Glow is an open-source toolkit to enable bioinformatics at biobank-scale and beyond.
Easy to get started
The toolkit includes the building blocks that you need to perform the most common analyses right away:
- Load VCF, BGEN, and Plink files into distributed DataFrames
- Perform quality control and data manipulation with built-in functions
- Variant normalization and liftOver
- Perform genome-wide association studies
- Integrate with Spark ML libraries for population stratification
- Parallelize command line tools to scale existing workflows
Built to scale
Glow makes genomic data work with Spark, the leading engine for working with large structured datasets. It fits natively into the ecosystem of tools that have enabled thousands of organizations to scale their workflows to petabytes of data. Glow bridges the gap between bioinformatics and the Spark ecosystem.
Glow works with datasets in common file formats like VCF, BGEN, and Plink as well as high-performance big data standards. You can write queries using the native Spark SQL APIs in Python, SQL, R, Java, and Scala. The same APIs allow you to bring your genomic data together with other datasets such as electronic health records, real world evidence, and medical images. Glow makes it easy to parallelize existing tools and libraries implemented as command line tools or Pandas functions.
Building and Testing
This project is built using sbt: https://www.scala-sbt.org/1.0/docs/Setup.html
Start an sbt shell using the
To compile the main code:
To run all tests:
To test a specific suite:
To run Python tests, you must install conda and
activate the environmet in
conda env create -f python/environment.yml conda activate glow
You can then run tests from sbt:
These tests will run with the same Spark classpath as the Scala tests.
If you use IntelliJ, you'll want to set up scalafmt on save.
To test or testOnly in remote debug mode with IntelliJ IDEA set the remote debug configuration in IntelliJ to 'Attach to remote JVM' mode and a specific port number (here the default port number 5005 is used) and then modify the definition of options in groupByHash function in build.sbt to
val options = ForkOptions().withRunJVMOptions(Vector("-Xmx1024m")).withRunJVMOptions(Vector("-agentlib:jdwp=transport=dt_socket,server=y,suspend=y,address=5005"))