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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, 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.

Download Links


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.

Main citations

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)

Additional citations

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 DataAnalytical 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.


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  • Java 9 and higher are now supported

  • CSI:FingerID trainings structures available

    • Trainings structures available via WebAPI.
    • Trainings structures are flagged in CSI:FingerID candidate list.
  • SMARTS filter for candidate list (GUI)

  • Molecular Property filter for candidate list (GUI)

  • Available prediction workers of the CSI:FingerID webservice can be listed from SIRIUS

  • Improved connection handling and auto reconnect to Webservice

  • Improved error messaged

  • Improved stability and load balancing of the CSI:FingerID webservice

  • Several bug fixes


  • Fragmentation tree heuristics

  • Negative ion mode data is now supported

  • Polished and more informative GUI

    • Sirius Overview: Explained intensity, number of explained peaks, median mass deviation
    • Fragmentation trees: Color coding of nodes by intensity/mass deviation, more informative Fragmentation tree nodes
    • CSI:FingerID Overview: Number of Pubmed publication with pubmed linking for each Candidate, Visualization of CSI:FingerID score.
    • Predicted Fingerprints: Visualisation of prediction (posterior probability), predictor quality (F1) and number of training examples.
    • Several small improvements
  • CPLEX ILP solver support

  • Consider a specific list of ionizations for Sirius

  • Consider a specific list of adducts for CSI:FingerID

  • Custom ionizations/adducts can be specified (CLI and GUI)

  • Full-featured standalone command line version (headless version)

  • Improved parallelization and task management

  • Improved stability of the CSI:FingerID webservice

  • Time limit for fragmentation tree computations

  • Specify fields to import name and ID from .sdf into a custom database (GUI).

  • CSI:FingerID results can be filtered by Custom databases (GUI).

  • Better filtering performance (GUI)

  • Bug fix in Database filtering view (GUI)

  • Error Reporter bug fixed (GUI)

  • Logging bugs fixed

  • Many minor bug fixes


  • Custom databases can be imported by hand or via csv file. You can manage multiple databases within Sirius.

  • New Bayesian Network scoring for CSI:FingerID which takes dependencies between molecular properties into account.

  • CSI:FingerID Overview which lists results for all molecular formulas.

  • Visualization of the predicted fingerprints.

  • ECFP fingerprints are now also in the CSI:FingerID database and do no longer have to be computed on the users side.

  • Connection error detection and refresh feature. No restart required to apply Sirius internal proxy settings anymore.

  • System wide proxy settings are now supported.

  • Many minor bug fixes and small improvements of the GUI


  • element prediction using isotope pattern

  • CSI:FingerID now predicts more molecular properties which improves structure identification

  • improved structure of the result output generated by the command line tool to its final version


  • fix missing MS2 data error

  • MacOSX compatible start script

  • add proxy settings, bug reporter, feature request

  • new GUI look


  • integration of CSI:FingerID and structure identification into SIRIUS

  • it is now possible to search formulas or structures in molecular databases

  • isotope pattern analysis is now rewritten and hopefully more stable than before


  • fix bug with penalizing molecular formulas on intrinsically charged mode

  • fix critical bug in CSV reader


  • Sirius User Interface

  • new output type -O sirius. The .sirius format can be imported into the User Interface.

  • Experimental support for in-source fragmentations and adducts


  • fix crash when using GLPK solver


  • fix bug: SIRIUS uses the old scoring system by default when -p parameter is not given

  • fix some minor bugs


  • if MS1 data is available, SIRIUS will now always use the parent peak from MS1 to decompose the parent ion, instead of using the peak from an MS/MS spectrum

  • fix bugs in isotope pattern selection

  • SIRIUS ships now with the correct version of the GLPK binary


  • release version