Phylogenetic analysis is widely used to predict enzyme function, yet building annotated and reusable trees is labor-intensive and requires extensive knowledge about the specific enzymes. Existing reso...

To improve annotation in non-targeted metabolomics studies, authors develop a Multiplexed Chemical Metabolomics (MCheM) platform, combining post-column derivatization with integrated data processing. ...

The members of CMFI can breathe a sigh of relief: On January 1, 2026, the Tübingen Cluster of Excellence will enter its second funding period with a duration of seven years. This was announced by the ...

Abstract. Accurate and accessible phylogenetic analysis is essential for understanding microbial taxonomy and evolution, which are integral to microbiology

YouTube video by Michael T Marty

The clustering of tandem mass spectra (MS/MS) is a crucial computational step to deduplicate repeated acquisitions in data-dependent experiments. This technique is essential in untargeted metabolomics, particularly with high-throughput mass spectrometers capable of generating hundreds of MS/MS spectra per second. Despite advancements in MS/MS clustering algorithms in proteomics, their performance in metabolomics has not been extensively evaluated due to the lack of database search tools with false discovery rate control for molecule identification. To bridge this gap, this study introduces the MS1-retention time (MS-RT) method to assess MS/MS clustering performance in metabolomics data sets. Here, we validate MS-RT by comparing MS-RT to established proteomics clustering evaluation approaches that utilize database search identifications. Additionally, we evaluate the performance of several MS/MS clustering tools on metabolomics data sets, highlighting their advantages and drawbacks. This MS-RT method and the MS/MS clustering tool benchmarking will provide valuable real world practical recommendations for tools and set the stage for future advancements in metabolomics MS/MS clustering.