Using ppx


By default, ppx will download project files in the .ppx directory under the current user’s home directory (~/.ppx on Linux and MacOS). There are several ways to specify different data directories:

  1. Change the ppx data directory for all future Python sessions by setting the PPX_DATA_DIR environment variable to your preferred directory.

  2. Change the ppx data directory for a Python session using the ppx.set_data_dir() function.

  3. Specify a data directory for a project using the local argument:

    >>> import ppx
    >>> proj = ppx.find_project("PXD000001", local="my/data/dir")

Why does ppx set a default data directory? We found that this makes it easier to reuse the same proteomics data files in multiple tasks that we’re working on.

As of ppx v1.3.0, cloud paths can also be used as the data directory. This allows you to stream downloaded files to AWS S3, Google Cloud Storage, or Azure Blob Storage. To use a cloud storage provider, simply set the data directory to a cloud URI, such as s3://my-data-bucket/ppx using any of the methods above. Please note that you’ll also need to setup credentials for your cloud provider—see the CloudPathLib documentation for details.


To begin, we first import the ppx package:

>>> import ppx

We can now find a project using its ProteomeXchange or MassIVE identifier. Note that ppx currently only supports projects hosted on PRIDE and MassIVE. For this example, we’ll use a project from PRIDE:

>>> proj = ppx.find_project("PXD000001")

Here, proj is a is PrideProject object with methods that let us explore the available files and download files that we select. Let’s retrieve a list of all of the files associated with this project on PRIDE:

>>> remote_files = proj.remote_files()
>>> print(remote_files)
['F063721.dat', 'F063721.dat-mztab.txt', 'PRIDE_Exp_Complete_Ac_22134.xml.gz', 'PRIDE_Exp_mzData_Ac_22134.xml.gz', 'PXD000001_mztab.txt', 'README.txt', 'TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML', 'TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzXML', 'TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzXML', 'TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.raw', 'erwinia_carotovora.fasta', 'generated/PRIDE_Exp_Complete_Ac_22134.pride.mgf.gz', 'generated/PRIDE_Exp_Complete_Ac_22134.pride.mztab.gz']

Alternatively, we can glob for specific files of interest:

>>> mzml_files = proj.remote_files("*.mzML")
>>> print(mzml_files)

Once we’ve determined what file we desire to download, we can download them to our local data directory. In this case, that is ~/.ppx/PXD000001:

>>> downloaded ="F063721.dat-mztab.txt")
>>> print(downloaded)

Once we’ve downloaded files, ppx no longer needs an internet connection to retrieve a project’s local data. However, you will need to specify the repository in which the project data resides. If we start a new Python session, we can find our previous files easily:

>>> import ppx
>>> proj = ppx.find_project("PXD000001", repo="PRIDE")
>>> local_files = proj.local_files()
>>> print(local_files)

For more details about the available methods for a project, see our Python API documentation for the PrideProject and MassiveProject classes.

Using Cloud Storage

We use CloudPathlib to power support for AWS S3, Google Cloud Storage, and Azure Blob Storage. To use a cloud storage provider, create the bucket for ppx to use and set it as the ppx data directory.

For example using AWS S3, we can save the files of a project to an S3 bucket:

>>> proj = ppx.find_project("PXD000001", local="s3://my-bucket/PXD000001")

CloudPathLib then provides methods to download files from S3 when you need them:

>>> readme_on_s3 = proj.local_files("README.txt")[0]
>>> readme_on_s3.download_to("README.txt")