Dissolved organic matter (DOM) is an ultracomplex mixture that plays a central role in global biogeochemical cycles. Despite its importance, DOM remains poorly understood at the molecular level. Over ...

A global analysis of more than 2,300 seawater samples found that largely unmonitored industrial compounds are widespread across oceans and may be changing crucial biological and carbon cycling process...

The viral shunt is a fundamental ecosystem process which diverts the flux of organic carbon fixed through photosynthesis during algal bloom events ...

Abstract. Bacterial–fungal interactions have traditionally been attributed to secondary metabolites, but the role of the bacterial extracellular matrix in

High-quality data preprocessing is essential for untargeted metabolomics experiments, where increasing data set scale and complexity demand adaptable, robust, and reproducible software solutions. Modern preprocessing tools must evolve to integrate seamlessly with downstream analysis platforms, ensuring efficient and streamlined workflows. Since its introduction in 2005, the xcms R package has become one of the most widely used tools for LC-MS data preprocessing. Developed through an open-source, community-driven approach, xcms maintains long-term stability while continuously expanding its capabilities and accessibility. We present recent advancements that position xcms as a central component of a modular and interoperable software ecosystem for metabolomics data analysis. Key improvements include enhanced scalability, enabling the processing of large-scale experiments with thousands of samples on standard computing hardware. These developments empower users to build comprehensive, customizable, and reproducible workflows tailored to diverse experimental designs and analytical needs. An expanding collection of tutorials, documentation, and teaching materials further supports both new and experienced users in leveraging broader R and Bioconductor ecosystems. These resources facilitate the integration of statistical modeling, visualization tools, and domain-specific packages, extending the reach and impact of xcms workflows. Together, these enhancements solidify xcms as a cornerstone of modern metabolomics research.

Culture-independent metagenomic approaches have proven to be effective tools for identifying previously hidden biosynthetic gene clusters (BGCs) encoding novel natural products with potential medical relevance. However, producing these compounds remains challenging as metagenomic BGCs often originate from organisms phylogenetically distant from available heterologous hosts. Lasso peptides, a subclass of ribosomally synthesized and post-translationally modified peptide (RiPP) natural products, exhibit diverse bioactivities, yet no lasso peptide has previously been discovered directly from a metagenome. Here, we report the discovery and heterologous expression of the first soil metagenome-derived lasso peptide. Expression of its biosynthetic gene cluster in Escherichia coli, followed by mass spectrometry analysis, strongly supported the predicted amino acid sequence and lasso structure of the peptide. Notably, this lasso peptide is the first to feature asparagine as the ring-forming residue at position one. Taxonomic analysis of the corresponding BGC identified an uncultivated member of the Steroidobacterales family (Gammaproteobacteria) as the closest known relative of the potential native host. These findings underscore the potential of metagenomic genome mining to reveal structurally novel RiPPs and to expand our understanding of the natural diversity of lasso peptides.

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

Courtesy of UC Riverside, biologists and chemists have a new programming language to uncover previously unknown environmental pollutants and other information at breakneck speed – without requiring th...

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.

Introduction The identification of lipids is a cornerstone of lipidomics, and due to the specific characteristics of lipids, it requires dedicated analysis workflows. Identifying novel lipids and lipi...