TABLE OF CONTENTS

Tutorial: VAST DB for High-Performance Tertiary Analysis of Genomic VCF Data

1. Introduction to Tertiary Genomic Analysis and VCF Data

• 1.0. Genetic Variants
• 1.1. The Genomic Analysis Pipeline: Primary, Secondary, and Tertiary
• 1.2. VCF (Variant Call Format): Structure and Key Information
  • 1.2.1 Header Section: Metadata and Definitions
  • 1.2.2 Data Lines: Genotypes, Quality Scores, and Annotations
  • 1.2.3 INFO and FORMAT Fields: Detailed Variant Information
• 1.3. Challenges in Tertiary Analysis: Scale, Complexity, and Speed
• 1.4 Why a Specialized Database is Crucial for Tertiary Analysis
• 1.5 Introduction to the VAST database capabilities.
• 1.6 Hands-on VCF Exploration: CLI Tools and Python
• 1.7. Estimating Current VCF Data Volumes in Europe

2. VAST DB: Architecture and Key Features for Genomic Analysis

• 2.1. Columnar Storage: Optimizing for Analytical Queries
• 2.2. Massively Parallel Processing (MPP): Scalability and Performance
• 2.3. Transactional Row-by-Row Inserts: Streaming VCF Data Ingestion
• 2.4. Data Compression and Encoding: Storage Efficiency
• 2.5. Predicate Pushdown: Minimizing Data Transfer
• 2.6. Partitioning and Data Locality: Optimizing Query Performance
• 2.7. Snapshots: Versioning and Reproducibility

3. Designing a VAST DB Schema for VCF Data

• 3.1. Data Modeling Considerations: Balancing Flexibility and Performance
• 3.2. Recommended Schema: Multi-Table Approach
  • 3.2.1. Variants Table: Core Variant Information (CHROM, POS, ID, REF, ALT)
  • 3.2.2. Genotypes Table: Sample-Specific Genotype Data (Sample ID, GT, GQ, DP)
  • 3.2.3. Annotations Table: Expanded INFO Fields (Parsed and Typed)
  • 3.2.4. Sample_Metadata table. Data about samples.
  • 3.2.5. File_Metadata table. Data about files.
• 3.3. Data Type Mapping: VCF Fields to VAST DB Data Types
• 3.4. Handling Complex INFO Fields: Arrays and Nested Data
• 3.5. Indexing Strategies: Optimizing for Common Query Patterns
• 3.6. Partitioning Strategies: By Chromosome, Region, or Sample Group
• 3.7 Example VAST database schema

4. Ingesting VCF Data into VAST DB

• 4.1. Preprocessing and Data Cleaning: Ensuring Data Quality
• 4.2. Parsing VCF Files: Libraries and Tools (e.g., bcftools, vt, custom scripts)
• 4.3. Bulk Loading vs. Streaming Inserts: Choosing the Right Approach
• 4.4. Handling Large VCF Files: Chunking and Parallel Ingestion
• 4.5. Data Validation and Integrity Checks
• 4.6 Example data load commands.

5. Performing Tertiary Analysis with VAST DB

• 5.1. Basic Queries: Retrieving Variants and Genotypes
  • 5.1.1 Filtering by Chromosome, Position, and ID
  • 5.1.2 Selecting Specific Samples and INFO Fields
• 5.2. Advanced Queries:
  • 5.2.1 Calculating Allele Frequencies and Counts
  • 5.2.2 Identifying Rare Variants
  • 5.2.3 Filtering by Genotype Quality and Depth
  • 5.2.4 Joining with Annotation Data
  • 5.2.5 Performing Region-Based Queries (e.g., using genomic intervals)
• 5.3. Statistical Analysis:
  • 5.3.1 Implementing Association Tests (e.g., Chi-squared, Fisher's Exact)
  • 5.3.2 Calculating Linkage Disequilibrium (LD)
  • 5.3.3 Integrating with External Statistical Packages (e.g., R, Python)
• 5.4 Working with Snapshots (clones): consistent data views, rollbacks.

6. Performance Optimization and Best Practices

• 6.1. Leveraging Predicate Pushdown: Writing Efficient Queries
• 6.2. Choosing Optimal Partitioning and Indexing Strategies
• 6.3. Monitoring Query Performance and Resource Utilization
• 6.4. Tuning VAST DB Configuration for Genomic Workloads
• 6.5 Understanding VAST database limits.

7. Hands-on Demo: Tertiary Analysis of Example VCF Data

• 7.1. Setting Up a VAST DB Instance (local or cloud)
• 7.2. Loading Sample VCF Data (e.g., from 1000 Genomes Project)
• 7.3. Executing Example Queries from Section 5
• 7.4. Visualizing Results and Interpreting Findings
• 7.5 Performing Comparative Timing Tests.

8. Advanced Topics and Future Directions

• 8.1. Integrating VAST DB with Other Genomic Analysis Tools (e.g., Hail, Glow)
• 8.2. Implementing Custom User-Defined Functions (UDFs)
• 8.3. Handling Very Large Cohorts: Scaling to Millions of Samples
• 8.4. Exploring New Features and Developments in VAST DB

9. Conclusion: VAST DB as a Powerful Platform for Genomic Discovery

• 9.1 Recap the Key Benefits of using VAST
• 9.2 Next steps.