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Please see the GATK website, where you can download a precompiled executable, read documentation, ask questions, and receive technical support. For GitHub basics, see here.


This repository contains the next generation of the Genome Analysis Toolkit (GATK). The contents of this repository are 100% open source and released under the Apache 2.0 license (see LICENSE.TXT).

GATK4 aims to bring together well-established tools from the GATK and Picard codebases under a streamlined framework, and to enable selected tools to be run in a massively parallel way on local clusters or in the cloud using Apache Spark. It also contains many newly developed tools not present in earlier releases of the toolkit.

Table of Contents


  • To run GATK:
    • Java 8 is needed to run or build GATK. We recommend either of the following:
    • Python 2.6 or greater (required to run the
      frontend script)
    • Python 3.6.2, along with a set of additional Python packages, is required to run some tools and workflows. See Python Dependencies for more information.
    • R 3.2.5 (needed for producing plots in certain tools)
  • To build GATK:
    • A Java 8 JDK
    • Git 2.5 or greater
    • git-lfs 1.1.0 or greater. Required to download the large files used to build GATK, and test files required to run the test suite. Run
      git lfs install
      after downloading, followed by
      git lfs pull
      from the root of your git clone to download all of the large files, including those required to run the test suite. The full download is approximately 5 gigabytes. Alternatively, if you are just building GATK and not running the test suite, you can skip this step since the build itself will use git-lfs to download the minimal set of large
      resource files required to complete the build. The test resources will not be downloaded, but this greatly reduces the size of the download.
    • Gradle 5.6. We recommend using the
      script which will download and use an appropriate gradle version automatically (see examples below).
    • R 3.2.5 (needed for running the test suite)
  • Pre-packaged Docker images with all needed dependencies installed can be found on our dockerhub repository. This requires a recent version of the docker client, which can be found on the docker website.
  • Python Dependencies:
    • GATK4 uses the Conda package manager to establish and manage the Python environment and dependencies required by GATK tools that have a Python dependency. This environment also includes the R dependencies used for plotting in some of the tools. The
      environment requires hardware with AVX support for tools that depend on TensorFlow (e.g. CNNScoreVariant). The GATK Docker image comes with the
      environment pre-configured.
      • At this time, the only supported platforms are 64-bit Linux distributions. The required Conda environment is not currently supported on OS X/macOS.
    • To establish the environment when not using the Docker image, a conda environment must first be "created", and then "activated":
      • First, make sure Miniconda or Conda is installed (Miniconda is sufficient).
      • To "create" the conda environment:
        • If running from a zip or tar distribution, run the command
          conda env create -f gatkcondaenv.yml
          to create the
        • If running from a cloned repository, run
          ./gradlew localDevCondaEnv
          . This generates the Python package archive and conda yml dependency file(s) in the build directory, and also creates (or updates) the local
          conda environment.
      • To "activate" the conda environment (the conda environment must be activated within the same shell from which GATK is run):
        • Execute the shell command
          source activate gatk
          to activate the
      • See the Conda documentation for additional information about using and managing Conda environments.

Quick Start Guide

  • Build the GATK:
    ./gradlew bundle
  • Get help on running the GATK:
    ./gatk --help
  • Get a list of available tools:
    ./gatk --list
  • Run a tool:
    ./gatk PrintReads -I src/test/resources/NA12878.chr17_69k_70k.dictFix.bam -O output.bam
  • Get help on a particular tool:
    ./gatk PrintReads --help

Downloading GATK4

You can download and run pre-built versions of GATK4 from the following places:

  • A zip archive with everything you need to run GATK4 can be downloaded for each release from the github releases page. We also host unstable archives generated nightly in the Google bucket gs://gatk-nightly-builds.

  • You can download a GATK4 docker image from our dockerhub repository. We also host unstable nightly development builds on this dockerhub repository.

    • Within the docker image, run gatk commands as usual from the default startup directory (/gatk).

