Quick analysis of QC data
Here is the USI for a QC run
mzspec:MSV000085852:QC_0
What we can easily do is paste in the QC molecules and pull them out in one fell swoop:
271.0315;278.1902;279.0909;285.0205;311.0805;314.1381
You can try it out at this URL
Quickly Compare Multiple files
mzspec:MSV000085852:QC_0 mzspec:MSV000085852:QC_1 mzspec:MSV000085852:QC_2
271.0315;278.1902;279.0909;285.0205;311.0805;314.1381
We aim to provide several APIs to programmatically get data.
/mspreview?usi=<usi>
There are several ways to get GNPS Dashboard working locally, our preferred and recommended way is using docker/docker-compose as it provides a more consistent experience.
The initial steps are identical:
To get everything up and running, we've created a make target for you to get docker up and running:
make server-compose-interactive
The requirements on your local system are:
This will bring the server up on http://localhost:6548.
Example shell
# make sure to have Python3 installed via conda (preferably 3.8)
conda install -c conda-forge datashader
conda install -c conda-forge openjdk
# install requirements
pip install -r requirements.txt
# run or debug the GNPS Dashboard with Python 3 on http://localhost:5000
python ./app.py
# on problem, maybe install the following (tested on Windows 10 with WSL2 Ubuntu)
sudo apt-get install libffi-dev
Since we utilize a USI to find datasets, there are a limited number of locations we can grab data from. If you want to provide a new data source, you'll have to implement the following
download.py
, specifically in _resolve_usi_remotelink
to implement how to get the remote URL for your new USI. To run our unit tests,
cd test
pip install pytest
pip install pytest-xdist
pip install pytest-profiling
make all
Dash and plotly documentations
One major thing about production deployemnts is the DNS routing. You want to do the following steps to have everything route properly:
make server-compose-production