Welcome to Metabonaut! 🧑‍🚀

Metabonaut presents a series of workflows based on a small LC-MS/MS dataset, leveraging R and Bioconductor packages. These workflows demonstrate how to adapt various algorithms to specific datasets and seamlessly integrate R packages for efficient, reproducible data processing.

Current Vignettes

1. Complete End-to-End LC-MS/MS Metabolomic Data Analysis

This primary workflow walks through every step of the analysis, from preprocessing raw data to statistical analysis and annotation of potential metabolites of interest.

2. Dataset Investigation

What should you do when you first receive your data? This vignette highlights key considerations before diving into the analysis. Investing time in preparation can save significant troubleshooting later.

3. Seamless Alignment: Merging New Data with an Existing Preprocessed Dataset

Learn how to use a newly implemented, flexible alignment algorithm to integrate new datasets with previously preprocessed ones based on shared features of interest.

4. LC-MS/MS Data Annotation using R and Python [NEW]

Explore the SpectriPy package for LC-MS/MS data annotation. This tutorial demonstrates how to combining the strengths of Python and R MS libraries for annotation.

We prioritize reproducibility. These workflows are designed to remain stable over time, allowing you to run all vignettes together as one comprehensive super-vignette. Any major changes will be clearly documented.

Acknowledgment

The work has been Funded by the European Union under the HORIZON-MSCA-2021 project 101073062 : HUMAN – Harmonising and unifying blood metabolic analysis networks.