Overview

Scripts used to run the experiments presented in the paper:

"Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification",

Eric Bach, Simon Rogers, John Williamson and Juho Rousu, 2020

Installation

All code was developed and tested in a Linux environment. Windows or MacOS are currently not officially supported. However, most of the code and installation procedure probably just works fine for those operating systems as well.

Requirements Packages

The code has been developed for Python >= 3.6 and the following packages are required in their specified minimum version:

• numpy >= 1.17
• scipy >= 1.3
• pandas >= 0.25.3
• scikit-learn>=0.22
• joblib >= 0.14
• matplotlib >= 3.1
• seaborn >= 0.9
• networkx >= 2.4
• setuptools >= 46.1

Install into a Virtual Environment

Clone the repository:

git clone https://github.com/aalto-ics-kepaco/msms_rt_score_integration.git

Change to the directory:

cd msms_rt_score_integration

Create a virtual Python environment and activate it:

virtualenv msmsrt_scorer_venv && source msmsrt_scorer_venv/bin/activate

Run the setup. All required packages will be fetched as well:

pip install .

Usage

An example how to reproduce the results can be found here.

Citation

Software citation:

To refer the original publication please use:

• For the general approach of combining prediction retention order with (tandem) mass spectrometry data for structure annotation

bibtex @article{10.1093/bioinformatics/btaa998, author = {Bach, Eric and Rogers, Simon and Williamson, John and Rousu, Juho}, title = "{Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification}", journal = {Bioinformatics}, year = {2020}, month = {11}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btaa998}, url = {https://doi.org/10.1093/bioinformatics/btaa998}, note = {btaa998}, eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa998/34557505/btaa998.pdf}, }

• For the actual msmsrt_scorer implementation

bibtex @software{Bach_msmsrt_scorer_Probabilistic_framework_2021, author = {Bach, Eric}, month = {11}, title = {{msmsrt\_scorer: Probabilistic framework for integration of mass spectrum and retention order information}}, url = {https://github.com/aalto-ics-kepaco/msms_rt_score_integration}, version = {0.2.3}, year = {2021} }