SIRIUS 4 End Of Life

After more than 4 successful years and over 300 million queries to our web services, SIRIUS 4 will reach its end of life on 31th of December 2022.

What does this mean for you? We will shut down the web services behind SIRIUS 4, so fingerprint prediction, structure database search and compound class prediction will not be possible with SIRIUS 4 anymore. Switching to SIRIUS 5 will solve the problem for you. As always, if you have a running long term project that exceeds the end of life date and cannot switch to SIRIUS 5 please contact us.  

SIRIUS email account verification failed, what now?

Dear SIRIUS users,
when creating a new SIRIUS account the link checkers of some email tools seem to execute the verification link before the user can click the link manually. In such cases the link will already be used (invalid) when the user is clicking it manually and the server returns an error message. In such cases the account has already been verified successfully, regardless of the error message. Just ignore the error and try login into SIRIUS. 

We are currently working on a solution to ensure that the verification can only be performed by the user itself.

SIRIUS 5 is released!

We are happy to announce that a major version upgrade of SIRIUS is available! Scroll to the bottom to get a visual impression of the changes.

SIRIUS 5 now includes the following new features and improvements:

  • Lipid class annotation with El Gordo: Lipid structures that have the same molecular formula (usually belonging to the same lipid class) can be extremely similar to each other, often only differing in the position of the double bonds. These extremely similar structures may be even not differentiable by mass spectrometry at all. SIRIUS 5 now predicts the lipid class from the spectrum; when performing CSI:FingerID database search, all structure candidates that belong to this lipid class are tagged.
  • Sub-structure annotation with Epimetheus: For experimentalists, CSI:FingerID search may seem like a black box. If you want to perform manual validation of CSI:FingerID structure candidates, Epimetheus now provides a direct connection between structure candidates and your input MS/MS spectra. Sub-structures of the structure candidate of your choice are generated by a combinatorial fragmenter and assigned to peaks in the MS/MS spectrum. The new Epimetheus view allows you to directly visualize and inspect these sub-structure annotations.
  • Feature-rich spectrum viewer: Improved functionality of the SIRIUS spectrum viewer, including mirror plots of measured vs predicted isotope pattern.
  • New LC-MS view: A new view in SIRIUS 5 that shows the extracted-ion chromatogram of a compound, including its detected adducts and isotopes. A traffic light allows for quick spectral quality assessment.
  • CANOPUS now fully supports Natural Product Classes (NPC).
  • Advanced filtering options on the compound list, including the new lipid class annotations.
  • Support for additional spectrum file formats (.msp, massbank, .mat)

If you were previously using SIRIUS 4, please be aware of the following breaking changes:

  • User authentication: A user account and license is now needed to use the online features of SIRIUS. The license is free and automatically available for non-commercial use. If your account is not automatically verified because your non-commercial research institution is not whitelisted yet, please contact us at
  • New project space compression: Method level directories are now compressed archives to reduce number of files and save storage.
  • Changed summary writing: Summary writing has been made a separate sub-tool (write-summaries). Summary files format has slightly changed. 
  • Prediction / DB search split: The fingerid/structure sub-tool has been split into a fingerprint (fingerprint prediction) and a structure (structure db search) sub-tool. This allows the user to recompute the database search without having to recompute the fingerprint and compound class predictions. It further allows to compute CANOPUS compound class prediction without having to perform structure db search.
  • Updated fingerprints: Updated fingerprint vector. Fingerprint related results of SIRIUS 4 projects may have to be recomputed to perform certain analysis steps (e.g. recompute db-search). Reading the projects is still possible and formula results are not affected.
  • Custom database format change: Custom database format has changed. Custom databases need to be re-imported.
  • GUI column rename: Some views in the GUI have been renamed to better reflect their position and role in the workflow.

For a quick overview on these new features and changes, visit our YouTube channel. Please refer to our online documentation for a more comprehensive overview and help us squash all remaining bugs by contributing at our GitHub!

 

 

COSMIC has appeared in Nature Biotechnology

Our article “High-confidence structural annotation of metabolites absent from spectral libraries” has just appeared in Nature Biotechnology. Congrats to Martin and all co-authors!

