Working with MEGARes, CARD, ResFinder, and DeepARG
- Homology-based versus model-based detection (DeepARG)
- Estimating ARG abundance and interpreting output tables
Module 5 — Detecting Mobile Genetic Elements
- Using PlasFlow to identify plasmid-associated sequences
- Using MobileElementFinder for transposon detection
- Interpreting co-localization signals between ARGs and MGEs
Module 6 — Integrating Profiles for HGT Assessment
- Linking resistance genes with potential mobility signatures
- Distinguishing gene prevalence from transfer potential
- Assessing ecological and epidemiological significance
Module 7 — Visualization of Resistome Patterns
- Plotting abundance in R with ggplot2
- Visualizing results in Python with matplotlib and seaborn
- Building publication-ready resistome summaries and diversity plots
Module 8 — Comparative Interpretation and Network Mapping
- Environmental versus clinical resistome comparison
- Constructing and interpreting HGT network maps
- Identifying patterns worth follow-up in ecological studies
| Area | Conceptual Focus | Hands-On Focus |
|---|---|---|
| Preprocessing | Read quality, trimming logic | FastQC, Trimmomatic |
| Assembly | Assembly in mixed communities | MEGAHIT, SPAdes |
| Annotation | Contig labeling & labeling | Prokka, MGnify workflows |
| ARG Detection | Database logic & strategies | DeepARG, CARD interpretation |
| MGE Analysis | Mobility and transfer potential | PlasFlow, MobileElementFinder |
Tools, Techniques, or Platforms Covered
MEGAHIT / SPAdes
DeepARG
CARD / MEGARes
PlasFlow
MobileElementFinder
R (ggplot2)
Python (Seaborn)
DeepARG
CARD / MEGARes
PlasFlow
MobileElementFinder
R (ggplot2)
Python (Seaborn)
Real-World Applications
Environmental Microbiology: Identifying resistance reservoirs in wastewater, river systems, and agricultural soils to support ecological monitoring.
Clinical Research: Characterizing microbial communities linked to patient environments or microbiome-associated resistance burden.
One Health interface: Comparing resistomes across human, animal, and environmental interfaces with technical confidence.
Who Should Attend
- PhD scholars in AMR, microbial ecology, or metagenomics research
- Postgraduate students in bioinformatics, genomics, or public health
- Bioinformatics professionals expanding into resistome and HGT analysis
- Public health and surveillance professionals using sequence-based methods
- Researchers Transitioning from wet-lab into sequence data interpretation
Prerequisites or Recommended Background
Basic familiarity with microbiology or AMR concepts. Introductory understanding of NGS data and some comfort with command-line workflows or R/Python for visualization.
Why This Course Stands Out
This course connects resistome analysis with HGT assessment rather than treating them as separate topics. It covers both detection and interpretation, using recognized AMR databases and publication-quality visualization techniques suited for real-world datasets.
Frequently Asked Questions
What is this course about?
It is a 3-day course on metagenomics, resistome profiling, and horizontal gene transfer analysis, covering assembly to HGT mapping.
Do I need prior coding experience?
Some familiarity with command-line bioinformatics or scripting is helpful, but advanced coding is not required.
Will the course include hands-on work?
Yes. Curriculum includes practical work with FastQC, Trimmomatic, MEGAHIT, SPAdes, DeepARG, and PlasFlow.
How is this useful in research or surveillance?
It supports wastewater monitoring, clinical microbiome characterization, and One Health comparative studies.
Is this focused on detection or HGT?
It covers both, specifically the point where they meet: integrating resistance profiles with mobility evidence.
Is this suitable for beginners?
It is best for learners with some background in genomics or bioinformatics. It is beginner-friendly but dense.
| Level | Professional |
|---|---|
| Duration | Self-Paced |
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What You’ll Gain
- Full access to e-LMS
- Publication opportunity
- Self-assessment & final exam
- e-Certificate
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