Building and Annotating Metagenome-Assembled Genomes (MAGs) from Metagenomics Reads

Comment: What is a Learning Pathway?

We recommend you follow the tutorials in the order presented on this page. They have been selected to fit together and build up your knowledge step by step. If a lesson has both slides and a tutorial, we recommend you start with the slides, then proceed with the tutorial.

This learning path will guide you through the process of constructing and analyzing Metagenome-Assembled Genomes (MAGs) using the Galaxy platform. You will explore the key steps involved in transforming raw metagenomic data into high-quality MAGs, from preprocessing to functional annotation.

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

• List and describe the essential steps in MAGs construction, including quality control, assembly, binning, and refinement.
• Define core concepts such as MAGs, binning, and functional annotation, and understand their significance in metagenomic analysis.
• Explain the importance of preprocessing metagenomic reads, focusing on quality control and contamination removal.
• Compare the quality of MAGs using metrics like completeness and contamination, and assess their suitability for downstream analysis.
• Evaluate the reliability of taxonomic assignments and functional annotations by leveraging reference databases.
• Analyze the relative abundance of microbial taxa in samples and infer ecological dynamics.
• Identify genomic features annotated by tools like Bakta, including coding sequences (CDS), rRNA, and tRNA.
• Interpret functional annotation results to uncover metabolic pathways, virulence factors, and other biological roles within microbial communities.

This path is designed to equip you with both the theoretical knowledge and practical skills needed to confidently construct, evaluate, and analyze MAGs in your research.

Module 0: Introduction to Galaxy – Navigating the Platform and Performing Your First Analysis

Before diving into metagenomics, it’s essential to become comfortable with the tools you’ll be using. This module is designed to introduce you to the Galaxy platform—a user-friendly, web-based environment for bioinformatics analysis.

Through a combination of video tutorials and hands-on exercises, you will:

• Familiarize yourself with the Galaxy interface, including its key features and navigation.
• Learn how to import data, organize your workspace, and use basic tools.
• Complete a guided, simple analysis to gain confidence in running workflows and interpreting results.

By the end of this module, you’ll be ready to tackle more advanced analyses in subsequent modules.

Time estimation: 1 hour 40 minutes

Module 1: Quality Control – Ensuring High-Quality Metagenomic Data

High-quality data is the foundation of reliable metagenomic analysis. Poor-quality reads—whether due to low base-calling accuracy, adapter contamination, or insufficient length—can introduce errors, bias assemblies, and compromise your results.

In this module, you will:

• Understand the importance of quality control in metagenomic workflows and its impact on downstream analyses.
• Learn how to assess, trim, and filter raw sequencing data to retain only high-quality reads.
• Use Galaxy tools to remove contaminants, trim adapters, and filter low-quality sequences, ensuring your data is clean and ready for further analysis.

By the end of this module, you’ll be equipped to confidently prepare your metagenomic data for assembly and other advanced analyses.

Time estimation: 1 hour 30 minutes

Module 2: Contamination and Host Reads Removal – Purifying Your Metagenomic Dataset

Metagenomic datasets frequently contain non-microbial sequences, such as host DNA (e.g., from honey bees) or external contaminants (e.g., human DNA introduced during sample handling or sequencing). These sequences can distort downstream analyses, leading to misassemblies, incorrect taxonomic assignments, and biased functional interpretations.

In this module, you will:

• Recognize the sources and impacts of contamination in metagenomic datasets.
• Learn how to identify and filter out host and contaminant sequences using Galaxy tools.
• Ensure your dataset is enriched for microbial reads, improving the accuracy of MAG reconstruction and enabling more reliable biological insights.

By the end of this module, you’ll be able to confidently clean your metagenomic data, setting the stage for high-quality MAG assembly and analysis.

Time estimation: 1 hour

Module 3: Assembly – Reconstructing and Assessing Contigs from Metagenomic Reads

The foundation of MAG reconstruction lies in assembly—the computational process of piecing together fragmented metagenomic reads into longer genomic sequences called contigs. Think of it as solving a complex jigsaw puzzle: your goal is to identify reads that “fit together” by detecting overlapping sequences.

In this module, you will:

• Understand the principles and challenges of metagenomic assembly.
• Learn how to use Galaxy tools to assemble high-quality contigs from your preprocessed reads.
• Explore strategies to optimize assembly parameters for improved accuracy and completeness.
• Assess the quality of your assembly using metrics such as contig length distribution, N50, and coverage, ensuring your contigs are suitable for downstream analysis.

By the end of this module, you’ll be equipped to transform your cleaned metagenomic data into contiguous sequences and evaluate their quality, setting the stage for successful MAG reconstruction.

Time estimation: 2 hours

Module 4: Binning – From Contigs to Refined Microbial Genomes

Metagenomic binning is the process of grouping assembled contigs into discrete bins, each representing a potential microbial genome. By analyzing sequence composition, coverage, and similarity, binning allows researchers to reconstruct individual genomes from complex microbial communities.

However, initial bins often contain fragmented, redundant, or contaminated sequences, which can compromise downstream analyses. To address this, bin refinement and de-replication are essential steps to improve the quality, completeness, and non-redundancy of your Metagenome-Assembled Genomes (MAGs).

In this module, you will:

• Understand how binning algorithms classify contigs into bins based on genomic signatures.
• Use Galaxy tools to perform binning and assign sequences to their likely microbial origins.
• Evaluate bin quality using metrics such as completeness, contamination, and strain heterogeneity.
• Learn techniques for refining bins, including merging, splitting, and contamination removal.
• Explore de-replication to identify and retain only the highest-quality representative MAG from sets of similar genomes.

By the end of this module, you’ll be able to reconstruct, refine, and validate high-quality MAGs, ensuring they are ready for taxonomic and functional analysis.

Time estimation: 2 hours

Module 6: Functional Annotation of MAGs – Applying Genomic Approaches to Metagenome-Assembled Genomes

Functional annotation is a fundamental process in genomic analysis, whether you’re working with microbial isolates or Metagenome-Assembled Genomes (MAGs). By applying the same robust approaches used for isolates, you can identify and characterize genes in MAGs, revealing their roles in metabolic pathways, environmental interactions, and ecological functions.

In this module, you will:

• Learn how functional annotation tools (such as Bakta) predict and classify genomic features, including coding sequences (CDS), rRNA, tRNA, and more.
• Use Galaxy to annotate your MAGs, uncovering their biological potential and functional capabilities.
• Explore antimicrobial resistance (AMR) gene detection as part of functional annotation, identifying genes associated with resistance mechanisms.
• Interpret the ecological and functional roles of your MAGs, including genes linked to pathogenicity, nutrient cycling, symbiosis, and AMR.
• Assess the reliability of annotations and understand the importance of reference databases in ensuring accurate predictions.

By the end of this module, you’ll be able to analyze MAGs with the same confidence and precision as microbial isolates, gaining deeper insights into their ecological roles and functional potential—including their resistance profiles.

Time estimation: 5 hours

Recommended follow-up tutorial

While this learning pathway goes into detail about all steps required for MAGs generation, a full end-to-end training was developed, which executes all workflows for MAGs generation and annotation instead of running individual steps. This tutorial can be used as a guide to execute the workflows on your own data. It explains how you can perform quality control, remove host contamination, and run the main MAGs workflow to obtain quality-controlled, taxonomically and functionally annotated MAGs.

Time estimation: 6 hours