Build end-to-end AMR analysis pipelines on Linux identify resistance genes, and prepare data for advanced ML predictions
Antimicrobial resistance (AMR) is one of the most critical challenges in modern medicine and bioinformatics provides the tools to detect, analyze, and predict resistance directly from genomic data.
What you’ll learn
- Understand the fundamentals of Antimicrobial Resistance (AMR) and its biological significance..
- Learn how bioinformatics tools and databases are applied in AMR research and genomic analysis..
- Set up a Linux-based bioinformatics environment and efficiently navigate the Linux file system..
- Perform data preprocessing and quality control using tools like FastQC and Fastp..
- Conduct de novo bacterial genome assembly using SPAdes and assess assembly quality with Quast..
- Annotate genomes using Prokka and interpret gene annotation results in the context of AMR research..
- Detect antimicrobial resistance genes from multiple databases using ABRicate..
- Integrate all steps into a complete AMR analysis pipeline from raw data to gene detection..
- Generate an AMR gene presence–absence matrix and prepare data for downstream analysis using Python..
- Build and interpret machine learning models to predict antimicrobial resistance patterns based on genomic data..
Course Content
- Introduction to Antimicrobial Resistance and Bioinformatics –> 4 lectures • 1hr 31min.
- Basic Linux For Bioinformatics (Optional) –> 6 lectures • 1hr 3min.
- Data Preparation and Quality Control –> 4 lectures • 1hr 33min.
- Genome Assembly –> 4 lectures • 57min.
- Genome Annotation –> 3 lectures • 45min.
- AMR Gene Detection and Analysis –> 3 lectures • 46min.
- Advance Machine Learning Models and Interpretation for AMR Genes –> 2 lectures • 49min.
Requirements
Antimicrobial resistance (AMR) is one of the most critical challenges in modern medicine and bioinformatics provides the tools to detect, analyze, and predict resistance directly from genomic data.
In this hands-on course, you’ll learn how to build complete AMR analysis pipelines starting from raw sequencing reads all the way to machine learning-based resistance prediction.
You’ll begin with the fundamentals of AMR and bioinformatics, then move on to Linux essentials, data preprocessing, and genome assembly using tools like SPAdes and Quast. Next, you’ll perform genome annotation with Prokka and detect resistance genes through ABRicate using multiple AMR databases (CARD, NCBI, ResFinder).
Finally, you’ll learn how to extract key features from AMR data, build an AMR gene presence–absence matrix, and apply machine learning models in Python to predict resistance patterns.
This course combines real-world genomic data, practical coding, and clear explanations to help you master AMR genomics analysis even if you’re a beginner.
No coding is required: all pipelines and codes are provided! Just follow the guided workflow and focus on learning the biological insights.
By the end of this course, you will:
- Understand the principles of antimicrobial resistance genomics
- Perform quality control and genome assembly using Linux-based tools
- Annotate genomes and detect AMR genes using Prokka and ABRicate
- Utilize major AMR databases for gene identification
- Prepare AMR gene presence–absence data for ML analysis
- Apply machine learning models to predict resistance patterns
- Use fully provided codes and pipelines without manual scripting
Ideal For:
- Students and researchers in bioinformatics, genomics, and microbiology
- Beginners who want a guided, no-coding approach to AMR analysis
- Professionals seeking hands-on AMR detection pipelines for real data
- Anyone curious about integrating bioinformatics and machine learning
Enroll now and start your journey to master AMR genomics and machine learning powered resistance detection today!