Introducing CANOPUS for comprehensive compound class annotation

We are happy to introduce CANOPUS, a tool for the comprehensive annotation of compound classes from MS/MS data (certain restrictions apply, see below). In principle, CANOPUS is doing something similar as CSI:FingerID: Whereas CSI:FingerID can tell you what substructures are part of the query compound, CANOPUS does so for compound classes. The differences between both tasks are subtle but have massive consequences. See this preprint on the details of this difference, how CANOPUS works, how good it works etc.

At present, CANOPUS predicts 1270 compound classes. In more detail, CANOPUS predicts ClassyFire compound classes. ClassyFire is not the first but, to the best of our knowledge, by far the most comprehensive approach to assign classes solely from structure. (This last point is key, as this allows us to assign thousands of classes for millions of molecular structures.) Please have a look there if you use CANOPUS: Certain compound class definitions may be not what you expect. For example, we found that many phytosteroids are classified as bile acids in ClassyFire. While the biochemical origin of both classes is very different, they are structural very similar and, therefore, represented by the same class in the ClassyFire ontology.

You can download, install and use CANOPUS through SIRIUS 4.4. You will notice a new tab where you can access, for each compound, all compound classes it does or does not belong to (and, how sure we are about that). Fancier visualizations (see the preprint) will be made available with upcoming releases.

ps. Clearly, CANOPUS is comprehensive only within the limits of the LC-MS/MS technology: If a compound does not ionize, if no fragmentation spectrum is recorded in Data Dependent Acquisition, if a compound does not show any fragmentation, if multiple compounds are fragmented in a single spectrum etc, then CANOPUS cannot help you. We don’t do magic. Also, CANOPUS is limited by the available (structure and MS/MS) training data; but several years of thinking have been invested to get the most out of it.

Introducing ZODIAC for improved molecular formula annotations

We are happy to introduce ZODIAC, a tool for the comprehensive annotation of molecular formulas for complete LC-MS/MS runs. SIRIUS 4 is currently best-of-class for this task (as far as we know); but ZODIAC can do better. Different from SIRIUS which considers one compound at a time, ZODIAC considers a complete dataset, assuming that all compounds are somehow related (usually through biotransformations). See the preprint for evaluation and method details.

ZODIAC is about de novo annotations, meaning that we can assign molecular formulas for novel compounds currently absent from any structure database. ZODIAC takes into account “uncommon” elements, as in C24H47BrNO8P or C15H30ClIO5; both examples are indeed novel molecular formulas annotated by ZODIAC (and verified by us). Enter those molecular formulas into the PubChem search and see what you get back. (Fun fact: the first query now returns two entries created Jan 2020 based on our annotations.)

You can download, install and use ZODIAC through SIRIUS 4.4. Results of ZODIAC are simply displayed in the molecular formula tab, if you choose to run it. You should definitely use ZODIAC if you want to run CANOPUS: Assigning molecular classes to novel compounds implies that some of the molecular formulas may be novel, too; and you do not want provide CANOPUS a wrong molecular formula.

The ZODIAC score is displayed in the overview tab.

ps. Sorry for tweeting early, WordPress sometimes has a mind of its own.

SIRIUS 4.4 released

We are happy to announce that SIRIUS 4.4 is finally released. (Unfortunately, the MacOS version will have to wait a few more days.) There have been numerous changes and improvements, only few of which can be mentioned here.

Probably the biggest change is that SIRIUS 4.4 now reads mzML files (“centroided” data) and processes complete LC-MS/MS datasets. You can use ProteoWizard to transform your dataset to mzML. This does not only make things easier for you; it also allows SIRIUS to extract isotope patterns and adduct information more thoroughly from the MS1 data. SIRIUS 4.4 also supports multi-run datasets and aligns runs.

If you are using the graphical user interface (GUI) you no longer have to care about installing (the correct version of) Java. It is part of the installed SIRIUS software.

SIRIUS 4.4 uses the same project space for the command-line (CLI) and the GUI version, allowing you to use the SIRIUS GUI to browse through results computed with the CLI. The GUI also allows you to save your project and reload it later, including all previously computed results. Finally, you can export summary CSV and mzTab-M files for downstream analysis.

