Big Data, Small Organism: Metagenomics in Action
Unlock the hidden secrets of microbial dark matter using Next-Generation Sequencing (NGS). Bridge the gap between Big Data analytics and Environmental Microbiology through Bioinformatics and AI-driven insights.
Course Description
In the era of Digital Biology, the study of unculturable microorganisms has shifted from the petri dish to the Data Center. This course, "Big Data Small Organism: Metagenomics in Action," provides a comprehensive deep dive into the Computational Biology workflows required to process massive Shotgun Metagenomics datasets. Students will explore how Machine Learning (ML) algorithms and Cloud Computing are revolutionizing our understanding of the Human Microbiome, soil health, and marine ecosystems.
Throughout this program, you will master the art of transforming raw, high-throughput sequencing reads into actionable biological knowledge. We focus on the Big Data challenges of Metagenomic Assembly, Binning, and Functional Annotation, using industry-standard tools and Python-based AI frameworks. By leveraging Scalable Data Pipelines and Predictive Modeling, you will learn to identify novel genes and metabolic pathways that were previously invisible. Whether you are a biologist looking to gain Data Science skills or a computer scientist entering Biotech, this course provides the technical roadmap to excel in the rapidly growing field of Precision Medicine and Synthetic Biology.
What You'll Learn
Next-Generation Sequencing (NGS): Mastery of Illumina and Nanopore data formats (FASTQ/FASTA).
Data Preprocessing: Advanced Quality Control (QC) and adapter trimming using AI-enhanced filtering.
Taxonomic Profiling: Identifying "who is there" using k-mer based classifiers like Kraken2.
Functional Annotation: Mapping genes to metabolic pathways via KEGG and GO databases.
Genome Binning: Reconstructing Metagenome-Assembled Genomes (MAGs) from complex mixtures.
Predictive Analytics: Utilizing Random Forests and Neural Networks to correlate microbial abundance with disease states.
Curriculum
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Module 1: Linux Foundations & Bioinformatics Environment SetupIntroduction to open-source environments: Step-by-step Linux operating system installation and configuration.Command-line basics: File system navigation, directory manipulation, text editing, and permissions management.Tool installation engineering: Working with packages, dependencies, and automated software environments.Metagenomics terminology: Fundamental principles of amplicon vs. whole-genome shotgun sequencing.Module 2: Raw Big Data Management & Quality ControlProgrammatic data downloading: Sourcing raw fastq datasets automatically from public sequence repositories (NCBI SRA).High-throughput quality control: Executing automated sequence evaluation reports using FastQC.Sequence preprocessing: Advanced trimming, filtering, and adapter removal to isolate high-quality biological reads. Module 3: Core QIIME2 Architecture & DADA2 PipelineQIIME2 environment activation: Mastering data artifacts, visualization types, and semantic data import frameworks.High-resolution denoising: Running the DADA2 pipeline to trim error-prone bases and remove chimeric structures. Feature table generation: Creating exact Amplicon Sequence Variant (ASV) counts across complex multi-sample datasets.Module 4: Taxonomic Classification & Advanced VisualizationsTaxonomic assignment: Deploying pre-trained machine learning classifiers to determine microbial identity.Phylogenetic tree construction: Aligning sequence features and generating rooted trees for diversity indexing.Hierarchical plot engineering: Generating interactive, shareable Krona plots and relative abundance charts.Downstream analysis foundations: Introduction to alpha and beta diversity metrics under complex experimental conditions.
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