Glow

Glow is an open-source toolkit for working with genomic data at biobank-scale and beyond. The toolkit is natively built on Apache Spark, the leading unified engine for big data processing and machine learning, enabling genomics workflows to scale to population levels.

• Introduction to Glow
• Getting Started
  • Running Locally
  • Getting started on Databricks
  • Notebooks embedded in the docs
• GWAS Tutorial
  • 1. Quality Control
  • 2. Glow Whole Genome Regression (GloWGR)
  • 3. Regression
• Customizing Glow
  • Customizing the Databricks environment
  • Maintaining private patches on top of Glow
• Variant Data Manipulation
  • Data Simulation
  • Read and Write VCF, Plink, and BGEN with Spark
  • Read Genome Annotations (GFF3) as a Spark DataFrame
  • Create a Genomics Delta Lake
  • Variant Quality Control
  • Sample Quality Control
  • Liftover
  • Variant Normalization
  • Split Multiallelic Variants
  • Merging Variant Datasets
  • Utility Functions
• Tertiary Analysis
  • The Pipe Transformer for Parallelizing Command-Line Bioinformatics Tools
  • Python Statistics Libraries
  • GloWGR: Whole Genome Regression
  • GloWGR: Genome-Wide Association Study (GWAS) Regression Tests
• Troubleshooting
• Contributing
  • Raise Issues
  • Contribute to the codebase
• Blog Posts
  • [Jul. 2020] Introducing GloWGR: An industrial-scale, ultra-fast and sensitive method for genetic association studies
  • [Jun. 2020] Glow 0.4 Enables Integration of Genomic Variant and Annotation Data
  • [Mar. 2020] Glow 0.3.0 Introduces Several New Large-Scale Genomic Analysis Features
  • [Nov. 2019] Streamlining Variant Normalization on Large Genomic Datasets
• Additional Resources
  • Databricks notebooks
  • External blog posts
• Python API
  • Glow Top-Level Functions
  • PySpark Functions
  • GloWGR