News

Meet us at Metabolomics 2019

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Meet Marcus and Sebastian at the conference of the Metabolomics Society 2019.

On Monday and Tuesday, Marcus will present a poster (539) about SIRIUS 4 and turning tandem mass spectra into metabolite structure information.

DFG project on retention time/order prediction granted

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The Deutsche Forschungsgemeinschaft has granted a project on retention time and order prediction for liquid chromatography. This is a joint project with Michael Witting, Helmholtz Zentrum München.

The idea of the project is to integrate retention times from liquid chromatography into the SIRIUS/CSI:FingerID identification pipeline. Literally hundreds of papers have been published on the topic of retention time prediction, but all of them fail to provide predictions that are transferable across chromatography conditions and compound classes; see Héberger’s review (Journal of Chromatography A, 2007) where he speaks rather frankly about the malpractices of publishing such RT-prediction methods. On the other hand, retention times can indeed be used to further boost CSI:FingerID’s identification performance. Also, transferable retention prediction is not impossible, as we have shown here. The trick is not to try to predict retention time (which is extremely dependent on instrument parameters etc) but rather retention order.

We are searching for a qualified and motivated PhD student who wants to accept this challenge. (S)he should be knowledgeable in machine learning and preferably also bioinformatics in general; biochemistry knowledge is clearly also a plus. We believe that this can be the next big thing to further push CSI:FingerID’s performance. Please contact Sebastian or Kathrin in case you are interested and qualified.

IMPRS application call for PhD students

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The International Max Planck Research School at the Max Planck Institute for Chemical Ecology in Jena is looking for PhD students. One of the projects is from our group on “making SIRIUS and CSI:FingerID GCMS-ready”. Deadline is May 24, 2019.

SIRIUS and CSI:FingerID are the best-of-class tools for MS-based compound identification in metabolomics, natural products and related fields. More than one million compound queries have been submitted to our web service, from over 3000 users and 47 countries. See our recent publication in Nature Methods (Dührkop et al., 2019).

Currently, our tools can only process tandem mass spectrometry data; extending them to Gas Chromatography Electron Ionization appears natural, but comes with numerous challenging problems from algorithmics and machine learning. This will be done in cooperation with the group of Georg Pohnert, see his recent publication in Nature (Thume et al., 2018).

We are searching for motivated candidates from bioinformatics, machine learning, cheminformatics and/or computer science who want to work in this exciting, quickly evolving interdisciplinary field. Please contact Sebastian Böcker in case of questions.

Half a position is being paid by the IMPRS; this will be supplemented by funding from our chair to 2/3 TV-L E13. (Note that the cost of living in East Germany is still considerably lower than in West Germany.) Jena is a beautiful city and wine is grown in the region: https://www.youtube.com/watch?v=DQPafhqkabc.

IMPRS: http://imprs.ice.mpg.de/
MPI-CE: http://www.ice.mpg.de/
SIRIUS & CSI:FingerID: https://bio.informatik.uni-jena.de/software/sirius/
Literature: https://bio.informatik.uni-jena.de/publications/ and https://bio.informatik.uni-jena.de/textbook-algoms/

Jena: https://www.google.de/search?q=jena&tbm=isch&
https://www.study-in.de/en/discover-germany/german-cities/jena_26976.php
https://www.google.com/search?q=jena&tbm=isch

Our SIRIUS 4 paper is now available at Nature Methods

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We are happy to announce that our paper “SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information” is now available online at Nature Methods.

  • 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,” Nature Methods, doi 10.1038/s41592-019-0344-8, 2019.

View-only access to the paper is available here.

Dagstuhl seminar on Computational Metabolomics filling up quickly

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Another Dagstuhl seminar on Computational Metabolomics will be held in January 2020. The seminar is filling up quickly: Less than a month ago, invitations have been send out; but 25 people have already accepted the invitation! That is a lot, considering that it is still 10 months to go.

The title of the Dagstuhl seminar is “Computational Metabolomics: From Cheminformatics to Machine Learning“; it will be organized by Corey Broeckling, Emma Schymanski, Nicola Zamboni and myself. Unfortunately, it is invitation only. Two Dagstuhl seminars on related topics (Seminar 15492 in Nov/Dec 2015 and Seminar 17491 in Dec 2017) were already very successful.

Hope that we have a jolly good time in Dagstuhl!

SIRIUS and CSI:FingerID user meeting?

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With SIRIUS and CSI:FingerID gathering interest in the community, we are thinking about a SIRIUS and CSI:FingerID user meeting (a SIRIUS user meeting, so to say) in Jena. This would be a 2-3 day come-together with the possibility to show what your are doing with our tools, discuss with the developers, give us feedback on what is SIRIUSly needed etc. We are open to suggestions.

But most importantly: Are you interested in such a meeting? Would you come to Jena for 2-3 days? When would be a good time? (September is the default, but this is usually packed.)

In case you are interested, please let us know. You can leave your comment below, but please also send an email to the SIRIUS email address.


SIRIUS 4.0.1 released

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A new version of SIRIUS 4 is available for download.
SIRIUS 4.0.1 brings many bugfixes, user interface polishing and improved stability of the CSI:FingerID backend.

  • SIRIUS 4.0.1 now supports JAVA 9 and higher
  • The structures used to train CSI:FingerID are now available via the web service:
    • https://www.csi-fingerid.uni-jena.de/webapi/trainingstructures.csv?predictor=pos
    • https://www.csi-fingerid.uni-jena.de/webapi/trainingstructures.csv?predictor=neg

See our changelog for further details .

