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.


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

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.


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.

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.