In some of my previous posts, I've talked about using the Databricks Runtime for Genomics for scaling up common bioinformatics analyses using Apache Spark. Today, I want to highlight a rather new package that further enhances our ability to perform genomics-based workloads in Azure Databricks: Glow. Project Glow began out of a partnership between the life sciences team at Databricks and the at Genetics Center at Regeneron.
Glow is an open-source and independent Spark library that brings even more flexibility and functionality to Azure Databricks. This toolkit is natively built on Apache Spark, enabling the scale of the cloud for genomics workflows.
Glow allows for genomic data to work with Spark SQL. So, you can interact with common genetic data types as easily as you can play with a .csv file. Learn more about Project Glow at projectglow.io.
Glow already includes easy-to-use functions for reading and writing common file formats like VCF, BGEN, Plink, or GFF3. In addition, there are tools for performing the following secondary and tertiary analyses:
|Secondary Analyses||Tertiary Analyses|
Learn more about the features of Glow here: https://glow.readthedocs.io/
Why Do We Need Scale?
Genome-wide association studies (GWAS) correlate genetic variants with a trait or disease of interest. These types of studies are effective in the identification of particular mutations and how they affect the disease in question. Traditionally, these analysis are performed by bioinformaticians and genetics and workstations, which have a limit in their processing power.
As genetic sequencing becomes cheaper and more prevalent and as study cohorts have increased in size to millions, there is a need to robustly engineer GWAS to work at scale. Luckily, the Azure cloud, Apache Spark, and Databricks are built for just that!
In the following video, I give a quick overview of some nice features from the Genomics Runtime in Azure Databricks and how to get started using the Glow package.