Building GATK4

  • To do a full build of GATK4, first clone the GATK repository using "git clone", then run:

    ./gradlew bundle

Equivalently, you can just type:


  • This creates a zip archive in the build/ directory with a name like containing a complete standalone GATK distribution, including our launcher gatk, both the local and spark jars, and this README.
  • You can also run GATK commands directly from the root of your git clone after running this command.
  • Note that you must have a full git clone in order to build GATK, including the git-lfs files in src/main/resources. The zipped source code alone is not buildable.
  • Other ways to build:

  • ./gradlew installDist

    • Does a fast build that only lets you run GATK tools from inside your git clone, and locally only (not on a cluster). Good for developers!
  • ./gradlew installAll
    • Does a semi-fast build that only lets you run GATK tools from inside your git clone, but works both locally and on a cluster. Good for developers!
  • ./gradlew localJar
    • Builds only the GATK jar used for running tools locally (not on a Spark cluster). The resulting jar will be in
      with a name like
      , and can be used outside of your git clone.
  • ./gradlew sparkJar
    • Builds only the GATK jar used for running tools on a Spark cluster (rather than locally). The resulting jar will be in
      with a name like
      , and can be used outside of your git clone.
    • This jar will not include Spark and Hadoop libraries, in order to allow the versions of Spark and Hadoop installed on your cluster to be used.
  • To remove previous builds, run:

    ./gradlew clean
  • For faster gradle operations, add

    to your
    file. This will keep a gradle daemon running in the background and avoid the ~6s gradle start up time on every command.
  • Gradle keeps a cache of dependencies used to build GATK. By default this goes in

    . If there is insufficient free space in your home directory, you can change the location of the cache by setting the
    environment variable.
  • The version number is automatically derived from the git history using

    git describe
    , you can override it by setting the
    property. (
    ./gradlew -DversionOverride=my_weird_version printVersion
  • Running GATK4

    • The standard way to run GATK4 tools is via the

      wrapper script located in the root directory of a clone of this repository.

      • Requires Python 2.6 or greater (this includes Python 3.x)
      • You need to have built the GATK as described in the Building GATK4 section above before running this script.
      • There are several ways
        can be run:
        • Directly from the root of your git clone after building
        • By extracting the zip archive produced by
          ./gradlew bundle
          to a directory, and running
          from there
        • Manually putting the
          script within the same directory as fully-packaged GATK jars produced by
          ./gradlew localJar
          ./gradlew sparkJar
        • Defining the environment variables
          , and setting them to the paths to the GATK jars produced by
          ./gradlew localJar
          ./gradlew sparkJar
      • gatk
        can run non-Spark tools as well as Spark tools, and can run Spark tools locally, on a Spark cluster, or on Google Cloud Dataproc.
      • Note: running with
        java -jar
        directly and bypassing
        causes several important system properties to not get set, including htsjdk compression level!
    • For help on using

      itself, run
      ./gatk --help
    • To print a list of available tools, run

      ./gatk --list

      • Spark-based tools will have a name ending in
        ). Most other tools are non-Spark-based.
    • To print help for a particular tool, run

      ./gatk ToolName --help

    • To run a non-Spark tool, or to run a Spark tool locally, the syntax is:

      ./gatk ToolName toolArguments

    • Tool arguments that allow multiple values, such as -I, can be supplied on the command line using a file with the extension ".args". Each line of the file should contain a single value for the argument.

    • Examples:

      ./gatk PrintReads -I input.bam -O output.bam
      ./gatk PrintReadsSpark -I input.bam -O output.bam

    Passing JVM options to gatk

    • To pass JVM arguments to GATK, run

      with the
      ./gatk --java-options "-Xmx4G" 

      ./gatk --java-options "-Xmx4G -XX:+PrintGCDetails"

      Passing a configuration file to gatk

    • To pass a configuration file to GATK, run

      with the
      ./gatk --gatk-config-file GATKProperties.config 

      An example GATK configuration file is packaged with each release as
      This example file contains all current options that are used by GATK and their default values.

    Running GATK4 with inputs on Google Cloud Storage:

    • Many GATK4 tools can read BAM or VCF inputs from a Google Cloud Storage bucket. Just use the "gs://" prefix:
      ./gatk PrintReads -I gs://mybucket/path/to/my.bam -L 1:10000-20000 -O output.bam
    • Important: You must set up your credentials first for this to work! There are three options:
      • Option (a): run in a Google Cloud Engine VM
        • If you are running in a Google VM then your credentials are already in the VM and will be picked up by GATK, you don't need to do anything special.
      • Option (b): use your own account
        • Install Google Cloud SDK
        • Log into your account:
          gcloud auth application-default login
        • Done! GATK will use the application-default credentials you set up there.
      • Option (c): use a service account
        • Create a new service account on the Google Cloud web page and download the JSON key file
        • Install Google Cloud SDK
        • Tell gcloud about the key file:
          gcloud auth activate-service-account --key-file "$PATH_TO_THE_KEY_FILE"
        • Set the
          environment variable to point to the file
        • Done! GATK will pick up the service account. You can also do this in a VM if you'd like to override the default credentials.

