xi-mzidentml-converter

xi-mzidentml-converter uses pyteomics (https://pyteomics.readthedocs.io/en/latest/index.html) to parse mzIdentML files (v1.2.0) and extract crosslink information. Results are written to a relational database (PostgreSQL or SQLite) using sqlalchemy.

Requirements:

python3.10

pipenv

sqlite3 or postgresql

These instructions use postgresql, it is recommended way. We recommend using postgres 13 or higher.

They assume you're using a linux system. If you're using a different system, you'll need to adapt the instructions.

1. create a postgresql role and database to use

With PostGreSQL installed, create a database and a user role to access it:

sudo su postgres
psql
create database xiview;
create user xiadmin with login password 'your_password_here';
grant all privileges on database xiview to xiadmin;

find the hba.conf file in the postgresql installation directory and add a line to allow the xiadmin role to access the database: e.g.

sudo nano /etc/postgresql/13/main/pg_hba.conf

then add the line: local xiview xiadmin md5

then restart postgresql:

sudo service postgresql restart

2. Installation

Clone git repository :

git clone https://github.com/Rappsilber-Laboratory/xi-mzidentml-converter.git

cd into the repository:

cd xi-mzidentml-converter

copy the file ./default.database.ini to ./database.ini and edit te [postgresql] section to conatin the login details for your database. E.g. so its content is:

[postgresql]
host=localhost
database=xiview
user=xiadmin
password=your_password_here
port=5432

Set up the python environment:

pipenv install --python 3.10

3. create the database schema

run create_db_schema.py to create the database tables:

python create_db_schema.py

4. parse an mzIdentML file into the database

parse a test dataset:

python process_dataset.py -p PXD038060

The above command downloads a dataset based on its ProteomeExchange accession and parses it.

You will more likely want to parse data from a local directory containing both te mzIdentML and the peaklists. To do this use the command in the form:

python process_dataset.py -d /path/to/directory -i projectidentifer

The argument -d is the directory to read files from and -i is the project identifier to use in the database. (Project identifier is like a grouping of identiifcation files).

process_dataset.py -h

will show you all the options.

5. start the API

An API to the crosslinking data is currently contained in this project. This is part of an ongoing collaboration with PRIDE and it will be moved to a separate repository in the near future.

At the moment, start it from here -- the xiVIEW visualisation (https://github.com/Rappsilber-Laboratory/xiview-server) will load the data from it:

python -m uvicorn app.api:app --reload --port 8081

this will start the API on port 8081 of localhost. The xiVIEW visualisation will look for it there by default.

You can browser the experimental, work-in-progess API at http://127.0.0.1:8081/pride/archive/xiview/ws/docs#/

You should now have created the database, parsed a dataset into it, and start te API used to access the data. You are now ready to start the xiVIEW visualisation, see https://github.com/Rappsilber-Laboratory/xiview-server.

To run tests

Make sure we have the right db user available

psql -p 5432 -c "create role ximzid_unittests with password 'ximzid_unittests';"
psql -p 5432 -c 'alter role ximzid_unittests with login;'
psql -p 5432 -c 'alter role ximzid_unittests with createdb;'
psql -p 5432 -c 'GRANT pg_signal_backend TO ximzid_unittests;'

run the tests

pipenv run pytest