You can download SIRIUS with CSI:FingerID here.

Bad Clade Deletion supertrees — swift and accurate, but project has ended

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With the publication of the beam search variant of BCD supertrees (Fleischauer and Böcker, PeerJ 2018), this project has come to an end. BCD supertrees shows an outstanding performance for a supertree method with guaranteed polynomial running time, and is usually on par or even better than established supertree methods such as MRP or SuperFine. With the beam search, you can trade running time for supertree quality; but for input trees that contain branch lengths, even the “regular” BCD shows excellent performance.

We sincerely hope that someone will continue our work and, in particular, will integrate BCD supertrees into a divide-and-conquer strategy to improve the quality of phylogenetic reconstruction for very large trees. In (Fleischauer and Böcker, Mol Biol Evol 2017) we have shown that this is indeed possible (Fig. 2): Computation with RAxML gets faster and the tree quality is improved. Given BCDs fast and guaranteed running times, this should be very interesting for large phylogenies with several thousand taxa: BCD requires only hours to compute a supertree with 5000+ taxa and, even more importantly, supertree quality does not deteriorate for such large datasets.

For us, this is it in phylogenetics — at least, for the moment. It has been a great experience with challenging and fascinating combinatorial problems!

ps. We gratefully acknowledge funding by Deutsche Forschungsgemeinschaft.

pps. The BCD code is available on GitHub.

 

Meet us at ASMS 2018

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Marcus is presenting ZODIAC on Monday at ASMS. This is our new method which enables comprehensive molecular formula identification on whole datasets.
The talk is “The whole is easier than the parts: Improving molecular formula identification using Gibbs sampling on fragmentation trees”.

IMPRS application call for PhD students

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The International Max Planck Research School at the MPI for Chemical Ecology in Jena is looking for PhD students, and one of the projects is on “making SIRIUS and CSI:FingerID GCMS-ready”. Only a half position is being paid by the IMPRS, but this can be supplemented by funding from our chair. We are searching for motivated candidates from bioinformatics, cheminformatics and computer science who want to work in this exciting, quickly evolving interdisciplinary field. Please see here for details, and apply here. Application deadline is May 16th, 2018. Contact Sebastian in case your have questions.

SIRIUS 3 is not longer supported

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We found a major bug in the web service of SIRIUS 3 which can also affect the stability of the new SIRIUS 4. Therefore we decided to shut down the web service of SIRIUS 3 immediately.

Please contact us () if you need to finish work that can only be done with SIRIUS 3. We will try to find a solution then.

How good is the new SIRIUS? (update)

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With the release of the new SIRIUS version (and, behind the scenes, a new version of CSI:FingerID), we want to share some numbers so you know if it was worth the hassle. We use CASMI 2016 data, to allow you to compare our results against those of other methods. We use the candidate structures provided as part of category 2, automated structural identification. See also Schymanski et al. (J Cheminf 2017).

For molecular formula identification, we use both isotope patterns and fragmentation patterns. (Isotope pattern data were released after the contest.) We consider all molecular formulas — we will not get bored to stress that if you limit molecular formulas to those found in some structure database, you will never ever find a new molecular formula. We find that SIRIUS 4 identifies the correct molecular formula for 91.3% of the challenges.

Next, we use CSI:FingerID to identify the compound structures. During the last year, the CASMI 2016 data have found their way into the CSI:FingerID training data. We know that CSI:FingerID has excellent identification performance if a spectrum for this structure is present in the training data (expect anything between 70% and 95%). But this is not challenging, and also does not tell us how good SIRIUS and CSI:FingerID can identify truly novel compounds.

To this end, we excluded all structures from CASMI 2016 from the training data. Hence, any structure is novel, in the sense that CSI:FingerID has never before seen any MS/MS data for this structure. SIRIUS 4 and the new CSI:FingerID reach 37.8% correct identifications for novel structures and positive ion mode; this is significantly better than the 27.6% reported in the CASMI paper (Schymanski et al., J Cheminf 2017). In addition, we can now also process challenges in negative ion mode, thanks to the training data available in NIST; here, we reach 28.4% correct identifications for novel structures.

These numbers are for “unambiguously correctly” identified structures: Sometimes, two candidate structures have exactly the same molecular fingerprint and are scored with exactly the same score. If we include these “ambiguously correctly” identified structures, numbers increase to 40.2% and 30.9%, respectively.

By the way: The idea of a challenge is to be challenging. That is why category 2 of CASMI 2016 uses candidate lists directly extracted from ChemSpider. In application, you will probably use a smaller candidate lists, which will make identification easier and improve identification rates: For example, unambiguous correct identifications for novel structures and positive ion mode increase to 71.7% if we search in a biomolecular structure database with “only” 0.5 million structures.

To cut a long story short: SIRIUS 4 and CSI:FingerID provide outstanding performance for molecular formula and structure identification. As mentioned in the release news, it is also much faster than before: On a set of 1533 GNPS compounds, we observed a 36-fold speedup.

Update: Two bugs had to be corrected in our evaluation. Minor: We chose the wrong parameter set (TOF vs. Orbitrap), resulting in small ID rate changes. Major: We did not search in the biomolecule DB but rather in “biomolecules plus MINEs”. Fixing this resulting in pretty dramatic changes.