Metagenomics Course

Lesson 1 • Genomics Principles for Metagenomics

Reviews DNA structure, gene organization, and genome architecture. Connects classical genomics knowledge to the multi-genome complexity of metagenomic datasets.

Lesson 2 • Microbial Communities and Ecology

Introduces microbial diversity, community structure, and ecological roles. Provides biological context for why metagenomics is essential to studying complex environments.

Lesson 3 • Ethical and Regulatory Considerations

Covers data privacy, biosafety, and responsible use of environmental and clinical metagenomic data. Prepares students to conduct research within ethical and regulatory frameworks.

Lesson 4 • Types of Metagenomic Approaches

Distinguishes amplicon, shotgun, and functional metagenomics strategies. Guides students in selecting the appropriate approach for a given research question.

Lesson 1 • DNA Quality Assessment

Introduces quantification and integrity assessment tools for extracted DNA. Quality checkpoints here determine suitability for library preparation and sequencing.

Lesson 2 • Sample Collection Strategies

Teaches collection protocols for soil, water, gut, and clinical specimens. Correct collection preserves microbial community integrity and DNA quality.

Lesson 3 • Experimental Design for Metagenomics

Covers hypothesis formulation, variable control, and statistical power in metagenomic studies. Proper design prevents confounding and ensures reproducible results.

Lesson 4 • DNA Extraction Methods

Compares mechanical, chemical, and enzymatic lysis approaches for diverse sample matrices. Students select and optimize extraction protocols to maximize yield and purity.

Lesson 1 • Library Preparation for Metagenomics

Details DNA fragmentation, adapter ligation, size selection, and amplification steps. Proper library preparation directly determines sequencing success and data representativeness.

Lesson 2 • Short-Read Sequencing Platforms

Covers sequencing-by-synthesis chemistry, cluster generation, and output characteristics. Short-read platforms dominate metagenomic studies due to high throughput and accuracy.

Lesson 3 • Long-Read Sequencing Platforms

Introduces nanopore and single-molecule real-time sequencing technologies and their error characteristics. Long reads resolve repetitive regions and improve genome assembly contiguity.

Lesson 4 • Amplicon Sequencing Workflow

Covers marker gene selection, primer design, and amplicon library construction. Amplicon sequencing enables cost-effective community profiling when full shotgun depth is unnecessary.

Lesson 1 • Linux Command-Line Essentials

Introduces file navigation, text manipulation, and process management in Linux. Command-line proficiency is the prerequisite for running all metagenomic software tools.

Lesson 2 • Reproducibility and Workflow Management

Introduces version control, containerization, and workflow managers for reproducible science. Reproducible pipelines ensure that results can be validated and shared across teams.

Lesson 3 • Bioinformatics File Formats

Explains FASTQ, FASTA, SAM/BAM, BED, and GFF formats used throughout metagenomic pipelines. Format literacy is essential for tool interoperability and data interpretation.

Lesson 4 • Scripting for Bioinformatics

Teaches Bash and Python scripting for automating repetitive bioinformatics tasks. Scripting skills reduce manual error and enable scalable, reproducible workflows.

Lesson 1 • Post-Processing Quality Validation

Applies aggregate QC reporting and read count tracking across processing steps. Validation confirms that processed reads meet quality standards before assembly or profiling.

Lesson 2 • Raw Read Quality Assessment

Uses quality control tools to visualize per-base quality, GC content, and duplication rates. Initial QC reveals sequencing artifacts before any downstream processing begins.

Lesson 3 • Deduplication and Complexity Filtering

Removes PCR duplicates and low-complexity reads that inflate diversity estimates. These steps improve the accuracy of taxonomic and functional profiling.

Lesson 4 • Host DNA Removal

Covers alignment-based and k-mer methods for removing host genome contamination. Host removal is critical for clinical and animal-associated microbiome samples.

Lesson 1 • Comparative Community Analysis

Applies diversity indices, ordination, and statistical tests to compare microbial communities. Students identify community-level patterns linked to environmental or clinical variables.

Lesson 2 • Read-Based Taxonomic Classification

Applies k-mer, alignment, and lowest common ancestor methods to classify raw reads. Read-based classification provides rapid community snapshots without requiring assembly.

Lesson 3 • Amplicon-Based Community Profiling

Covers OTU and ASV clustering, chimera removal, and taxonomic assignment for amplicon data. Amplicon profiling enables high-throughput community surveys at reduced cost.

Lesson 4 • Reference Databases for Classification

Surveys major nucleotide, protein, and marker gene databases used in taxonomic classification. Database choice directly affects sensitivity, specificity, and taxonomic resolution.

Lesson 1 • Principles of Metagenomic Assembly

Explains de Bruijn graph assembly, k-mer selection, and challenges unique to metagenomes. Understanding assembly theory guides parameter choices and quality interpretation.

Lesson 2 • Assembly Quality Assessment

Uses contiguity, completeness, and correctness metrics to evaluate assembly outputs. Quality assessment determines whether assemblies are suitable for binning and annotation.

Lesson 3 • Genome Binning Methods

Applies tetranucleotide frequency and coverage-based binning to group contigs into bins. Binning recovers draft genomes of individual organisms from complex communities.

Lesson 4 • Assembly Tools and Execution

Covers leading metagenomic assemblers, their parameters, and computational requirements. Practical execution skills enable students to assemble diverse metagenomic datasets.

Lesson 1 • Metabolic Pathway Reconstruction

Reconstructs metabolic pathways from annotated gene sets and assesses pathway completeness. Pathway reconstruction reveals community-level metabolic capabilities and gaps.

Lesson 2 • Differential Functional Abundance Analysis

Applies statistical methods to identify functions enriched across sample groups. Differential analysis connects functional potential to environmental or health-related variables.

Lesson 3 • Functional Database Annotation

Maps predicted genes to COG, KEGG, Pfam, and CAZy databases for functional assignment. Database breadth determines the scope of metabolic pathways that can be detected.

Lesson 4 • Antibiotic Resistance and Virulence Genes

Detects resistance and virulence determinants using curated databases and alignment methods. These analyses have direct clinical and public health relevance.