    Running GATK4 Spark tools locally:

    • GATK4 Spark tools can be run in local mode (without a cluster). In this mode, Spark will run the tool in multiple parallel execution threads using the cores in your CPU. You can control how many threads Spark will use via the

    • Examples:


    with 4 threads on your local machine:
        ./gatk PrintReadsSpark -I src/test/resources/large/CEUTrio.HiSeq.WGS.b37.NA12878.20.21.bam -O output.bam \
            -- \
            --spark-runner LOCAL --spark-master 'local[4]'
    with as many worker threads as there are logical cores on your local machine:
        ./gatk PrintReadsSpark -I src/test/resources/large/CEUTrio.HiSeq.WGS.b37.NA12878.20.21.bam -O output.bam \
            -- \
            --spark-runner LOCAL --spark-master 'local[*]'
    • Note that the Spark-specific arguments are separated from the tool-specific arguments by a

    Running GATK4 Spark tools on a Spark cluster:

    ./gatk ToolName toolArguments -- --spark-runner SPARK --spark-master  additionalSparkArguments
    * Examples:

      ./gatk PrintReadsSpark -I hdfs://path/to/input.bam -O hdfs://path/to/output.bam \
          -- \
          --spark-runner SPARK --spark-master 
    ./gatk PrintReadsSpark -I hdfs://path/to/input.bam -O hdfs://path/to/output.bam \
      -- \
      --spark-runner SPARK --spark-master  \
      --num-executors 5 --executor-cores 2 --executor-memory 4g \
      --conf spark.executor.memoryOverhead=600
    • You can also omit the "--num-executors" argument to enable dynamic allocation if you configure the cluster properly (see the Spark website for instructions).
    • Note that the Spark-specific arguments are separated from the tool-specific arguments by a
    • Running a Spark tool on a cluster requires Spark to have been installed from, since
      invokes the
      tool behind-the-scenes.
    • Note that the examples above use YARN but we have successfully run GATK4 on Mesos as well.

    Running GATK4 Spark tools on Google Cloud Dataproc:

    • You must have a Google cloud services account, and have spun up a Dataproc cluster in the Google Developer's console. You may need to have the "Allow API access to all Google Cloud services in the same project" option enabled (settable when you create a cluster).
    • You need to have installed the Google Cloud SDK from here, since
      invokes the
      tool behind-the-scenes. As part of the installation, be sure that you follow the
      setup instructions here. As this library is frequently updated by Google, we recommend updating your copy regularly to avoid any version-related difficulties.
    • Your inputs to the GATK when running on dataproc are typically in Google Cloud Storage buckets, and should be specified on your GATK command line using the syntax
    • You can run GATK4 jobs on Dataproc from your local computer or from the VM (master node) on the cloud.

    Once you're set up, you can run a Spark tool on your Dataproc cluster using a command of the form:

    ./gatk ToolName toolArguments -- --spark-runner GCS --cluster myGCSCluster additionalSparkArguments

    • Examples:

        ./gatk PrintReadsSpark \
            -I gs://my-gcs-bucket/path/to/input.bam \
            -O gs://my-gcs-bucket/path/to/output.bam \
            -- \
            --spark-runner GCS --cluster myGCSCluster
        ./gatk PrintReadsSpark \
            -I gs://my-gcs-bucket/path/to/input.bam \
            -O gs://my-gcs-bucket/path/to/output.bam \
            -- \
            --spark-runner GCS --cluster myGCSCluster \
            --num-executors 5 --executor-cores 2 --executor-memory 4g \
            --conf spark.yarn.executor.memoryOverhead=600
    • When using Dataproc you can access the web interfaces for YARN, Hadoop and HDFS by opening an SSH tunnel and connecting with your browser. This can be done easily using included

      scripts/dataproc-cluster-ui myGCSCluster

      Or see these these instructions for more details.

    • Note that the spark-specific arguments are separated from the tool-specific arguments by a

    • If you want to avoid uploading the GATK jar to GCS on every run, set the

      environment variable to a bucket you have write access to (eg.,
      export GATK_GCS_STAGING=gs:///
    • Dataproc Spark clusters are configured with dynamic allocation so you can omit the "--num-executors" argument and let YARN handle it automatically.

