rustims

rustims is a framework developed for processing raw data from Ion-Mobility Spectrometry (IMS) in proteomics mass spectrometry. It draws inspiration from OpenMS but is distinguished by its use of Rust as the backend language, aiming for efficient algorithm implementations and robust data structures. Like OpenMS, rustims exposes most of its logic to Python, here via PyO3. This setup is intended to enable easy pick-up, quick prototyping, and integration into existing Python-centric scientific workflows.

rustims is about exploring and improving the way we process ion-mobility spectrometry data, providing re-usable building blocks that can be extensively configured. It's a work in progress, reflecting the open-source ethos of collaboration, engagement, and sharing of knowledge. Whether you're here to contribute or learn, we welcome your interest!

Quickstart

To quickly get started, we recommend installing the Python package imspy, the high-level Python API designed for most users to interact with rustims functionality, via pip into a separate Virtual Environment using Python3.11 (currently the only supported Python version due to TensorFlow). If you don't know how to create a Virtual Environment, you can follow the instructions here. This way, you can avoid potential dependency conflicts with other Python packages. The following command installs the latest version of imspy from PyPi:

pip install imspy

This will install tensorflow as a dependency without GPU support. The easiest way to get GPU support is to additionally install the tensorflow[and-cuda] package:

pip install tensorflow[and-cuda]==2.15.*

Which comes with the necessary CUDA and cuDNN libraries. Have a look at the imspy-README to learn about basic functionalities of the package.

Repository Structure

The rustims project architecture is designed around two core Rust crates: mscore and rustdf. These crates are the foundation of the project, housing the in-memory data structures, algorithms, and input/output functionalities specifically for TDF files. These Rust components are seamlessly integrated with Python through pyO3, which allows the main functionalities of mscore and rustdf to be accessible in Python by compiling them into a single, installable Python wheel named imspy_connector. On top of this, imspy is a native Python package that not only interfaces with the Rust crates for enhanced performance but also introduces additional logic, such as TensorFlow models for ion-mobility prediction, thereby combining the strengths of Rust and Python in one cohesive framework.

Analyzing a DDA dataset from Bruker timsTOF with imspy_dda

You can directly run the imspy_dda command to analyze a DDA dataset:

imspy_dda path/to/bruker.tdf path/to/proteome.fasta

The tool has a lot of options, which you can explore by running:

imspy_dda --help

Dive into processing of timsTOF DDA data with jupyter notebooks

We are now providing jupyter notebook examples that allow you to interactively learn about the functionality of our tooling. You can also checkout the sagepy notebook examples which are hosted in a separate repository since sagepy is not limited to DB searching of timsTOF data.

Read the docs

The codbease of all native rust crates and python packages is now available:

• rustdf
• mscore
• rustms
• imspy

Generating a synthetic PASEF-like dataset with timsim

You can also generate a synthetic PASEF-like dataset using the following command (timsim currently requires a reference.tdf file of a real dataset):

timsim path/to/output.tdf path/to/reference.tdf path/to/proteome.fasta

The tool has a lot of options, which you can explore by running:

timsim --help

Rust backend: mscore and rustdf

There are two Rrust projects: mscore and rustdf. The former is a library that contains implementations of in-memory data structures and algorithms for raw-data processing. The latter contains a Rust-native reader and writer of TDF, the serialization format written by Bruker timsTOF devices. It also contains the implementation of the I/O logic needed for synthetic timsTOF PASEF-like in-silico dataset generation.

Python bindings: imspy_connector

The imspy_connector module bridges Rust code with Python, allowing Rust components to be used in Python with minimal dependencies. This setup keeps the system lightweight for Python users but introduces complexity, especially in development and debugging. Changes in Rust need to be reflected in Python, often requiring updates in multiple places. Despite the added complexity, this architecture is chosen for its benefits. It allows for parts of the code in Rust or Python that don't interact with the other language to be developed independently and asynchronously. However, this flexibility is limited to components that do not require cross-language access.

Python package: imspy

imspy is a Python package designed for end-users. It utilizes imspy_connector for accessing Rust functionalities exposed via pyO3, incorporating additional libraries like tensorflow, scikit-learn, and sagepy. This setup enables users to perform detailed tasks such as calculating peptide fragment ions, analyzing isotope patterns, studying quadrupole transmission, and applying deep learning to ion mobility and retention time predictions.

Julia bindings

Julia support is currently experimental. Julia interfaces via imsjl_connector, FFI.

Installation

Install via pip

We are now providing stable versions of the python-bound components via Python wheels on PyPi. We recommend that you use a Python virtual environment with python3.11, since imspy has some heavy weight dependencies like tensorflow, numpy, and numba, where version mismatches can lead to potential issues.

pip install imspy

Build from source

Rust backend

Assuming a rust is installed on your system and you cloned this repository, the build process currently looks like this (example for mscore):

cd rustims/mscore && cargo build --release

Python bindings

Assuming a rust and Python (==3.11) version is installed on your system, the build process currently looks like this:

1. The Python connector imspy_connector needs to be built by Maturin. Maturin can be installed via pip:

   pip install maturin[patchelf]

2. Once Maturin is installed navigate to the imspy_connector folder and run:

   maturin build --release

   This generates a .whl file that can be installed by pip.
3. Install the generated .whl file:

   pip install --force-reinstall ./target/wheels/[FILE_NAME].whl

   The --force-reinstall flag ensures that pip is overwriting old installations of the bindings. This is relevant when you make changes in the rust backend code (i.e. the bindings themselves, mscore or rustdf).

Julia bindings

Julia support is currently experimental.

Python package

The Python library is installed via Poetry.

1. Poetry can be installed via pip:

   pip install poetry

2. Navigate to the imspy folder and install it with Poetry.

   poetry install