Sebastian Böcker

Good news for those who want to attend the Prague workshop but were not admitted: The Prague workshop on computational mass spectrometry will be streamed, see here. You should get the necessary software installed beforehand if you want to join in.

 

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 (Project 7) is from our group on “rethinking molecular networks”. Application deadline is April 19, 2024.

Mass spectrometry (MS) is the analytical platforms of choice for high-throughput screening of small molecules and untargeted metabolomics. Molecular networks were introduced in 2012 by the group of Pieter Dorrestein, and have found widespread application in untargeted metabolomics, natural products research and related areas. Molecular networking is basically a method of visualizing your data, based on the observation that similar tandem mass spectra (MS/MS) often correspond to compounds that are structurally similar. Constructing a molecular network allows us to propagate annotations through the network, and to annotate compounds for which no reference MS/MS data are available. Since its introduction, the computational method has received a few “updates”, including Feature-Based Molecular Networks and Ion Identity Molecular Networks. Yet, the fundamental idea of using the modified cosine to compare tandem mass spectra, has basically remained unchanged at the core of the method.

In this project, we want to “rethink molecular networks”, replacing the modified cosine by other measures of similarity, including fragmentation tree similarity, the Tanimoto similarity of the predicted fingerprints, and False Discovery Rate estimates. See the project description for details.

We are searching for a qualified and motivated candidate 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. Payment is 0.65 positions TV-L E13.

IMPRS: https://www.ice.mpg.de/129170/imprs
MPI-CE: https://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 is a beautiful city and wine is grown in the region:
https://www.youtube.com/watch?v=DQPafhqkabc
https://www.google.com/search?q=jena&tbm=isch
https://www.study-in.de/en/discover-germany/german-cities/jena_26976.php

Unfortunately, the Prague Workshop on Computational Mass Spectrometry (April 15-17, 2024) is heavily overbooked. The organizers will try to stream the workshop and the recorded sessions will be made available online, so check there regularly.

The workshop is organized by Tomáš Pluskal and Robin Schmid (IOCB Prague). Marcus Ludwig (Bright Giant) and Sebastian will give a tutorial on SIRIUS, CANOPUS etc.

Topics of the workshop are: MZmine, SIRIUS, matchms, MS2Query, LOTUS, GNPS, MassQL, MASST, and Cytoscape.

Our paper “RepoRT: a comprehensive repository for small molecule retention times” has just appeared in Nature Methods. This is joint work with Michael Witting (Helmholtz Zentrum München) as part of the DFG project “Transferable retention time prediction for Liquid Chromatography-Mass Spectrometry-based metabolomics“. Congrats to Fleming, Michael and all co-authors! In case you do not have access to the paper, you can find the preprint here and a read-only version here.

RepoRT is a repository for retention times, that can be used for any computational method development towards retention time prediction. RepoRT contains data from diverse reference compounds measured on different columns with different parameters and in different labs. At present, RepoRT contains 373 datasets, 8809 unique compounds, and 88,325 retention time entries measured on 49 different chromatographic columns using varying eluents, flow rates, and temperatures. Access RepoRT here.

If you have measured a dataset with retention times of reference compounds (that is, you know all the compounds identities) then please, contribute! You can either upload it to GitHub yourself, or you can contact us in case you need help. In the near future, a web interface will become available that will make uploading data easier. There are a lot of data in RepoRT already, but don’t let that fool you; to reach a transferable prediction of retention time and order (see below), this can only be the start.

If you want to use RepoRT for machine learning and retention time or order prediction: We have done our best to curate RepoRT: We have searched and appended missing data and metadata; we have standardized data formats; we provide metadata in a form that is accessible to machine learning; etc. For example, we provide real-valued parameters (Tanaka, HSM) to describe the different column models, in a way that allows machine learning to transfer between different columns. Yet, be careful, as not all data are available for all compounds or datasets. For example, it is not possible to provide Tanaka parameters for all columns; please see the preprint on how you can work your way around this issue. Similarly, not all compounds that should have an isomeric SMILES, do have an isomeric SMILES; see again the preprint. If you observe any issues, please let us know. See this interesting blog post and this paper as well as our own preprint on why providing “clean data” as well as “good coverage” are so important issues for small molecule machine learning.

Nach aktuellem Kenntnisstand muss das Modul “Bioinformatische Methoden in der Genomforschung” im WS 23/24 leider ausfallen. Wir dürfen die Mitarbeiterstelle nicht besetzen, die wir dafür zwingend brauchen. Wir haben gekämpft und argumentiert und alles getan was wir konnten, aber am Ende war es leider vergeblich. Das Modul findet voraussichtlich das nächste Mal im WS 25/26 statt.

Warnung: Im Zuge der Sparmaßnamen an der FSU Jena kann es in Zukunft häufiger zu sollen kurzfristigen Ausfällen kommen.

