Chemical Classification

SIRIUS predicts the complete chemical class hierarchy of an unknown compound by assigning ClassyFire compound classes directly from the molecule’s predicted molecular fingerprint, requiring neither spectral nor structural reference data. This feature immediately provides contextual understanding even for novel or unannotated compounds. For full biological datasets, this feature provides a comprehensive overview of compound classes present in the sample and allows for comparisons between different cohorts at compound class level.

This archive showcases discoveries and major breakthroughs made by research groups worldwide using the chemical classification feature of SIRIUS.

Discoveries

Revealing the Hidden Burden of Offshore Discharges with SIRIUS

Offshore produced water discharges introduce over 240 billion liters of complex chemical mixtures into the marine environment annually. In a case study using produced water from North Sea offshore oil platforms, researchers demonstrated that traditional monitoring significantly underestimates the persistence of discharged chemicals. They introduce a novel analytical framework using advanced non-target analysis and computational workflows, including SIRIUS to highlight a significant “hidden burden” of persistent pollutants.

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Discoveries

Environmental and Maternal Drivers of the Infant Gut Metabolome Revealed Using SIRIUS

The maturation of the gut microbiome during the first six months of life is a foundational process, playing a key role in metabolic programming and training of the immune system, but these patterns remain critically underexplored in populations from low- and middle-income countries. To address this gap, an observational, longitudinal study profiled the developing gut microbiome and metabolome in Bangladesh using a multi-omics approach. Systematic classification and metabolite annotation was achieved using SIRIUS. The overall aim was to understand how maternal and environmental factors—specifically delivery mode, maternal milk composition, and household water treatment—shape this foundational postnatal development.

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Discoveries

SIRIUS in the Jena Experiment: It’s a Battle for Resources, Not a Break from Pests

In a biodiverse ecosystem, it’s often assumed that plants have safety in numbers—that a mix of species will confuse pests and dilute disease pressure, allowing individual plants to save energy on defences and focus on growth. But is this ecological truism always the case? A recent study from the long-running Jena Experiment uses untargeted metabolomics and SIRIUS for feature annotation and compound class prediction. They found that for many plants in a diverse community it is less about relaxing their defences and more about adapting to intense competition for light and nutrients.

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Discoveries

SIRIUS in Space: The ISS metabolome

As we prepare for longer human missions beyond Earth, understanding the invisible ecosystems of space habitats has become critical for astronaut health. The International Space Station (ISS) is not just a home and laboratory—it is also a closed microbial and chemical environment unlike anything on Earth. This study mapped the ISS microbiome and metabolome in unprecedented detail, uncovering its vast chemical “dark matter” using SIRIUS.

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Two hands full of soil.
Collaborations

Unlocking a Greater Perspective: Mapping the Chemical Space of Biomes Using SIRIUS

Untargeted mass spectrometry is a powerful tool for analyzing the immense chemical complexity of natural environments. However, interpreting such large datasets remains a significant challenge. To overcome this, researchers have developed an innovative approach using SIRIUS that prioritizes chemical profiling over exhaustive identification. This method allows for more effective comparisons of (micro-)biomes, providing deeper insights into biochemical diversity across different environments.

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Collaborations

Why Training Data Matters: Exploring Coverage Bias in Small Molecule Machine Learning

Machine learning is transforming analytical chemistry by enabling predictions of small molecule properties, crucial for drug development and other applications. However, ensuring reliable results requires careful selection of training data to avoid biases that can mislead models. Here, we explain why it was important to prepare high-quality training datasets for the machine learning methods in SIRIUS, especially given that many widely used datasets fail to evenly represent the diversity of biomolecular structures.

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Discoveries

a-MAIZE-ing: Sustainable Pest Control Investigated with SIRIUS

Agriculture has always been a dance with nature, requiring farmers to constantly adapt to changing conditions. One particularly promising method that has emerged over recent decades is push-pull technology, a strategy that uses nature’s own defenses to protect crops and boost yields. Using SIRIUS, researchers uncovered metabolites in push-pull maize that enhance its natural defense against pests.

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Discoveries

Thawing permafrost: Another step towards assessing the consequences

Thawing permafrost, caused by climate change, releases stored carbon into the atmosphere, accelerating global warming. The enzyme latch hypothesis suggests that low-oxygen conditions in wetlands slow down enzymatic polyphenol degradation and carbon release. But are oxygen-dependent phenol oxidases really the only enzymes that microbial communities have in their arsenal? Or should we perhaps take a closer (metatranscriptomic and metabolomic) look at the microbially catalysed carbon cycle?

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Microbial breakdown, facilitated by microorganisms like bacteria and fungi, plays a pivotal role in decomposing organic matter. (Image by Thomas Breher on Pixabay)
Discoveries

SIRIUS on the body farm: Investigating microbial decomposers

Microbial decomposers break down human remains, recycling nutrients and influencing ecosystem dynamics. Is there a universal microbial decomposer network that assembles in response to mammalian remains? How does the network and the cadaver-derived nutrient pool change during the decomposition process and can this microbial community change be used for predicting time since death for forensic purposes?

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