In short, COSMIC allows you to assign confidence to structure annotations. For every structure annotated by CSI:FingerID, COSMIC provides a confidence score (a number between 0 and 1) that tells you how likely it is that this annotation is correct. This is similar in spirit to what is done in spectral library search: Not only is the cosine score used to decide which candidate best fits to the query spectrum; in addition, we use the cosine of the top-scoring candidate (the hit) to decide whether it is likely correct (say, above 0.8), incorrect (say, below 0.6) or in the “twilight” in-between. If you have been using CSI:FingerID for some time, you might have noticed that finding such thresholds is not possible for the CSI:FingerID score. COSMIC closes this gap and tells you if an annotation is likely correct or incorrect.

Doing so is undoubtedly convenient in practice; but this is not what COSMIC is all about. What we can do now is to sort all hits in a dataset or even a repository with respect to confidence, and then concentrate our downstream analysis on high-confidence annotations. Next, we can replace the “usual” structure databases we search in by a structure database made entirely from hypothetical structures generated by combinatorics, machine learning or in silico enzymatic reactions.

We demonstrate COSMIC’s power by generating a database of hypothetical bile acid structures, combinatorially adding amino acids to bile acid cores, yielding 28,630 plausible bile acid conjugate structures. We then searched query MS/MS data from a mice fecal dataset in this combinatorial database, and used the COSMIC confidence score to distinguish between hits that are likely correct or incorrect. We manually evaluated the top 12 hits and found that 11 annotation (91.6%) were likely correct; two annotations were further confirmed using synthetic standards. All 11 bile acid conjugates are “truly novel”, meaning that we could not find those structures in PubChem or any other structure database (or publication). Whereas reporting 11 novel bile acid conjugates may appear rather cool, we argue it is even cooler that we did this without a biological hypothesis beyond “there might be bile acid conjugates out there which nobody knows about”; and that COSMIC found the top bile acid conjugate annotations in a fully automated manner and in in a matter of hours.

We have also annotated 2,666 LC-MS/MS runs from human samples with molecular structures which are currently absent from HMDB, and for which no MS/MS reference data are available; and finally, 17,414 LC-MS/MS runs with annotations for which no MS/MS reference data are available. We hope that some of them might be of interest to you.

If you have an idea of hypothetical structures, similar to the bile acid conjugates, to be searched against thousands of datasets, please let us know.

COSMIC’s confidence score is available through SIRIUS since version 4.8, download here.

 

Happy 25 million queries, CANOPUS!

We are fully aware that this post is far less interesting to you than it is to us; but sometimes, proud parents just have to do what proud parents have to do: CANOPUS has passed 25 million queries! Congratulations! Wow, that was fast, the preprint appeared on bioRxiv only 14 months ago.

In this context, we can also report that CSI:FingerID has surpassed 120 million queries. Which basically means we missed the round anniversary. We are bad parents; but kids are sometimes growing so quickly, you turn around and they are past 100 million queries.

Have fun with our tools!

 

SIRIUS 4.0.1 End Of Life

After 2 and a half successful years and over 35 million predicted fingerprints, SIRIUS 4.0.1 will reach its end of life on Friday the 30th of April 2021.
What does this mean for you? We will shut down the web service for CSI:FingerID, so no fingerprint prediction and structure database search will be possible with SIRIUS 4.0.1 anymore.

SIRIUS 4.7.0 Released

We are happy to announce that SIRIUS 4.7.0 is now available for download . This release is all about fixing bugs and performance optimization. To all who had problems with the ILP solvers, a freezing GUI, high memory consumption or long running times: This update should make your life way easier. For a full list of changes see the Changelog.

We further integrated the option to compute fragmentation trees only with our heuristic algorithm (no ILP involved) to speedup molecular formula identification for high mass compounds.
Together with applying timeouts on compound level this should make the processing of large datasets much more feasible.

 

SIRIUS screener
SIRIUS 4.7.0

Video Behind the Scenes: CSI:FingerID

There is a new video available and it is finally explaining CSI:FingerID in much detail — possibly too much detail, the video is more than 2 hours. Covers everything from general thoughts and considerations about in silico methods and methods evaluation, to the details of molecular fingerprints, FingerID and, finally, CSI:FingerID. I am sorry for the bad audio quality, still using my build-in laptop mic.

 

SIRIUS online documentation now available!

We are happy to announce that the new online documentation for SIRIUS is now available at https://boecker-lab.github.io/docs.sirius.github.io/.

The content is completely written in Markdown which makes contributions by the community very easy. No programming skills required!
Help us with your contributions to make this documentation more comprehensive and useful for the community. See our GitHub repository for 
detailed information on how to contribute.

