News
The Dark Metabolome Debate
Mass spectrometry does produce different ion forms, but claiming the metabolome is mostly known is misleading. Analysis LC-MS data of 30,000 chemical standards found fewer in-source fragments than expected, suggesting they’re not as prevalent in biological samples as some argue. Even within a well-studied US National Institute of Standards and Technology (NIST) human fecal reference standard dataset, a staggering 82% of molecules lacked annotations for any ion form when carefully grouped by retention time, MS/MS data, and peak shape. This compelling finding underscores that the dark metabolome remains a significant frontier for discovery, holding countless unannotated features that could potentially represent novel molecules or provide crucial biochemical insights.
#SIRIUSDiscoveries
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.
Sponsor for Metabolomics 2025
We’re proud to announce that we are a START-UP SPONSOR for Metabolomics 2025, the 21st Annual International Conference of the Metabolomics Society, taking place June 22-26, 2025, in Prague, Czech Republic!
This premier global event brings together world-renowned experts, researchers, and industry leaders to explore the latest advancements in metabolomics. From groundbreaking science in health, disease, food, and the environment to cutting-edge technologies shaping the future, Metabolomics 2025 is the place to be!
We are committed to driving innovation in metabolomics and look forward to connecting with fellow researchers, entrepreneurs, and industry leaders.
See you in Prague!
Join us at Pittcon 2025
Pittcon is a premier global conference for laboratory science, bringing together experts in analytical chemistry, spectroscopy, and scientific instrumentation.
Attendees will gain insights into how AI-powered tools, like SIRIUS, can autonomously process mass spectrometry data, generate accurate mass formulas, and predict chemical structures, significantly enhancing analytical workflows. Learn how SIRIUS is transforming the way we identify and classify unknown compounds—without the need for extensive reference libraries.
Attendees will gain insights into how AI-powered tools, like SIRIUS, can autonomously process mass spectrometry data, generate accurate mass formulas, and predict chemical structures, significantly enhancing analytical workflows. Learn how SIRIUS is transforming the way we identify and classify unknown compounds—without the need for extensive reference libraries.
DiagnosTech Pitch
Beim nächsten DiagnosTech Pitch des InfectoGnostics Research Campus Jena geben wir Einblicke in unsere Software SIRIUS.
Jetzt anmelden und mehr über die Zukunft der Molekülanalyse lernen.
Join Us at Agilent Forum Analytik
Don't miss the opportunity to attend Martin Hoffmann's talk about small molecule identification with SIRIUS.
The program promises an exciting lineup of lectures on key topics like microplastics, green energy, food and environmental analysis, and (bio-)pharma.
Martin will be available on both days to answer your questions and engage in discussions.
Attendance is free, but spots are limited!
The program promises an exciting lineup of lectures on key topics like microplastics, green energy, food and environmental analysis, and (bio-)pharma.
Martin will be available on both days to answer your questions and engage in discussions.
Attendance is free, but spots are limited!
Why Training Data Matters
Why is high-quality training data for machine learning important?
Machine learning is transforming the way we approach problems in analytical chemistry. But there’s a catch: ensuring reliable results requires careful selection of training data to avoid biases that can mislead models. We explain ✨ why high-quality training datasets are important for SIRIUS method development ✨ why representing the full "universe" of small molecules is crucial ✨ how widely used datasets fail to evenly represent the diversity of biomolecular structures ✨ which tools can help evaluating dataset quality.
Machine learning is transforming the way we approach problems in analytical chemistry. But there’s a catch: ensuring reliable results requires careful selection of training data to avoid biases that can mislead models. We explain ✨ why high-quality training datasets are important for SIRIUS method development ✨ why representing the full "universe" of small molecules is crucial ✨ how widely used datasets fail to evenly represent the diversity of biomolecular structures ✨ which tools can help evaluating dataset quality.
Follow us on Bluesky!
🦋 You can now find us on Bluesky! 🦋
We’re excited to connect with you on this growing platform.