    Using R to generate plots

    Certain GATK tools may optionally generate plots using the R installation provided within the conda environment. If you are uninterested in plotting, R is still required by several of the unit tests. Plotting is currently untested and should be viewed as a convenience rather than a primary output.

    Bash Command-line Tab Completion (BETA)

    • A tab completion bootstrap file for the bash shell is now included in releases. This file allows the command-line shell to complete GATK run options in a manner equivalent to built-in command-line tools (e.g. grep).

    • This tab completion functionality has only been tested in the bash shell, and is released as a beta feature.

    • To enable tab completion for the GATK, open a terminal window and source the included tab completion script:

    • Sourcing this file will allow you to press the tab key twice to get a list of options available to add to your current GATK command. By default you will have to source this file once in each command-line session, then for the rest of the session the GATK tab completion functionality will be available. GATK tab completion will be available in that current command-line session only.

    • Note that you must have already started typing an invocation of the GATK (using gatk) for tab completion to initiate:

    • We recommend adding a line to your bash settings file (i.e. your ~/.bashrc file) that sources the tab completion script. To add this line to your bash settings / bashrc file you can use the following command:
    echo "source /" >> ~/.bashrc
    • Where
       is the fully qualified path to the

    For GATK Developers

    General guidelines for GATK4 developers

    • Do not put private or restricted data into the repo.

    • Try to keep datafiles under 100kb in size. Larger test files should go into

      (and subdirectories) so that they'll be stored and tracked by git-lfs as described above.
    • GATK4 is Apache 2.0 licensed. The license is in the top level LICENSE.TXT file. Do not add any additional license text or accept files with a license included in them.

    • Each tool should have at least one good end-to-end integration test with a check for expected output, plus high-quality unit tests for all non-trivial utility methods/classes used by the tool. Although we have no specific coverage target, coverage should be extensive enough that if tests pass, the tool is guaranteed to be in a usable state.

    • All newly written code must have good test coverage (>90%).

    • All bug fixes must be accompanied by a regression test.

    • All pull requests must be reviewed before merging to master (even documentation changes).

    • Don't issue or accept pull requests that introduce warnings. Warnings must be addressed or suppressed.

    • Don't issue or accept pull requests that significantly decrease coverage (less than 1% decrease is sort of tolerable).

    • Don't use

      for anything other than human consumption (ie. don't base the logic of your code on results of
    • Don't override

      unless you really know what you're doing. If you do override it, document thoroughly. Otherwise, prefer other means of making copies of objects.
    • For logging, use org.apache.logging.log4j.Logger

    • We mostly follow the Google Java Style guide

    • Git: Don't push directly to master - make a pull request instead.

    • Git: Rebase and squash commits when merging.

    • If you push to master or mess up the commit history, you owe us 1 growler or tasty snacks at happy hour. If you break the master build, you owe 3 growlers (or lots of tasty snacks). Beer may be replaced by wine (in the color and vintage of buyer's choosing) in proportions of 1 growler = 1 bottle.

    Testing GATK

    • Before running the test suite, be sure that you've installed

      git lfs
      and downloaded the large test data, following the git lfs setup instructions
    • To run the test suite, run

      ./gradlew test

      • Test report is in
      • What will happen depends on the value of the
        environment variable:
        • unset or any other value : run non-cloud unit and integration tests, this is the default
        • cloud
          : run only the cloud, unit, integration, conda (python + R), or Spark tests
        • all
          : run the entire test suite
      • Cloud tests require being logged into
        and authenticated with a project that has access to the cloud test data. They also require setting several certain environment variables.
          : path to a local JSON file with service account credentials
          : your google cloud project
          : a gs:// path to a writable location
          : path to cloud test data, ex: gs://hellbender/test/resources/
      • Setting the environment variable
        will produce much less output from the test suite
    • To run a subset of tests, use gradle's test filtering (see gradle doc):

      • You can use
        with a wildcard to run a specific test class, method, or to select multiple test classes:
        • ./gradlew test --tests *SomeSpecificTestClass
        • ./gradlew test --tests *SomeTest.someSpecificTestMethod
        • ./gradlew test --tests*
    • To run tests and compute coverage reports, run

      ./gradlew jacocoTestReport
      . The report is then in

      . (IntelliJ has a good coverage tool that is preferable for development).
    • We use Travis-CI as our continuous integration provider.

