During the Dagstuhl seminar on Computational Metabolomics a few days ago, there was a session on “bridging the gap” between methods developers and experimentalists, which resulted in a long list of what method developers should do to bridge the gap (develop GUIs, provide example data, manuals, tutorials, etc) but rather little that experimentalists can do.

Now, let us turn the tables: SIRIUS and CSI:FingerID come with a nice GUI, manual etc. But undoubtedly the best to explain how to use our software in practice, are you, the users! To this end, we ask you to send us any tutorial material you have (maybe, a video tutorial?) that may help fellow experimentalists to get acquainted to the software more quickly; you may also point out what the software cannot do, and how you deal with it; and so on.

Please leave a comment below with the link (I hope that works) or, even better, send us a link () that we can put on our soon-to-come training material web page!

ps. The first video tutorial is already out there: Louis-Felix Nothias-Scaglia (UCSD) has prepared this video tutorial on YouTube that explains how to combine Optimus (OpenMS), SIRIUS plus CSI:FingerID and GNPS. Many thx to him! The tutorial is potentially already outdated as development is progressing fast, but isn’t that nice?!

CASMI (Critical Assessment of Small Molecule Identification) 2017 results are finally out! Kai participated with SIRIUS/CSI:FingerID, and won categories 1 (Best Structure Identification on Natural Products), 2 (Best Automatic Structural Identification – In Silico Fragmentation Only), and 4 (Best Automatic Candidate Ranking). He did not participate in category 3.

For category 4 (in silico methods for searching in molecular structure databases), CSI:FingerID correctly identified 66 of 198 compounds (33.3%). This is more than 6-fold of what the best non-CSI:FingerID contestant reached. (Submissions containing “IOKR” in their name, correspond to different variants of the Input Output Kernel Regression version of CSI:FingerID.)

Unfortunately, CASMI 2017 data did not include isotope patterns. It appears that in many cases, SIRIUS was not able to find the correct molecular formula among the top candidate. This resulted in several cases where the correct structure was excluded due to the “wrong” molecular formula. We will investigate how many compounds would have been correctly identified if isotope pattern data would have been available.

The CSI:FingerID web service has just passed the mark of processing data from 500,000 query compounds — congratulations to CSI:FingerID, and thank you for your interest in our tools! (Be reminded that CSI:FingerID should be accessed via the SIRIUS application, not via the web page.)

Our new version, Sirius 3.5, comes with several advancements. Download and use it here.
We have a new overview tab for CSI:FingerID hits which displays results of structure search for multiple molecular formulas.
You can examine the predicted fingerprint of each compound (and molecular formula) independently of any database.
We now offer the possibility to create and search in custom structure databases.
Besides, we have a new bayesian networks scoring function for CSI:FingerID which considers dependencies between different molecular properties.
This and much more.

A new Sirius version is available. We included new features to enable a more intuitive workflow. We hope you’ll like it. Download and use it here.
We provide element prediction using isotope pattern. CSI:FingerID now predicts more molecular properties which improves structure identification.
Besides we fundamentally changed the structure of the result output generated by the command line tool to its final version. This means you might have to adjust  your workflow.

SIRIUS+CSI:FingerID is no available for Mac. You can download it here. A few highlights of the new version:

• Searching your tandem mass spectra in molecular databases using CSI:FingerId
• Restrict the SIRIUS molecular formula identification to formulas appearing in molecular databases – or search through the whole space of possible formulas. It’s up to you.
• Sort you identification results by confidence such that more reliable identifications (e.g. from high quality spectra or easy recognizable compounds) are separated from bogus identifications.
• Predict structural features and substructures from tandem mass spectra and visualize them in your candidate structure list.

Don’t hesitate to give us your feedback, report bugs or suggesting new features. Just write to