Build end-to-end AMR analysis pipelines on Linux identify resistance genes, and prepare data for advanced ML predictions

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.

Requirements
- No prior experience in bioinformatics or Linux is required, this course is designed to start from the basics.
- A computer (Windows, macOS, or Linux) with stable internet access for software installation and dataset downloads.
- Basic understanding of biology or genetics will be helpful but not mandatory.
- Interest in genomic data analysis, antimicrobial resistance, or computational biology.
- Willingness to learn through hands-on practice using real-world data and pipelines.
- Familiarity with Python programming will be an advantage but is not required, all steps are explained in detail.

Description
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 usingProkkaandABRicate

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 inbioinformatics,genomics, andmicrobiology

Beginners who want aguided, no-coding approachto AMR analysis

Professionals seeking hands-onAMR detection pipelinesfor real data

Anyone curious about integratingbioinformatics and machine learning

Enroll now and start your journey to masterAMR genomics and machine learning powered resistance detectiontoday!

Who this course is for:
- Students and beginners in bioinformatics, microbiology, or computational biology who want to understand how antimicrobial resistance (AMR) is studied using genomic data.
- Researchers and academics interested in learning practical AMR analysis pipelines for bacterial genomics.
- Healthcare and public health professionals looking to explore how bioinformatics tools can be used for AMR gene detection and surveillance.
- Data science and machine learning enthusiasts who want to apply predictive modeling to real biological datasets.
- Bioinformatics professionals seeking to expand their skills in AMR genomics, genome assembly, annotation, and machine learning integration.
- Anyone curious about antimicrobial resistance and eager to learn how to analyze sequencing data using Linux and Python.
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