News

Unraveling the complex structure of lignin with SIRIUS

Despite being one of Earth’s most abundant polymeric organic compounds, lignin is often considered a lower-value byproduct in industrial processes. Converting lignin into valuable chemicals or biomaterials requires a thorough structural characterisation of depolymerised products. This non-targeted analysis method involving 2D liquid chromatography and high-resolution tandem mass spectrometry uses SIRIUS in versatile ways to unravel the complex structures of depolymerized lignin.

Read the full blog post.

E. Tammekivi et al. Anal. Chim. Acta (2024) doi: 10.1016/j.aca.2023.342157

Weltoffenes Thüringen

Das gesamte Team von Bright Giant steht hinter den Grundwerten einer offenen und toleranten Gesellschaft. Deshalb bekennen wir uns als Thüringer Unternehmen klar zu einer weltoffenen, vielfältigen und toleranten Unternehmenskultur.

Unser Ziel ist ein respektvolles und integratives Miteinander, frei von Vorurteilen und Hass, unter Achtung der Menschenrechte und der Menschenwürde in einer pluralistischen Demokratie und Rechtsstaatlichkeit. Diese Grundwerte sind unabdingbar für vielfältige Forschung und Innovation und für eine erfolgreiche globale Zusammenarbeit.

Vor diesem Hintergrund unterstützen wir gemeinsam mit vielen anderen die Initiative “Weltoffenes Thüringen”.

Computational Mass Spectrometry Workshop

Gain expertise in non-targeted LC-MS metabolomics data processing. Computational Mass Spectrometry Workshop for beginners, expert users and software developers. Hands-on non-target MS data processing experience using MZmine, SIRIUS, GNPS, MATCHMS, The LOTUS Initiative, and many more.

The SIRIUS lecture will be held by Marcus Ludwig and Sebastian Böcker.

Eco-Metabolomics Workshop

International experts will guide you through the intricacies of developing comprehensive metabolomics studies tailored for ecological interactions from experimental design to compound annotation.

The SIRIUS lecture will be held by Marcus Ludwig.

Top 50: AI in health and life sciences

CANOPUS, our tool to predict the complete chemical class hierarchy of an unknown compound by assigning ClassyFire compound classes, is among the top 50 cited articles on AI in health and life sciences.

B. S. Glicksberg et al. Applied Sciences (2024) 10.3390/app14020785

The conceptual leap of SIRIUS structure annotation

Already over 30 years ago, Curry and Rumelhart introduced one of the first machine learning approaches to predict the presence or absence of key chemical substructures from tandem mass spectra. There are noteworthy conceptual similarities between their approach and SIRIUS structure annotation (CSI:FIngerID).
What they did not see was that a large number of weakly accurate predictions can still allow molecules to be unambiguously identified – a crucial conceptual leap that makes CSIFingerID the state-of-the-art method currently available for identifying small molecules from their MS/MS spectra.

M. A. Skinnider. Nat Rev Chem (2024) doi: 10.1038/s41570-023-00570-2

Chemical classes of exometabolites investigated with SIRIUS

Examining seawater presents an enduring challenge due to the complexity of molecules present in trace amounts and their dynamic nature. The lowest ecological region of the sea is inhabited by holobionts, such as sponges, which significantly shape the marine chemical landscape through the release of diverse exometabolites. In addressing the need to capture these molecules immediately after release, a novel underwater device was developed, allowing in situ collection and enrichment without harming organisms. To test the device, researchers investigated exometabolites of sponges in the Mediterranean sea using untargeted mass spectrometry and SIRIUS to understand the chemical class distribution. This approach holds promise for studying endangered species in marine protected areas, assessing seasonal variations in exometabolite production, and monitoring toxins or human impacts in the marine environment.

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M. Mauduit et al. ACS Cent. Sci. (2023) doi: 10.1021/acscentsci.3c00661