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MetaboCrates

MetaboCrates is designed for early analysis of data obtained from targeted metabolomics, e.g., Biocrates® kits. It simplifies and streamlines the data processing workflow, allowing you to efficiently analyze metabolite data. Here’s a brief overview of its key features:

Features

1. Data Import: Easily import metabolomics data generated from Biocrates® platforms.

2. Preprocessing: Perform data preprocessing tasks such as metabolites selection and <LOD imputation.

  • Automatically identify and remove metabolites with a high Limit of Detection (LOD) proportion.
  • Enhance the accuracy of your data by eliminating unreliable measurements.
  • Complete missing data points for metabolites based on LOD values.
  • Ensure that your dataset is comprehensive and suitable for analysis.

3. Quality Control: Quality control checks on your data.

4. Analysis: Calculate descriptive statistics.

5. Save your work: Save your progress, allowing you to resume your analysis at a later time.

  • Track and manage multiple projects effortlessly.
  • Easily share your findings with colleagues or collaborators.

Web server

The MetaboCrates web server can be accessed through our web server.

Installation

To install MetaboCrates you need to have R version >= 4.2.0.

devtools::install_github("BioGenies/MetaboCrates")

Reproducibility

To reproduce our environment you need to git clone our repo and activate renv.

git clone https://github.com/BioGenies/metaborates.git
renv::activate()
renv::restore()

Run MetaboCrates

To run MetaboCrates type the following command into an R console.

MetaboCrates::MetaboCrates_gui()

How to cite?

Krystyna Grzesiak, Joanna Pokora, Jarosław Chilimoniuk, Adrian Godlewski, Mariia Solovianova, Rafał Kolenda, Adam Krętowski, Michał Ciborowski, Michał Burdukiewicz (2025). MetaboCrates :An open-source pipeline for quality-aware analysis of targeted metabolomics data.

Contact

If you have any questions, suggestions or comments, contact Michal Burdukiewicz.

Funding and acknowledgements

We want to thank the Clinical Research Centre (Medical University of Białystok) members for fruitful discussions. K.G. wants to acknowledge grant no. 2021/43/O/ST6/02805 (National Science Centre). J. P. and M. B. wants to acknowledge grant no. 2023/51/D/NZ7/02847 (National Science Centre). We also acknowledge the Center for Artificial Intelligence at the Medical University of Białystok (funded by the Ministry of Health of the Republic of Poland).