The default way to use Groovy NGS is to build a "fat" jar and include it in your project. This jar file can be created by cloning the repository and executing the gradle command to build the jar: git clone --recursive https://github.com/ssadedin/groovy-ngs-utils.git ./gradlew jar The jar file is created in build/libs/groovy-ngs-utils.jar and includes all necessary dependencies except for Groovy itself. Therefore, you should include the https://mvnrepository.com/artifact/org.apache.groovy/groovy-all groovy-all dependency from Maven for the appropriate Groovy version in your project as well.Note: in the future it is expected that Groovy NGS will move to support Maven repository based dependency resolution so that it can be more easily included in other projects.

Another way to use Groovy NGS is to use the gngs command to execute expressions directly on the command line. For example, after building (as above), to filter a VCF to only chromosome 21: bash cat test.vcf | ./bin/gngs 'VCF.filter { it.chr == "chr21" }' > test.chr21.vcf

You can run Groovy NGS as a https://en.wikipedia.org/wiki/Read%E2%80%93eval%E2%80%93print_loop REPL to run commands interactively and see the results using the standard Groovy Shell. To facilitate this, there is a script in the bin folder to launch the Groovy Shell with Groovy NGS added to the classpath and the gngs package automatically imported. This can be a very useful way to experiment and learn about how Groovy NGS and Groovy itself works.Here is how an example session looks: bash ./bin/gngsh Groovy Shell (3.0.10, JVM: 11.0.18) Type ':help' or ':h' for help. ------------------------------------------------------------------------------------------------------ groovy:000> import gngs.*; ===> gngs.* groovy:000> vcf = VCF.parse('src/test/data/giab1.tiny.trio.vcf') ===> VCF file src/test/data/giab1.tiny.trio.vcf for NA12877,NA12878,NA12879 groovy:000> vcf.size() ===> 24 groovy:000> vcf.count { it.het } ===> 20

A final way to use Groovy NGS is via Jupyter. You can use any available Groovy kernel, however a particularly useful one is https://github.com/twosigma/beakerx BeakerX which provides Groovy kernels out of the box along with a range of Jupyter widgets as enhancements. Together with Groovy NGS. this makes a very effective data analysis platform for genomic data.