Meet Marcus and Sebastian at the conference of the Metabolomics Society 2019.

On Monday and Tuesday, Marcus will present a poster (539) about SIRIUS 4 and turning tandem mass spectra into metabolite structure information.

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.

Is it a script? Is it a textbook? Or maybe, lecture notes? See all the details at https://bio.informatik.uni-jena.de/textbook-algoms/!

Version 0.7 is now available and contains chapters on p-value calculation and decoy databases.

The previous version was not publicly announced because I hoped to get some feedback first; but somehow, it did not work out this way…

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!

…but only today, he finished the last important step of becoming a PhD. (Darned fall fair.) Congratulations!

Congratulations!

Congratulations!

…and we forgot to applaud Martin E who defended on May 4th – (late) congratulations to him, too!

Sebastian was awarded the prize for best contributed talk at Mittelerde (Central German Meeting on Bioinformatics) 2018 at the Hochschule Mittweida. Many thanks!

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.