Bringing structure to your spectra
Profound algorithms and state-of-the-art machine learning meet enterprise software solutions for your metabolomics MS/MS analysis.
How it works
Solutions
Standard SaaS offering
Access to our CSI:FingerID+CANOPUS hosting:
- get started immediately
- CPU cores and infrastructure are shared with other users
- scalable computing resources
- limited or unlimited number of computations
- your data will be processed on our servers
SaaS offering with dedicated resources
Get your own CSI:FingerID+CANOPUS hosting with the computational power that fits your needs:
- get started almost immediately
- private CPU cores and infrastructure exclusively for your jobs.
- scalable computing resources
- your data will be processed on our servers
Customized solutions
Get your personalized structure elucidation workflow including:
- CSI:FingerID trained on your reference spectral libraries
- custom fingerprint models
- custom structure databases
- custom tailored workflows and reporting
- hosting in your own cloud infrastructure
For further information, trial versions and detailed offerings and pricing:
What is SIRIUS and CSI:FIngerID+CANOPUS?
SIRIUS together with CSI:FingerID and CANOPUS greatly facilitate structure elucidation of small molecules — such as metabolites — measured by tandem mass spectrometry. All are based on cutting edge academical research and offer superb performance.
SIRIUS is an open-source software which annotates measured compounds and their corresponding fragments with molecular formulas. This is done with statistically profound combinatorial optimization. CSI:FingerID and CANOPUS are seamlessly integrated. CSI:FingerID predicts a compound’s molecular fingerprint and searches it in a structure database. This allows for the identification of molecules which are not present in any mass spectral library. CANOPUS predicts compound classes directly from the MS/MS data without any database search involved. This allows to gain structural information about molecules which are not even present in any structure database.
CSI:FingerID and CANOPUS enable excellent automated analysis and ease time-consuming manual interpretation.
To benefit from CSI:FingerID’s and CANOPUS’ structure elucidation please contact us.
What is SIRIUS+CSI:FIngerID?
SIRIUS+CSI:FingerID greatly facilitate structure elucidation of small molecules — such as metabolites — measured by tandem mass spectrometry. Both are based on cutting edge academical research and offers superb performance.
SIRIUS is an open-source software which annotates measured compounds and their corresponding fragments with molecular formulas. This is done with statistically profound combinatorial optimization. CSI:FingerID is seamlessly integrated. It predicts a compound’s molecular fingerprint and searches it in a structure database. This allows for the identification of molecules which are not present in any mass spectral library.
CSI:FingerID enables excellent automated analysis and eases time-consuming manual interpretation.
To benefit from CSI:FingerID’s structure search please contact us.
Based on cutting edge research
SIRIUS, our software framework which integrates all steps for structure elucidation, has been published in Nature Methods. CSI:FingerID was published in PNAS and has been cited several hundred times. SIRIUS, CSI:FingerID and CANOPUS are the result of many years of research which was published among others in Nature Biotechnology, Bioinformatics, the Journal of Cheminformatics and presented on leading Bioinformatics conferences RECOMB and ISMB.
Users all over the world
CSI:FingerID and CANOPUS are free for academic use and enjoy great popularity in the metabolomics community. Millions of queries are submitted and processed every year.
This list of discoveries made by independent research groups might help you to find out how our tools will enhance your metabolomics data analysis.
Best method in independent evaluation
SIRIUS+CSI:FingerID is proofed to be the best computational method to identify molecules from tandem mass spectrometry data by winning the CASMI contest for structure identification in 2016 and 2017 in category 2 (automatic structure identification)