 

Sebastian will give a tutorial on using SIRIUS and beyond at the Munich Metabolomics Meeting 2023.

Unfortunately, we have not been able to fill the PhD position for the HUMAN EU project so far. In case you are interested, please contact us!

Update: The position has been filled.

 

Im Rahmen des MINT Festivals in Jena hat Sebastian ein neues Video zum Studium der Bioinformatik aufgenommen: “Kleine Moleküle. Was uns tötet, was uns heilt“. Es richtet sich vom Vorwissen her an Schüler aus der Oberstufe, aber vielleicht können auch Schüler aus den Jahrgangsstufen darunter etwas mitnehmen. Das Video ist erst mal nur über diese Webseite zu erreichen, wird aber in Kürze bei YouTube hochgeladen.

Am Ende noch zwei Fragen: Erstens, welche Zelltypen im menschlichen Körper enthalten nicht die (ganze) DNA? Da war ich aus Zeitgründen bewusst schlampert; keine Regel in der Biologie ohne Ausnahme. Und zweitens, NMR Instumente werden häufig nicht mit flüssigem Stickstoff gekühlt, sondern mit… was? Wie so oft geht es dabei ums liebe Geld.

Meet us at the GCB 2023 in Hamburg! Nils, Wei and Fleming are going to present posters, and Sebastian will give a keynote talk.

 

The RepoRT (well, Repository for Retention Times, you guessed it) preprint is available now. It has been a massive undertaking to get to this point; honestly, we did not expect it to be this much work. It is about diverse reference compounds measured on different columns with different parameters and in different labs. At present, RepoRT contains 373 datasets, 8809 unique compounds, and 88,325 retention time entries measured on 49 different chromatographic columns using varying eluents, flow rates, and temperatures. Access RepoRT here.

If you have measured a dataset with retention times of reference compounds (that is, you know all the compounds identities) then please, contribute! You can either upload it to GitHub yourself, or you can contact us in case you need help. In the near future, a web interface will become available that will make uploading data easier. There are a lot of data in RepoRT already, but don’t let that fool you; to reach a transferable prediction of retention time and order (see below), this can only be the start.

If you want to do anything with the data, be our guests! It is available under the Creative Commons License CC-BY-SA.

If you want to use RepoRT for machine learning and retention time or order prediction, then, perfect! That is what we intended it for. 🙂 We have done our best to curate RepoRT: We have searched and appended missing data and metadata; we have standardized data formats; we provide metadata in a form that is accessible to machine learning; etc. For example, we provide real-valued parameters (Tanaka, HSM) to describe the different column models, in a way that allows machine learning to transfer between different columns. Yet, be careful, as not all data are available for all compounds or datasets. For example, it is not possible to provide Tanaka parameters for all columns; please see the preprint on how you can work your way around this issue. Similarly, not all compounds that should have an isomeric SMILES, do have an isomeric SMILES; see again the preprint. If you observe any issues, please let us know.

 

A few years ago, Michael Witting and I joined forces to get a transferable prediction of retention times going: That is, we want to predict retention times (more precisely, retention order) for a column even if we have no training data for that column. Yet, to describe a column to a machine learning model, you have to provide some numerical values that allow the model to learn what columns are similar, and how similar. We are currently focusing on reversed-phase (RP) columns because there are more datasets available, and also because it appears to be much easier to predict retention times for RP.

Tanaka parameters and Hydrophobic Subtraction Model (HSM) parameters are reasonable choices for describing a column. Unfortunately, for many columns that are in “heavy use” by the metabolomics and lipidomics community, we do not know these parameters! Michael recently tweeted about this problem, and we got some helpful literature references — kudos! for that. Yet, there are still many columns in the unknown.

Now, the problem is not so much that the machine learning community will not be able to make use of training data from these columns, simply because a few column parameters are unknown. This is unfortunate, but so be it. The much bigger problem is that even if someone comes up with a fantastic machine learning model for transferable retention time prediction — it may not be applicable for your column. Because for your column we do not know the parameters! That would be very sad.

So, here is a list of columns that are heavily used, but where we do not know Tanaka parameters, HSM parameters, or both. Columns are ordered by “importance to the community”, whatever that means… If you happen to know parameters for any of the columns below, please let us know! You can post a comment below or write us an email or send a carrier pigeon, whatever you prefer. Edit: I have switched off comments, it was all spam.

Missing HSM parameters

1. Waters ACQUITY UPLC HSS T3
2. Waters ACQUITY UPLC HSS C18
3. Restek Raptor Biphenyl
4. Waters CORTECS UPLC C18
5. Phenomenex Kinetex PS C18

Missing Tanaka parameters

1. Waters CORTECS T3
2. Waters ACQUITY UPLC HSS T3
3. Waters ACQUITY UPLC HSS C18
4. Restek Raptor Biphenyl
5. Waters CORTECS UPLC C18
6. Phenomenex Kinetex PS C18

Meet Sebastian at the annual symposium of the Korean Metabolomics Society (April 5-7). Sebastian will give a keynote lecture on April 7.

