Next Generation Sequencing Data Analysis: Hands-on NGS Data Analysis for Genomic Research Projects
Master Genomic Data Science with AI-Driven Bioinformatics Pipelines Bridging Experimental Biology and Computational Intelligence for Research Excellence
Course Description
This intensive 6-month program is designed to transform biological researchers into proficient genomic data scientists through an immersive, hands-on curriculum. Participants will navigate the entire NGS workflow, from raw signal processing and Quality Control (QC) to advanced secondary and tertiary analysis. The course integrates cutting-edge AI and Machine Learning models to enhance variant calling accuracy and interpret complex single-cell transcriptomics data. By working on real-world genomic research projects, you will master the Linux command-line, R/Bioconductor environments, and automated Nextflow pipelines. This training bridges the gap between wet-lab experimentation and dry-lab computational insights, preparing you for high-impact roles in precision medicine, drug discovery, and evolutionary genomics.
What You'll Learn
Sequencing Fundamentals: Understand the chemistry and physics of Illumina, Oxford Nanopore, and PacBio platforms.
Data Preprocessing: Master tools like FastQC, Trimmomatic, and MultiQC for rigorous quality assessment.
Alignment & Mapping: Perform high-accuracy read mapping using BWA, Bowtie2, and STAR aligners.
Variant Discovery: Identify SNPs, Indels, and Structural Variants using GATK and AI-driven variant callers.
Transcriptomics (RNA-Seq): Analyze differential gene expression and functional enrichment using DESeq2 and ClusterProfiler.
AI in Genomics: Apply Deep Learning models for base calling and predictive oncology analytics.
Pipeline Automation: Build reproducible genomic workflows using Nextflow and Snakemake.
Curriculum
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Module 1: Introduction to NGS Technologies and Genomic Data Formats (FASTQ, BAM, VCF).
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Module 2: Essential Linux Command Line and Shell Scripting for Big Data.
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Module 3: Quality Control, Read Trimming, and Reference Genome Mapping Strategies.
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Module 4: DNA-Seq Analysis: Germline and Somatic Variant Calling Workflows.
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Module 5: RNA-Seq Analysis: From Transcript Quantification to Pathway Enrichment.
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Module 6: Epigenomics and Metagenomics: ChIP-Seq, ATAC-Seq, and Microbiome Profiling.
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Module 7: Integrating AI and Machine Learning for Genomic Pattern Recognition.
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Module 8: Capstone Project: End-to-End Analysis of a Real-World Genomic Dataset.
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