Program Structure

Module 1: Getting Started with Genomic Data (What You’ll Actually Work With)

• Understanding sequencing types: whole genome, exome, targeted panels, RNA-seq basics.
• Meet the files: FASTQ, reference genomes, annotation files, and why they matter.
• Setting up a clean workflow: folder structure, naming, and reproducible runs.

Module 2: Quality Control and Read Cleaning (Before Anything Else)

• How to check if your sequencing data is good or problematic.
• Quality reports: what to look for (adapters, low-quality ends, duplication, GC bias).
• Trimming and filtering reads so downstream analysis becomes more reliable.

Module 3: Reference Preparation and Read Alignment

• How reference genomes are indexed and why that step matters.
• Aligning reads to a genome and understanding mapping quality.
• Generating BAM files and performing basic alignment checks.

Module 4: Post-Alignment Processing (Making the Data Analysis-Ready)

• Sorting, indexing, and marking duplicates in a clean, standard way.
• Understanding coverage: depth, breadth, and regions that fail quietly.
• How to confirm your alignment results are strong enough for variant calling.

Module 5: Variant Calling and Variant Files (Turning Reads into Genetic Differences)

• What variant calling really means and what it can and cannot detect.
• Generating VCF files and understanding SNPs vs indels.
• Filtering variants to reduce noise and improve confidence.

Module 6: Variant Annotation and Interpretation (From Coordinates to Meaning)

• Annotating variants with gene names, predicted impact, and known databases (conceptual workflow).
• Prioritizing variants: frequency, effect, location, and relevance to phenotype or research question.
• How to read an annotated variant table without getting overwhelmed.

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

• When to use RNA-seq and how it differs from DNA analysis.
• Read alignment/quantification basics for expression workflows.
• Differential expression overview and how to interpret expression outputs responsibly.

Module 8: Visualization and Reporting (Presenting Results Like a Researcher)

• Basic genomic visualization: coverage views, variant summaries, and QC plots.
• Building a clean result narrative: methods, results, interpretation, limitations.
• Creating a short report that can be used for a thesis, paper draft, or lab update.

Module 9: Troubleshooting and Best Practices (The Part Everyone Needs)

• Common errors: reference mismatch, tool version conflicts, low coverage, memory issues.
• How to validate outputs and avoid silent pipeline failures.
• Best practices for reproducibility: logging, versioning, and workflow checkpoints.

Final Project

• Analyze a provided dataset (or your own, if available) using a guided workflow.
• Generate key outputs: QC summary, alignment metrics, variant list or expression summary (as applicable).
• Submit a final report with interpretation, visuals, and next-step recommendations.
• Example projects: Variant discovery in a targeted gene panel, comparing variants across two samples, or a small RNA-seq expression comparison.