The Deutsche Forschungsgemeinschaft has provided us with funding for our project Harvester. The problem in many areas of small molecule machine learning is the available training data and how slowly more data become available. This is also true for MS/MS data, where doubling time is a decade or two, possibly more. To this end, a somewhat obvious idea is to resort to unlabeled data, as there is tons of it available (particularly in GNPS). Yet, using these data is non-trivial. We have already experimented with pre-training, but this improved annotation rates by a mere single percentage point. In our new project, we instead want to resort to self-training, a technique recently “rediscovered” and successfully used for AlphaFold2, among others. What we now need is someone to do the work and take the money. If you are interested, let us know!

We are thrilled to announce that our newest tool MAD HATTER can correctly annotate 98% of small molecule tandem mass spectra, when searching in PubChem! We are extremely excited about this massive breakthrough! MAD HATTER combines CSI:FingerID results with information from the searched structure database via a metascore, using viable compound information such as the melting point, or the number of “was it a cat I saw?” in the compound description.

Our evaluations use the well-known CASMI 2016 data, and we are happy to announce that MAD HATTER strongly outperforms all tools that participated in the contest. MAD HATTER also performs very well if we replace the MS/MS spectra by either empty spectra or random spectra. This opens up fantastic new venues in the future, where instrument vendors may replace bulky and expensive traps and collision cells by a random number generators or /dev/null.

Read the exciting preprint on bioRxiv: https://doi.org/10.1101/2022.12.07.519436

We assume that everybody will be thrilled to use MAD HATTER in the future. At the moment, you may find additional information here, here and here.

Update: Read the exciting final paper in Metabolites: https://doi.org/10.3390/metabo13030314

As part of the EU HUMAN doctoral network, my group is looking for a PhD student from bioinformatics, computer science or cheminformatics for the computational analysis of mass spectrometry data. The PhD student is expected to have experience with and interest in the development and evaluation of computational methods and machine learning models. The project will involve international and intersectorial secondments/visits to other project partners in the network for the doctoral candidates to learn new skills and foster collaborations.

The doctoral network HUMAN (Harmonising and Unifying Blood Metabolomic Analysis Networks) is a multidisciplinary consortium that focuses on topics such as cross-laboratory comparisons, integration, co-evaluation, and proofing of liquid chromatography mass spectrometry for the analysis of human blood. It offers twelve PhD positions.

If you are interested, check here for more details, contact Sebastian or apply here.

 

 

Congratulations!

We are happy to announce that we are part of the EU HUMAN doctoral network, funded by EU Horizon. The topic of the network is “Harmonising and Unifying Blood Metabolomic Analysis Networks“. In the very near future, we will officially advertise the doctoral positions that are part of this network on our website. My group is looking for a PhD student from bioinformatics, computer science or cheminformatics for the computational analysis of mass spectrometry data. The student is expected to have experience with and interest in the development and evaluation of computational methods and machine learning models. If you are interested, please contact Sebastian.

HUMAN is a multidisciplinary consortium that focuses on topics such as cross-laboratory comparisons, integration, co-evaluation, and proofing of liquid chromatography mass spectrometry for the analysis of human blood.

 

Congratulations to Michael “Michele” Stravs from the group of Nicola Zamboni at ETH Zürich: The article “MSNovelist: De novo structure generation from mass spectra” has appeared in Nature Methods, and we are thrilled to be part of this research.

In short, MSNovelist is a computational method that transforms the tandem mass spectrum of a small molecule into its molecular structure. Full stop. That sounds shocking and surprising, and in fact, I view this as the “final frontier” in small molecule mass spectrometry. It is understood that “certain restrictions apply”, as they say. But Michele has undertaken an huge amount amount of work to clearly show what the method can do and what it cannot; for example, an in-depth evaluation against a method that basically ignores MS/MS data when generating structures. That methods works disturbingly well; but if you think about it for some time, it becomes clear why: I just say, “blockbuster metabolites“. Michele has written a very nice blog post where he explains in much detail what MSNovelist is all about. If I had to recap the method in one sentence, then this is it: MSNovelist gives you a head start for the de novo elucidation of a novel structure.

We will make MSNovelist available through SIRIUS in an upcoming release — hopefully, soon.

ps. Also read the article “Some assembly required” by Corey D. Broeckling.

Full citation: M. A. Stravs, K. Dührkop, S. Böcker, and Nicola Zamboni. MSNovelist: De novo structure generation from mass spectra. Nature Methods, 2022. https://doi.org/10.1038/s41592-022-01486-3