SIRIUS and CSI:FingerID are offered to the public as freely available resources. Use and re-distribution of the methods, in whole or in part, for commercial purposes requires explicit permission of the authors and explicit acknowledgment of the source material and the original publications. We ask that users who use SIRIUS and CSI:FingerID cite the corresponding papers in any resulting publications.
The CSI:FingerID web-service hosted by the boecker group at https://www.csi-fingerid.uni-jena.de, which is used by default in SIRIUS, is for non-commercial use only. For commercial users the Bright Giant GmbH provides CSI:FignerID related services that can be used with SIRIUS.
SIRIUS is a new java-based software framework for discovering a landscape of de-novo identification of metabolites using single and tandem mass spectrometry. SIRIUS uses isotope pattern analysis for detecting the molecular formula and further analyses the fragmentation pattern of a compound using fragmentation trees. Fragmentation trees can be uploaded to CSI:FingerID via a web service, and results can be displayed in the SIRIUS graphical user interface. (This is also possible using the command line version of SIRIUS.) This is the recommended way of using CSI:FingerID.
SIRIUS+CSI:FingerID GUI and CLI - Version 4.0.1 (Build 10 from 2019-10-23)
SIRIUS+CSI:FingerID Commandline only - Version 4.0.1 (Build 10 from 2019-10-23)
Sources on GitHub
Integration of CSI:FingerID
Fragmentation trees and spectra can be directly uploaded from SIRIUS to a CSI:FingerID web service (without the need to access the CSI:FingerID website). Results are retrieved from the web service and can be displayed in the SIRIUS graphical user interface. This functionality is also available for the SIRIUS command-line tool. The training Structures of CSI:FingerID predictors are available through the CSI:FingerID WebAPI.
Training structures for positive ion mode:
Training structures for negative ion mode:
Fragmentation Tree Computation
The manual interpretation of tandem mass spectra is time-consuming and non-trivial. SIRIUS analyses the fragmentation pattern resulting in hypothetical fragmentation trees in which nodes are annotated with molecular formulas of the fragments and arcs represent fragmentation events. SIRIUS allows for the automated and high-throughput analysis of small-compound MS data beyond elemental composition without requiring compound structures or a mass spectral database.
Isotope Pattern Analysis
SIRIUS deduces molecular formulas of small compounds by ranking isotope patterns from mass spectra of high resolution. After preprocessing, the output of a mass spectrometer is a list of peaks which corresponds to the masses of the sample molecules and their abundance. In principle, elemental compositions of small molecules can be identified using only accurate masses. However, even with very high mass accuracy, many formulas are obtained in higher mass regions. High resolution mass spectrometry allows us to determine the isotope pattern of sample molecule with outstanding accuracy and apply this information to identify the elemental composition of the sample molecule. SIRIUS can be downloaded either as graphical user interface (see Sirius GUI) or as command-line tool.
K. Dührkop, M. Fleischauer, M. Ludwig, A. A. Aksenov, A. V. Melnik, M. Meusel, P. C. Dorrestein, J. Rousu, and S. Böcker, Sirius 4: turning tandem mass spectra into metabolite structure information, Nat methods, 2019.
Kai Dührkop and Sebastian Böcker. Fragmentation trees reloaded. J Cheminform, 8:5, 2016. (Cite this for fragmentation pattern analysis and fragmentation tree computation)
Kai Dührkop, Huibin Shen, Marvin Meusel, Juho Rousu, and Sebastian Böcker. Searching molecular structure databases with tandem mass spectra using CSI:FingerID. Proc Natl Acad Sci U S A, 112(41):12580-12585, 2015. (cite this when using CSI:FingerID)
Sebastian Böcker, Matthias C. Letzel, Zsuzsanna Lipták and Anton Pervukhin. SIRIUS: decomposing isotope patterns for metabolite identification. Bioinformatics (2009) 25 (2): 218-224. (Cite this for isotope pattern analysis)
W. Timothy J. White, Stephan Beyer, Kai Dührkop, Markus Chimani and Sebastian Böcker. Speedy Colorful Subtrees. In Proc. of Computing and Combinatorics Conference (COCOON 2015), volume 9198 of Lect Notes Comput Sci, pages 310-322. Springer, Berlin, 2015. (cite this on why computations are swift, even on a laptop computer)
Huibin Shen, Kai Dührkop, Sebastian Böcker and Juho Rousu. Metabolite Identification through Multiple Kernel Learning on Fragmentation Trees. Bioinformatics, 30(12):i157-i164, 2014. Proc. of Intelligent Systems for Molecular Biology (ISMB 2014). (Introduces the machinery behind CSI:FingerID)
Imran Rauf, Florian Rasche, François Nicolas and Sebastian Böcker. Finding Maximum Colorful Subtrees in practice. J Comput Biol, 20(4):1-11, 2013. (More, earlier work on why computations are swift today)
Heinonen, M.; Shen, H.; Zamboni, N.; Rousu, J. Metabolite identification and molecular fingerprint prediction through machine learning. Bioinformatics, 2012. Vol. 28, nro 18, pp. 2333-2341. (Introduces the idea of predicting molecular fingerprints from tandem MS data)
Florian Rasche, Aleš Svatoš, Ravi Kumar Maddula, Christoph Böttcher, and Sebastian Böcker. Computing Fragmentation Trees from Tandem Mass Spectrometry Data. Analytical Chemistry (2011) 83 (4): 1243–1251. (Cite this for introduction of fragmentation trees as used by SIRIUS)
Sebastian Böcker and Florian Rasche. Towards de novo identification of metabolites by analyzing tandem mass spectra. Bioinformatics (2008) 24 (16): i49-i55. (The very first paper to mention fragmentation trees as used by SIRIUS)
Starting with version 3.4, SIRIUS is licensed under the GNU General Public License (GPL). If you integrate SIRIUS into other software, we strongly encourage you to make the usage of SIRIUS as well as the literature to cite transparent to the user.