CSI:FingerID also had some updates:

  • Additional large molecular substructures: Have a look at the Fingerprint tab in the SIRIUS GUI, filter for large substructures.
  • Standardization of molecular structures (mesomerism, charge etc) through PubChem. This does not only improve identification statistics by a few percentage points, but also gets rid of certain cases where CSI:FingerID was doing “strange things”. Unfortunately, PubChem keeps changing the standardization without giving big notice, so some issues remain; but the current situation is definitely better than no standardization.

More stuff:

  • There is currently no version for MacOS; we are sorry. Somehow, MacOS does not like our multithreading. At present, we do not have access to a Mac for debugging, thanks to Corona.
  • Please report bugs using the SIRIUS GitHub repository or . There will be numerous such bugs, as SIRIUS 4.4 again carries major improvements and transformations under the hood. Help us to make SIRIUS better.
  • To allow for a smooth transition, you can continue to use SIRIUS 4.0.1 and the corresponding CSI:FingerID web service for a couple of weeks.
  • SIRIUS 4.4 integrates ZODIAC and CANOPUS, see the separate news.
  • passatutto is integrated into SIRIUS 4.4, allowing you to generate your own spectral library decoy database for FDR estimation.
  • We have included a beautiful interactive fragmentation tree viewer.
  • There may be a few more releases of SIRIUS 4.4.x that ship those things which are done in principle.
  • Finally, we have not reported the number of CSI:FingerID queries for some time, so here we go: There have been 47 million CSI:FingerID queries. (Plus a few million we lost through a little scripting bug. Our bad.) That is roughly one query every 1.5 seconds since we reported one million queries in Feb 2018.

 

Hiccup of FSU computer network

Yesterday (27 April 2020) our university computer network experienced some issues and was unavailable for several hours. Not unexpectedly, this also resulted in the unavailability of the CSI:FingerID web service, website etc. As usual, computer problems cause more computer problems: It looks like today (28 April 2020) we still have certain issues restarting the CSI:FingerID workers. That is hopefully resolved soon. We apologize for any inconvenience.

 

SIRIUS 4.4 beta released

Some of you may have noticed that yesterday, April 17, the SIRIUS 4.4 beta has been released. This update is huge so we are particularly careful not to break too many things. (We will definitely break some things so please report bugs using the SIRIUS GitHub repository or .) Some facts of what you can expect:

  • The official SIRIUS 4.4 release will happen in a few days.
  • Even after SIRIUS 4.4 has been officially deployed, you can continue to use SIRIUS 4.0.1 and the corresponding CSI:FingerID web service. We hope that this allows for a smooth transition.
  • SIRIUS 4.4 integrates ZODIAC for better molecular formulas.
  • SIRIUS 4.4 integrates CANOPUS for compound class assignments.
  • SIRIUS 4.4 now reads mzML files (“centroided” data) and processes complete LC-MS/MS datasets.
  • CSI:FingerID had some massive updates, including more and larger molecular properties and standardization of molecular structures.
  • SIRIUS 4.4 also supports multi-run datasets and aligns runs.
  • SIRIUS 4.4 uses the same project space for the command-line and the user interface version, allowing you to use the SIRIUS GUI to browse through results computed with the CLI.
  • passatutto is integrated into SIRIUS 4.4, allowing you to generate your own spectral library decoy database for FDR estimation.
  • If you wonder why we jump from version 4.0.1 to 4.4: There have been several internal releases in between.
  • A word of warning: Many features and changes have accumulated and there will be a few more releases (4.4.x) until the quiver is empty. For example, the structure database will change again as we have massive issues with the way PubChem handles structure standardization.

New version of Lecture Notes on Algorithmic MS

I have just uploaded a new version (0.8.3) of the Lecture Notes on Algorithmic Mass Spectrometry. As expected, I did not have too much time to work on it (them?) during lecture time, which is luckily over now. It is a lot of small improvements. Also, Magnus Palmblad was so kind and had an expert look through the isotope pattern sections. Unfortunately, the stuff that was missing from the previous version, is still missing now…

DFG project on retention time/order prediction granted

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

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

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

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?

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.


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

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.