Program Structure

Module 1: Getting Started with Genomic Data (Understanding Real Sequencing Files)

• Introduction to sequencing types such as whole genome, exome, targeted panels, and RNA-seq.
• Understanding essential file formats like FASTQ, reference genomes, and annotation files.
• Learning how to organize files and create reproducible workflows for reliable analysis.

Module 2: Quality Control and Read Cleaning (Preparing Reliable Data)

• How to evaluate sequencing quality and identify potential issues.
• Understanding quality reports, adapter contamination, duplication levels, and GC bias.
• Cleaning and trimming sequencing reads to improve downstream analysis accuracy.

Module 3: Reference Preparation and Read Alignment

• Understanding reference genome indexing and its importance.
• Aligning sequencing reads to reference genomes and interpreting alignment quality.
• Generating and evaluating BAM files for accurate downstream analysis.

Module 4: Post-Alignment Processing (Preparing Data for Variant Detection)

• Sorting, indexing, and marking duplicate reads for standardized processing.
• Understanding sequencing coverage and identifying potential weak regions.
• Ensuring alignment quality meets requirements for variant analysis.

Module 5: Variant Calling and Variant Files

• Understanding how variant calling identifies genetic differences.
• Generating and interpreting VCF files, including SNPs and insertions/deletions.
• Filtering and refining variants to improve reliability and accuracy.

Module 6: Variant Annotation and Interpretation

• Annotating variants with gene information, predicted impact, and database references.
• Learning how to prioritize variants based on relevance and biological significance.
• Understanding annotated variant reports and extracting meaningful insights.

Module 7: RNA-seq Track (Optional): Expression Analysis Workflow

• Understanding RNA sequencing and gene expression analysis concepts.
• Learning alignment and quantification methods for RNA-seq data.
• Understanding differential expression analysis and interpretation.

Module 8: Visualization and Reporting

• Learning how to visualize genomic data using plots, coverage views, and summaries.
• Building clear and professional analysis reports.
• Presenting results in formats suitable for research papers, presentations, or lab reports.

Module 9: Troubleshooting and Best Practices

• Identifying and fixing common analysis issues such as reference mismatches and tool conflicts.
• Learning validation techniques to ensure pipeline accuracy.
• Applying best practices for reproducibility and workflow reliability.

Final Project

• Analyze real genomic datasets using a guided workflow.
• Generate quality reports, alignment metrics, and variant or expression results.
• Prepare a professional report with interpretation and recommendations.
• Build a portfolio-ready project demonstrating genomic data analysis skills.