Developer Quickstart
Learn to build an app that you can run on the Platform.
The steps below require the DNAnexus SDK. You must download and install it if you have not done so already.
Besides this Quickstart, there are Developer Tutorials located in the sidebar that go over helpful tips for new users as well. A few of them include:
Step 1. Build a Simple App
Every DNAnexus app starts with 2 files:
dxapp.json
: a file containing the app's metadata: its inputs and outputs, how the app is run, and execution requirementsa script that is executed in the cloud when the app is run
Let's start by creating a file called dxapp.json
with the following text:
{ "name": "coolapp",
"runSpec": {
"distribution": "Ubuntu",
"release": "24.04",
"version": "0",
"interpreter": "python3",
"file": "code.py"
}
}
Above, we've specified the name for our app (coolapp
), the type of interpreter (python3
) to run our script with, and a path (code.py
) to the script that we create next. ("version":"0"
) refers to the version of Ubuntu 24.04 application execution environment that supports (python3
) interpreter.
Next, we create our script in a file called code.py
with the following text:
import dxpy
@dxpy.entry_point('main')
def main(**kwargs):
print("Hello, DNAnexus!")
return {}
That's all we need. To build the app, first log in to DNAnexus and start a project with dx login
. In the directory with the two files above, run:
dx login
dx build -a
Next, run the app and watch the output:
dx run coolapp --watch
That's it! You have just made and run your first DNAnexus applet. Applets are lightweight apps that live in your project, and are not visible in the App Library. When you typed dx run
, the app ran on its own Linux instance in the cloud. You have exclusive, secure access to the CPU, storage, and memory on the instance. The DNAnexus API lets your app read and write data on the Platform, as well as launch other apps.
The app is available in the DNAnexus web interface, as part of the project that you started. It can be configured and run in the Workflow Builder, or shared with other users by sharing the project.
Step 2. Run BLAST
Next, we make our app do something a bit more interesting: take in two files with FASTA-formatted DNA, run the BLAST tool to compare them, and output the result.
In the cloud, your app runs on Ubuntu Linux 24.04, where BLAST is available as an APT package, ncbi-blast+
. You can request that the DNAnexus execution environment install it before your script is run by listing ncbi-blast+
in the execDepends
field of your dxapp.json
like this:
{ "name": "coolapp",
"runSpec": {
"distribution": "Ubuntu",
"release": "24.04",
"version": "0",
"interpreter": "python3",
"file": "code.py",
"execDepends": [ {"name": "ncbi-blast+"} ]
}
}
Next, let's update code.py
to run BLAST:
import dxpy, subprocess
@dxpy.entry_point('main')
def main(seq1, seq2):
dxpy.download_dxfile(seq1, "seq1.fasta")
dxpy.download_dxfile(seq2, "seq2.fasta")
subprocess.call("blastn -query seq1.fasta -subject seq2.fasta > report.txt", shell=True)
report = dxpy.upload_local_file("report.txt")
return {"blast_result": report}
We're ready to rebuild the app and test it on some real data. You can use some demo inputs available in the Demo Data project, or you can upload your own data with dx upload
or via the website. If you use the Demo Data inputs, make sure the project you are running your app in is the same region as the Demo Data project.
Rebuild the app with dx build -a
, and run it like this:
dx run coolapp \
-i seq1="Demo Data:/Developer Quickstart/NC_000868.fasta" \
-i seq2="Demo Data:/Developer Quickstart/NC_001422.fasta" \
--watch
Once the job is done, you can examine the output with dx head report.txt
, download it with dx download
, or view it on the website.
Step 3. Provide an Input/Output Spec
Workflows are a powerful way to visually connect, configure, and run multiple apps in pipelines. To add our app to a workflow and be able to connect its inputs and/or outputs to other apps, our app needs both input and output specifications. Let's update our dxapp.json
as follows:
{
"name": "coolapp",
"runSpec": {
"distribution": "Ubuntu",
"release": "24.04",
"version": "0",
"interpreter": "python3",
"file": "code.py",
"execDepends": [ {"name": "ncbi-blast+"} ]
},
"inputSpec": [
{"name": "seq1", "class": "file"},
{"name": "seq2", "class": "file"}
],
"outputSpec": [
{"name": "blast_result", "class": "file"}
]
}
Rebuild the app with dx build -a
. You can run it in the same way as before, but we can add the applet to a workflow. Click "New Workflow" while looking at your project on the website, and click on coolapp
once to add it to the workflow. You see inputs and outputs appear on the workflow stage which can be connected to other stages in the workflow.
Also, if you go back to the command line and run dx run coolapp
with no input arguments, it prompts you for the input values for seq1
and seq2
.
Step 4. Configure App Settings
Besides specifying input files, the I/O specification can also be used to configure settings that we want the app to use. For example, we can configure the E-value setting and other BLAST settings with this code and dxapp.json
:
import dxpy, subprocess
@dxpy.entry_point('main')
def main(seq1, seq2, evalue, blast_args):
dxpy.download_dxfile(seq1, "seq1.fasta")
dxpy.download_dxfile(seq2, "seq2.fasta")
command = "blastn -query seq1.fasta -subject seq2.fasta -evalue {e} {args} > report.txt".format(e=evalue, args=blast_args)
subprocess.call(command, shell=True)
report = dxpy.upload_local_file("report.txt")
return {"blast_result": report}
{
"name": "coolapp",
"runSpec": {
"distribution": "Ubuntu",
"release": "24.04",
"version": "0",
"interpreter": "python3",
"file": "code.py",
"execDepends": [ {"name": "ncbi-blast+"} ]
},
"inputSpec": [
{"name": "seq1", "class": "file"},
{"name": "seq2", "class": "file"},
{"name": "evalue", "class": "float", "default": 0.01},
{"name": "blast_args", "class": "string", "default": ""}
],
"outputSpec": [
{"name": "blast_result", "class": "file"}
]
}
Rebuild the app again and add it in the workflow builder. You should see the evalue
and blast_args
settings available when you click the gear button on the stage. After building and configuring a workflow, you can run the workflow itself with dx run workflowname
.
Step 5. Use SDK Tools
One of the utilities provided in the SDK is dx-app-wizard
. This tool prompts you with a series of questions with which it creates the basic files needed for a new app. It also gives you the option of writing your app as a bash shell script instead of Python. Just run dx-app-wizard
to try it out.
Learn More
For additional information and examples of how to run jobs using the CLI, see Working with files using dx run
may be useful. This material is not a part of the official DNAnexus documentation and is for reference only.
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