Next-Generation Sequencing (NGS) Data Analysis Course

Module 1: Foundations of Next-Generation Sequencing (NGS) Data Analysis and Core Biological Principles

• Analyze the molecular mechanisms of DNA replication, transcription, and mutation to interpret how sequencing errors propagate in NGS platforms
• Evaluate the architectural differences between Illumina short-read, PacBio long-read, and Oxford Nanopore sequencing technologies for experimental selection
• Calculate coverage depth, read length distributions, and error profiles using FASTQC and MultiQC to assess raw sequencing data quality

Module 2: Laboratory Techniques, Protocols, and Data Collection

• Design end-to-end wet-lab workflows including DNA/RNA extraction, library preparation, and quality control for whole-genome and targeted sequencing
• Troubleshoot common protocol failures such as adapter dimer formation, PCR amplification bias, and sample cross-contamination using gel electrophoresis and qPCR validation
• Execute standardized sample tracking, batch recording, and chain-of-custody documentation to ensure reproducible multi-center sequencing studies

Module 3: Bioinformatics Tools and Computational Analysis

• Construct automated variant calling pipelines using BWA-MEM for alignment, GATK HaplotypeCaller for SNP/indel detection, and ANNOVAR for functional annotation
• Develop reproducible analysis environments by containerizing workflows with Docker/Singularity and orchestrating pipelines with Snakemake or Nextflow
• Visualize genomic data tracks, coverage profiles, and structural variants using Integrative Genomics Viewer (IGV) and UCSC Genome Browser for manual curation

Module 4: Research Methodology and Experimental Design

• Calculate statistical power and sample sizes for case-control, cohort, and family-based sequencing studies using tools like GATK-SV or power calculators
• Design balanced experimental layouts with proper randomization, blocking, and batch effect controls to minimize confounding in multi-lane sequencing runs
• Formulate falsifiable hypotheses and define primary/secondary endpoints aligned with FAIR data principles for publishable NGS research

Module 5: Advanced Next-Generation Sequencing (NGS) Data Analysis Applications and Translational Research

• Integrate multi-omics datasets by combining RNA-seq expression quantification with ChIP-seq peak calling and ATAC-seq chromatin accessibility analysis
• Apply machine learning classifiers such as random forests and deep neural networks to predict disease phenotypes from variant burden scores and pathway enrichment data
• Interpret clonal evolution trajectories and tumor mutational burden from single-cell and bulk whole-exome sequencing in precision oncology contexts

Module 6: Regulatory Compliance, Bioethics, and Safety Standards

• Navigate CLIA/CAP accreditation requirements, FDA guidance on NGS-based diagnostics, and GDPR/HIPAA frameworks for genomic data privacy
• Evaluate informed consent protocols for secondary use of genomic data, return of incidental findings, and data sharing through controlled-access repositories like dbGaP
• Implement cybersecurity measures including encryption, access logging, and de-identification pipelines to protect sensitive human genomic datasets

Module 7: Industry Applications, Career Pathways, and Case Studies

• Assess commercial NGS service models, diagnostic assay development timelines, and regulatory submission strategies from Illumina, Thermo Fisher, and emerging biotech case studies
• Analyze cost-per-sample economics, turnaround time optimization, and CLIA-lab operational workflows for clinical and pharmaceutical NGS deployment
• Construct professional portfolios demonstrating end-to-end project ownership, cross-functional collaboration, and stakeholder communication for biotech hiring managers