See you there!
SIRIUS 6.1.0 is here!
We’re excited to announce the latest version of SIRIUS designed to improve your small molecule analysis workflow with a refreshed interface, streamlined processes, and several important fixes that ensure smoother performance and better data handling.
✨ Key Highlights: 🌈 New Color Scheme: A consistent and intuitive look throughout the entire identification process. ⚡ Streamlined Workflows: Tools are now automatically activated/deactivated to comply with SIRIUS workflow principles. You can also easily save and reload computation settings with our new preset function. 🔍 Detect More Features: LC-MS/MS preprocessing with improved feature detection, adduct assignment and better sensitivity. Automated quality assignment helps you not to drown in the masses of features. 🏠 Welcome Page Redesign: Get a quick overview of your account, connection details, and helpful resources to make the most of SIRIUS. 📄 Improved Result Views: Numerous enhancements and fixes across result views for a smoother analysis experience.
✨ Key Highlights: 🌈 New Color Scheme: A consistent and intuitive look throughout the entire identification process. ⚡ Streamlined Workflows: Tools are now automatically activated/deactivated to comply with SIRIUS workflow principles. You can also easily save and reload computation settings with our new preset function. 🔍 Detect More Features: LC-MS/MS preprocessing with improved feature detection, adduct assignment and better sensitivity. Automated quality assignment helps you not to drown in the masses of features. 🏠 Welcome Page Redesign: Get a quick overview of your account, connection details, and helpful resources to make the most of SIRIUS. 📄 Improved Result Views: Numerous enhancements and fixes across result views for a smoother analysis experience.
#SIRIUSDiscoveries
The rise of drug-resistant fungal infections shows the need for better antifungal drug discovery methods. A research team led by Tim Bugni at the University of Wisconsin-Madison has developed a screening platform to discover antifungal drugs from natural sources. They combine liquid chromatography-tandem mass spectrometry (LC-MS/MS) for structural identification with yeast chemical genomics (YCG) for mechanism-of-action analysis and use SIRIUS to predict the structure of the antifungal agents.
💡 This combined approach efficiently identifies promising antifungal compounds while filtering out known or undesirable ones, significantly accelerating the drug discovery process.
#SIRIUSDiscoveries
Boosting Crop Resilience: Sustainable farming methods can naturally protect crops from pests while boosting yields. Push-pull technology is a powerful technique that uses plants to create a natural pest defense around maize fields.
Researchers have done a large-scale field study in three African countries to identify metabolites in push-pull maize that enhance its natural defense against pests. Using SIRIUS, they identified two benzoxazinoid glycosides that suggest that push-pull technology could bolster maize’s natural defenses through biochemical pathways enhanced by intercropping.
#SIRIUSDIscoveries
With the growing complexity of production processes and widespread use of synthetic polymers, plastic-related contamination in food and drinks is becoming a critical concern. A non-targeted approach to analysis is essential for uncovering both known and unknown contaminants in our drinks.
Researchers at McGill University, Canada, developed a non-targeted approach for investigating suspected and unanticipated chemicals in a variety of 37 liquor samples. By analysing fragmentation patterns, SIRIUS predicts chemical structures of unknowns to identify unexpected contaminants and ensure safety of our food.
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#SIRIUSDiscoveries
Black apples result from late-stage microbial decomposition, mainly by Monilinia fructigena and Penicillium expansum, after falling to the ground. These fungi produce various secondary metabolites, some with antifungal properties, affecting interactions and control of other microbes. However, fungal secondary metabolites in apples are not well understood.
A research team identified 3,319 unique chemical features in black apples, with only 6.4% being known compounds. They used SIRIUS and ZODIAC to predict molecular formulae, CSI:FingerID to annotate structures, and CANOPUS to predict compound classes.
