Learn AMR Detection: From Raw Genomic Reads to ML Prediction

Antimicrobial Resistance (AMR) is a global health crisis, and bioinformatics offers powerful solutions. This hands-on course equips you with the skills to detect, analyze, and predict antimicrobial resistance directly from genomic data.

You'll build complete AMR analysis pipelines, starting with raw sequencing reads and culminating in machine learning-based resistance prediction. This course is designed for beginners, requiring no prior coding experience – all pipelines and code are provided! Focus on understanding the biological insights.

Here's what you'll learn:

• Fundamentals of Antimicrobial Resistance & Bioinformatics: Gain a solid foundation in the key concepts driving AMR and the role of bioinformatics.
• Linux Essentials: Navigate the command line and essential Linux tools for bioinformatics workflows.
• Data Preprocessing & Genome Assembly: Master tools like SPAdes and Quast to prepare and assemble genomes.
• Genome Annotation & AMR Gene Detection: Utilize Prokka to annotate genomes and ABRicate to identify resistance genes using comprehensive databases like CARD, NCBI, and ResFinder.
• AMR Data Analysis & Machine Learning: Extract key features, create AMR gene presence-absence matrices, and apply machine learning models in Python to predict resistance patterns.

By the end of this course, you will be able to:

• Understand the principles of antimicrobial resistance genomics.
• Perform quality control and genome assembly using Linux tools.
• Annotate genomes and detect AMR genes with Prokka and ABRicate.
• Leverage major AMR databases for accurate gene identification.
• Prepare data for machine learning analysis of AMR.
• Apply machine learning models to predict resistance patterns.
• Utilize fully provided codes and pipelines without scripting.

This course is ideal for:

• Bioinformatics students and researchers
• Genomics professionals
• Microbiology students and practitioners
• Beginners seeking a guided, no-coding approach to AMR analysis.
• Professionals needing hands-on AMR detection pipelines for real data.
• Anyone interested in the intersection of bioinformatics and machine learning for resistance detection.

Enroll now and begin your journey to mastering AMR genomics and machine learning-powered resistance detection!