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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