      • Before merging any branch make sure that all required tests pass on travis.
      • Every travis build will upload the test results to our GATK Google Cloud Storage bucket. A link to the uploaded report will appear at the very bottom of the travis log. Look for the line that says
        See the test report at
        . If TestNG itself crashes there will be no report generated.
    • We use Broad Jenkins for our long-running tests and performance tests.

      • To add a performance test (requires Broad-ID), you need to make a "new item" in Jenkins and make it a "copy" instead of a blank project. You need to base it on either the "-spark-" jobs or the other kind of jobs and alter the commandline.
    • To output stack traces for

      set the environment variable

    Using Git LFS to download and track large test data

    We use git-lfs to version and distribute test data that is too large to check into our repository directly. You must install and configure it in order to be able to run our test suite.

    • After installing git-lfs, run

      git lfs install
      • This adds hooks to your git configuration that will cause git-lfs files to be checked out for you automatically in the future.
    • To manually retrieve the large test data, run

      git lfs pull
      from the root of your GATK git clone.
      • The download size is approximately 5 gigabytes.
    • To add a new large file to be tracked by git-lfs, simply:

      • Put the new file(s) in
        (or a subdirectory)
      • git add
        the file(s), then
        git commit -a
      • That's it! Do not run
        git lfs track
        on the files manually: all files in
        are tracked by git-lfs automatically.

    Creating a GATK project in the IntelliJ IDE (last tested with version 2016.2.4):

    • Ensure that you have

      and the Java 8 JDK installed
    • You may need to install the TestNG and Gradle plugins (in preferences)

    • Clone the GATK repository using git

    • In IntelliJ, click on "Import Project" in the home screen or go to File -> New... -> Project From Existing Sources...

    • Select the root directory of your GATK clone, then click on "OK"

    • Select "Import project from external model", then "Gradle", then click on "Next"

    • Ensure that "Gradle project" points to the build.gradle file in the root of your GATK clone

    • Select "Use auto-import" and "Use default gradle wrapper".

    • Make sure the Gradle JVM points to Java 1.8. You may need to set this manually after creating the project, to do so find the gradle settings by clicking the wrench icon in the gradle tab on the right bar, from there edit "Gradle JVM" argument to point to Java 1.8.

    • Click "Finish"

    • After downloading project dependencies, IntelliJ should open a new window with your GATK project

    • Make sure that the Java version is set correctly by going to File -> "Project Structure" -> "Project". Check that the "Project SDK" is set to your Java 1.8 JDK, and "Project language level" to 8 (you may need to add your Java 8 JDK under "Platform Settings" -> SDKs if it isn't there already). Then click "Apply"/"Ok".

    Setting up debugging in IntelliJ

    • Follow the instructions above for creating an IntelliJ project for GATK

    • Go to Run -> "Edit Configurations", then click "+" and add a new "Application" configuration

    • Set the name of the new configuration to something like "GATK debug"

    • For "Main class", enter

    • Ensure that "Use classpath of module:" is set to use the "gatk" module's classpath

    • Enter the arguments for the command you want to debug in "Program Arguments"

    • Click "Apply"/"Ok"

    • Set breakpoints, etc., as desired, then select "Run" -> "Debug" -> "GATK debug" to start your debugging session

    • In future debugging sessions, you can simply adjust the "Program Arguments" in the "GATK debug" configuration as needed

    Updating the Intellij project when dependencies change

    If there are dependency changes in

    it is necessary to refresh the gradle project. This is easily done with the following steps.
    • Open the gradle tool window ( "View" -> "Tool Windows" -> "Gradle" )
    • Click the refresh button in the Gradle tool window. It is in the top left of the gradle view and is represented by two blue arrows.

    Setting up profiling using JProfiler

    • Running JProfiler standalone:

      • Build a full GATK4 jar using
        ./gradlew localJar
      • In the "Session Settings" window, select the GATK4 jar, eg.
        for "Main class or executable JAR" and enter the right "Arguments"
      • Under "Profiling Settings", select "sampling" as the "Method call recording" method.
    • Running JProfiler from within IntelliJ:

      • JProfiler has great integration with IntelliJ (we're using IntelliJ Ultimate edition) so the setup is trivial.
      • Follow the instructions above for creating an IntelliJ project for GATK
      • Right click on a test method/class/package and select "Profile"

    Uploading Archives to Sonatype (to make them available via maven central)

    To upload snapshots to Sonatype you'll need the following:

    • You must have a registered account on the sonatype JIRA (and be approved as a gatk uploader)
    • You need to configure several additional properties in your

    • If you want to upload a release instead of a snapshot you will additionally need to have access to the gatk signing key and password

    #needed for snapshot upload

    #needed for signing a release signing.keyId= signing.password= signing.secretKeyRingFile=/Users//.gnupg/secring.gpg

    To perform an upload, use

    ./gradlew uploadArchives

    Builds are considered snapshots by default. You can mark a build as a release build by setting

    The archive name is based off of
    git describe

    Building GATK4 Docker images

    Please see the the Docker README in

    . This has instructions for the Dockerfile in the root directory.