Classes for the masses: CANOPUS has appeared in Nature Biotechnology

Our article “Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra” has just appeared in Nature Biotechnology. Congrats to Kai and all co-authors!

In short: CANOPUS is a computational tool for systematic compound class annotation. It uses a deep neural network to predict 2,497 compound classes from fragmentation spectra, including all biologically relevant classes. From the machine learning perspective, the interesting part is that different levels of the neural network are trained using different data (heterogeneous training). CANOPUS explicitly targets compounds for which neither spectral nor structural reference data are available, and even predicts classes completely lacking tandem mass spectrometry training data. In evaluation using reference data, CANOPUS reached very high prediction performance (average accuracy of 99.7% in cross-validation) and outperformed four (rather advanced) baseline methods. We used CANOPUS to investigating the effect of microbial colonization in the mouse digestive system, for analyzing the chemodiversity of different Euphorbia plants, and for the structural elucidation of a novel marine natural product.

CANOPUS is already available to users through SIRIUS 4.5, which was released last Thursday. See also the designated CANOPUS page. A view-only version of the article is available here.

Full citation: K. Dührkop, L.-F. Nothias, M. Fleischauer, R. Reher, M. Ludwig, M. A. Hoffmann, D. Petras, W. H. Gerwick, J. Rousu, P. C. Dorrestein, and S. Böcker. Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra. Nat Biotechnol, 2020. https://doi.org/10.1038/s41587-020-0740-8

 

SIRIUS 4.5 released

We are happy to announce that a new version of SIRIUS is available. With that, CANOPUS now supports negative ion mode data. Additionally, we included more structure databases CSI:FingerID can search in, such as COCONUT (Sorokina & Steinbeck, 2020) and NORMAN (Brack et al., 2012). And in case an important database is missing: With the new version, you can import custom databases using the GUI.

Even more features:

  • All molecular structures have been standardized using the PubChem standardization service, to make structures more consistent. This update was already reported for version 4.4 but kept bugging us; it should now be solved for good. The standardization has a (small but measurable) positive impact on CSI:FingerID’s performance. More importantly, you will find fewer cases where CSI:FingerID is doing “something really strange”; this strange behavior was often due to un-standardized structures.
  • Breaking news: We renamed a few columns in the SIRIUS project space (see Changelog), to make column names more descriptive. Sorry about that; please make sure your downstream analysis is reading in the right columns.
  • CSI:FingerID now uses the molecular formula-specific Bayesian network scoring from our ISMB 2018 publication. Integrating this new score was a huge effort, but again has a positive impact on CSI:FingerID’s performance.
  • To allow for a smooth transition, you can continue to use SIRIUS 4.4 and the corresponding CSI:FingerID web service until November the 30th.
  • Please help us to make SIRIUS great again: Report bugs using the SIRIUS GitHub repository, or send an email to .

 

ZODIAC has appeared in Nature Machine Intelligence

Our article “Database-independent molecular formula annotation using Gibbs sampling through ZODIAC” has just appeared in Nature Machine Intelligence. Congrats to Marcus and all co-authors!

In short: Annotating the molecular formula of a small molecule is the first step towards its structural elucidation but remains highly challenging, particularly for “large compounds” above 500 Daltons. ZODIAC is a network-based algorithm for the de novo annotation (no database needed) of molecular formulas, and processes complete experimental LC-MS/MS runs. (No metabolite is an island.) In comparison to SIRIUS, previously best-of-class for this task, ZODIAC reduces the error rate of false annotations roughly to the half. And sometimes, much more…

If you have problems accessing the paper: Here is a read-only version

ZODIAC is already available to users through SIRIUS 4.4. See also the designated ZODIAC page.

Full citation: M. Ludwig, L.-F. Nothias, K. Dührkop, I. Koester, M. Fleischauer, M.A. Hoffmann, D. Petras, F. Vargas, M. Morsy, L. Aluwihare, P.C. Dorrestein, and S. Böcker. Database-independent molecular formula annotation using Gibbs sampling through ZODIAC. Nat Mach Intell 2:629–641, 2020.

SIRIUS 4.4.21 – Can now be used along with 4.0.1

Some of you may have noticed problems where SIRIUS 4.4 GUI did not start without reporting any error.
This might be due to old incompatible configs (.sirius directory) from version 4.0.1. SIRIUS 4.4.21 fixes this problem and now uses a separate config directory (.sirius-4.4). It is now possible to use version 4.0.1 and 4.4.x along on the same system without interfering each other.

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.