#SIRIUSDiscoveries
Defense syndromes are combinations of chemical and physical traits plants develop to protect against herbivores, working better together than alone, with high trait variability among species enhancing defenses by reducing shared herbivores. Willows, with their diverse chemical defenses and specialized herbivore interactions, are ideal for studying these syndromes. Researchers analyzed several lowland willow species using advanced chemical profiling techniques together with CSI:FingerID and CANOPUS and found that trait syndromes result from complex selection pressures, trade-offs, and herbivory, leading to chemical differentiation and niche segregation.
#SIRIUSDiscoveries
Gut microbiota's lipids play a key role in health, affecting immune responses and gut inflammation, with diverse structures and functions. Targeted and non-targeted lipidomics, combined with LC-MS, are effective for lipid analysis. Researchers tested five different reversed-phase LC columns and eight different mobile phase conditions to improve coverage of intestinal lipids, using CANOPUS and CSI:FingerID for lipid classification and the prediction of known and unknown features. They found that hybrid surface technology improves chromatographic parameters for lipid analysis, and ammonium acetate in ESI(−) or ammonium formate in ESI(+) increase lipid detection.
#SIRIUSDiscoveries
Studying how bacteria adjust their lipid composition under different environmental and nutrient stresses is crucial for understanding their survival and can inform the design of synthetic membranes for applications like filtration and drug development.
In a study of Desulfatibacillum alkenivorans, researchers found that temperature changes and phosphorus deficiency influenced lipid diversity. Advanced analytical methods identified nearly 400 different lipids, revealing how these factors affected lipid composition. MS2 data analysis using Feature-Based Molecular Networking and SIRIUS showed that phosphorus limitation led to the creation of new lipids, such as glucuronosyl glycerols and butyramide cysteine glycerols, indicating a new bacterial survival strategy.
#SIRIUSFacts
Ambient ionization is a form of ionization in which ions are formed in an ion source outside the mass spectrometer without sample preparation or chromatographic separation. Chain Electrospray Ionization is a method developed to ionize tiny amounts of samples that uses a primary ion source to trigger a secondary electrospray ionization with very low sample consumption (in picoliters per minute). Missing chromatographic separationleads to a higher presence of co-eluting components and more intricate peaks in MS2. Consequently, comparing experimental MS2 spectra with standard spectra in the database becomes more challenging due to increased interferences. Sirius exlcudes spurious peak interference when calculating fragmentation tree information, making it suitable for deciphering the more complex MS2 spectra produced by ambient mass spectrometry.
#SIRIUSDiscoveries
Fusarium graminearum, a fungal plant pathogen, affects cereal crops leading to significant economic losses due to reduced yields and contamination of grains with harmful mycotoxins. Synthetic fungicides can harm non-target organisms and promote fungal resistance. Natural products like plant extracts have shown antimicrobial properties against mycotoxin production.
This study explores natural alternatives to chemical fungicides, focusing on grapevine byproducts rich in phenolic compounds, especially stilbenes. Using UHPLC–HRMS/MS analysis and metabolomics, they identified potent antifungal compounds, including several oligomeric stilbenes. Despite challenges in annotating stilbenes due to limited standards and databases, tools like CSI:FingerID and CANOPUS facilitated the dereplication process. Four significant oligomeric stilbenes were identified: cyphostemmin B, isohopeaphenol, another stilbene tetramer, and a trimer putatively annotated as vaticanol G.
#SIRIUS Discoveries
Cyanobacteria are found worldwide living in various environments like freshwater, land, and sea. Cyanobacterial blooms in freshwater sources harm water quality by reducing oxygen levels and releasing harmful toxins. These toxins, known as cyano-metabolites, can enter water treatment plants if not treated properly, potentially contaminating drinking water.
Researchers at Eawag developed a method for efficient high-throughput screening of how different cyano-metabolites interact with ozone during water treatment. Since no spectral libraries exist for most cyano-metabolites, they used SIRIUS and MetFrag for compound annotation. Studying the fate of 31 cyano-metabolites, they found that some may be effectively removed by ozonation, while others may require hydroxyl radicals for degradation.
