# MS1FA: Shiny app for the annotation of redundant features in untargeted metabolomics datasets

## TL;DR

MS1FA is a web-based tool that helps scientists annotate redundant features in metabolomics data using multiple approaches in one interactive platform.

## Contribution

MS1FA is the first web-based platform integrating multiple annotation methods for redundant features in metabolomics.

## Key findings

- MS1FA combines correlation-based grouping with MS2 data for reliable annotation of redundant features.
- The platform supports annotation using MS1 data only, MS2 data only, or both.
- MS1FA provides interactive exploration and download options for annotated feature tables.

## Abstract

Untargeted metabolomics, the comprehensive analysis of small molecules in biological systems, has become an invaluable tool for understanding physiology and metabolism. However, the annotation of metabolomic data is often confounded by the presence of redundant features, which can arise from e.g. multimerization, in-source fragments (ISFs), and adducts.

MS1FA uniquely integrates all major annotation approaches for redundant features within a single interactive platform. It combines correlation-based grouping with reliable ISF annotation using MS2 data and operates with MS1 data only, MS2 data only, or both. Additionally, it offers a distinctive method for grouping features based on relational criteria. As the only web-based platform with these capabilities, MS1FA provides easy access and allows users to explore and annotate the feature table interactively, with options to download the results.

MS1FA is freely accessible at https://ms1fa.helmholtz-hzi.de. The source code and data are available at https://github.com/RuibingS/MS1FA_RShiny_dashboard and are archived with the DOI 10.5281/zenodo.15118962.