    Releasing GATK4

    Please see the How to release GATK4 wiki article for instructions on releasing GATK4.

    Generating GATK4 documentation

    To generate GATK documentation, run

    ./gradlew gatkDoc
    • Generated docs will be in the

    Generating GATK4 WDL Wrappers

    • A WDL wrapper can be generated for any GATK4 tool that is annotated for WDL generation (see the wiki article How to Prepare a GATK tool for WDL Auto Generation) to learn more about WDL annotations.

    • To generate the WDL Wrappers, run

      ./gradlew gatkWDLGen
      . The generated WDLs and accompanying JSON input files can be found in the
    • To generate WDL Wrappers and validate the resulting outputs, run

      ./gradlew gatkWDLGenValidation
      . Running this task requires a local cromwell installation, and environment variables
      to be set to the full pathnames of the
      jar files. If no local install is available, this task will run automatically on travis in a separate job whenever a PR is submitted.
    • WDL wrappers for each GATK release are published to the gatk-tool-wdls repository. Only tools that have been annotated for WDL generation will show up there.

    Using Zenhub to track github issues

    We use Zenhub to organize and track github issues.

    • To add Zenhub to github, go to the Zenhub home page while logged in to github, and click "Add Zenhub to Github"

    • Zenhub allows the GATK development team to assign time estimates to issues, and to mark issues as Triaged/In Progress/In Review/Blocked/etc.

    Further Reading on Spark

    Apache Spark is a fast and general engine for large-scale data processing. GATK4 can run on any Spark cluster, such as an on-premise Hadoop cluster with HDFS storage and the Spark runtime, as well as on the cloud using Google Dataproc.

    In a cluster scenario, your input and output files reside on HDFS, and Spark will run in a distributed fashion on the cluster. The Spark documentation has a good overview of the architecture.

    Note that if you don't have a dedicated cluster you can run Spark in standalone mode on a single machine, which exercises the distributed code paths, albeit on a single node.

    While your Spark job is running, the Spark UI is an excellent place to monitor the progress. Additionally, if you're running tests, then by adding

    you can run a single Spark test and look at the Spark UI (on http://localhost:4040/) as it runs.

    You can find more information about tuning Spark and choosing good values for important settings such as the number of executors and memory settings at the following:

    How to contribute to GATK

    (Note: section inspired by, and some text copied from, Apache Parquet)

    We welcome all contributions to the GATK project. The contribution can be a issue report or a pull request. If you're not a committer, you will need to make a fork of the gatk repository and issue a pull request from your fork.

    For ideas on what to contribute, check issues labeled "Help wanted (Community)". Comment on the issue to indicate you're interested in contibuting code and for sharing your questions and ideas.

    To contribute a patch: * Break your work into small, single-purpose patches if possible. It’s much harder to merge in a large change with a lot of disjoint features. * Submit the patch as a GitHub pull request against the master branch. For a tutorial, see the GitHub guides on forking a repo and sending a pull request. If applicable, include the issue number in the pull request name. * Make sure that your code passes all our tests. You can run the tests with

    ./gradlew test
    in the root directory. * Add tests for all new code you've written. We prefer unit tests but high quality integration tests that use small amounts of data are acceptable. * Follow the General guidelines for GATK4 developers.

    We tend to do fairly close readings of pull requests, and you may get a lot of comments. Some things to consider: * Write tests for all new code. * Document all classes and public methods. * For all public methods, check validity of the arguments and throw

    if invalid. * Use braces for control constructs,
    etc. * Make classes, variables, parameters etc
    unless there is a strong reason not to. * Give your operators some room. Not
    a + b
    and not
    foo(int a,int b)
    foo(int a, int b)
    . * Generally speaking, stick to the Google Java Style guide

    Thank you for getting involved!



    The authors list is maintained in the AUTHORS file. See also the Contributors list at github.


    Licensed under the Apache 2.0 License. See the LICENSE.txt file.

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