TY - JOUR A1 - Schorn, Michelle A. A1 - Verhoeven, Stefan A1 - Ridder, Lars A1 - Huber, Florian A1 - Acharya, Deepa D. A1 - Aksenov, Alexander A. A1 - Aleti, Gajender A1 - Moghaddam, Jamshid Amiri A1 - Aron, Allegra T. A1 - Aziz, Saefuddin A1 - Bauermeister, Anelize A1 - Bauman, Katherine D. A1 - Baunach, Martin A1 - Beemelmanns, Christine A1 - Beman, J. Michael A1 - Berlanga-Clavero, María Victoria A1 - Blacutt, Alex A. A1 - Bode, Helge B. A1 - Boullie, Anne A1 - Brejnrod, Asker A1 - Bugni, Tim S. A1 - Calteau, Alexandra A1 - Cao, Liu A1 - Carrión, Víctor J. A1 - Castelo-Branco, Raquel A1 - Chanana, Shaurya A1 - Chase, Alexander B. A1 - Chevrette, Marc G. A1 - Costa-Lotufo, Leticia V. A1 - Crawford, Jason M. A1 - Currie, Cameron R. A1 - Cuypers, Bart A1 - Dang, Tam A1 - de Rond, Tristan A1 - Demko, Alyssa M. A1 - Dittmann, Elke A1 - Du, Chao A1 - Drozd, Christopher A1 - Dujardin, Jean-Claude A1 - Dutton, Rachel J. A1 - Edlund, Anna A1 - Fewer, David P. A1 - Garg, Neha A1 - Gauglitz, Julia M. A1 - Gentry, Emily C. A1 - Gerwick, Lena A1 - Glukhov, Evgenia A1 - Gross, Harald A1 - Gugger, Muriel A1 - Guillén Matus, Dulce G. A1 - Helfrich, Eric J. N. A1 - Hempel, Benjamin-Florian A1 - Hur, Jae-Seoun A1 - Iorio, Marianna A1 - Jensen, Paul R. A1 - Kang, Kyo Bin A1 - Kaysser, Leonard A1 - Kelleher, Neil L. A1 - Kim, Chung Sub A1 - Kim, Ki Hyun A1 - Koester, Irina A1 - König, Gabriele M. A1 - Leao, Tiago A1 - Lee, Seoung Rak A1 - Lee, Yi-Yuan A1 - Li, Xuanji A1 - Little, Jessica C. A1 - Maloney, Katherine N. A1 - Männle, Daniel A1 - Martin H, Christian A1 - McAvoy, Andrew C. A1 - Metcalf, Willam W. A1 - Mohimani, Hosein A1 - Molina-Santiago, Carlos A1 - Moore, Bradley S. A1 - Mullowney, Michael W. A1 - Muskat, Mitchell A1 - Nothias, Louis-Félix A1 - O'Neill, Ellis C. A1 - Parkinson, Elizabeth I. A1 - Petras, Daniel A1 - Piel, Jörn A1 - Pierce, Emily C. A1 - Pires, Karine A1 - Reher, Raphael A1 - Romero, Diego A1 - Roper, M. Caroline A1 - Rust, Michael A1 - Saad, Hamada A1 - Saenz, Carmen A1 - Sanchez, Laura M. A1 - Sørensen, Søren Johannes A1 - Sosio, Margherita A1 - Süssmuth, Roderich D. A1 - Sweeney, Douglas A1 - Tahlan, Kapil A1 - Thomson, Regan J. A1 - Tobias, Nicholas J. A1 - Trindade-Silva, Amaro E. A1 - van Wezel, Gilles P. A1 - Wang, Mingxun A1 - Weldon, Kelly C. A1 - Zhang, Fan A1 - Ziemert, Nadine A1 - Duncan, Katherine R. A1 - Crüsemann, Max A1 - Rogers, Simon A1 - Dorrestein, Pieter C. A1 - Medema, Marnix H. A1 - van der Hooft, Justin J. J. T1 - A community resource for paired genomic and metabolomic data mining JF - Nature Chemical Biology Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-34708 UR - http://www.ncbi.nlm.nih.gov/pubmed/33589842 SN - 1552-4469 N1 - Additional information: Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41589-020-00724-z. VL - 17 IS - 4 SP - 363 EP - 368 PB - Nature ER - TY - JOUR A1 - Migueles, Jairo H. A1 - Rowlands, Alex V. A1 - Huber, Florian A1 - Sabia, Séverine A1 - van Hees, Vincent T. T1 - GGIR: A Research Community–Driven Open Source R Package for Generating Physical Activity and Sleep Outcomes From Multi-Day Raw Accelerometer Data JF - Journal for the Measurement of Physical Behaviour Y1 - 2019 U6 - https://doi.org/10.1123/jmpb.2018-0063 SN - 2575-6605 VL - 2 IS - 3 SP - 188 EP - 196 PB - Human Kinetics ER - TY - JOUR A1 - Beniddir, Mehdi A. A1 - Kang, Kyo Bin A1 - Genta-Jouve, Grégory A1 - Huber, Florian A1 - Rogers, Simon A1 - van der Hooft, Justin J. J. T1 - Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches JF - Natural Product Reports Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-34772 SN - 1460-4752 VL - 38 IS - 11 SP - 1967 EP - 1993 PB - The Royal Society of Chemistry ER - TY - JOUR A1 - Huber, Florian A1 - Boire, Adeline A1 - López, Magdalena Preciado A1 - Koenderink, Gijsje H. T1 - Cytoskeletal crosstalk: when three different personalities team up JF - Current Opinion in Cell Biology Y1 - 2015 U6 - https://doi.org/10.1016/j.ceb.2014.10.005 SN - 1879-0410 VL - 32 SP - 39 EP - 47 PB - Elsevier ER - TY - JOUR A1 - Huber, Florian A1 - Käs, Josef T1 - Self-regulative organization of the cytoskeleton JF - Cytoskeleton N2 - Despite its impressive complexity the cytoskeleton succeeds to persistently organize itself and thus the cells' interior. In contrast to classical man-made machines, much of the cellular organization originates from inherent self-assembly and self-organization allowing a high degree of autonomy for various functional units. Recent experimental and theoretical studies revealed numerous examples of cytoskeleton components that arrange and organize in a self-regulative way. In the present review we want to shortly summarize some of the principle mechanisms that are able to inherently trigger and regulate the cytoskeleton organization. Although taken individually most of these regulative principles are rather simple with intuitively predictable consequences, combinations of two or more of these mechanisms can quickly give rise to very complex, unexpected behavior and might even be able to explain the formation of different functional units out of a common pool of available building blocks. Y1 - 2011 UR - http://www.ncbi.nlm.nih.gov/pubmed/21438165 U6 - https://doi.org/10.1002/cm.20509 SN - 1949-3592 N1 - Acknowledgment We would like to thank Dan Strehle for critical reading ofthis manuscript. This work was funded by the Leipzig School of Natural Sciences ‘‘BuildMoNa’’ as well as bythe Sa¨chsische Aufbaubank project number 13403 VL - 68 IS - 5 SP - 259 EP - 265 PB - Wiley ER - TY - JOUR A1 - Huber, Florian A1 - Käs, Josef A1 - Stuhrmann, Björn T1 - Growing actin networks form lamellipodium and lamellum by self-assembly JF - Biophysical Journal N2 - Many different cell types are able to migrate by formation of a thin actin-based cytoskeletal extension. Recently, it became evident that this extension consists of two distinct substructures, designated lamellipodium and lamellum, which differ significantly in their kinetic and kinematic properties as well as their biochemical composition. We developed a stochastic two-dimensional computer simulation that includes chemical reaction kinetics, G-actin diffusion, and filament transport to investigate the formation of growing actin networks in migrating cells. Model parameters were chosen based on experimental data or theoretical considerations. In this work, we demonstrate the system's ability to form two distinct networks by self-organization. We found a characteristic transition in mean filament length as well as a distinct maximum in depolymerization flux, both within the first 1-2 microm. The separation into two distinct substructures was found to be extremely robust with respect to initial conditions and variation of model parameters. We quantitatively investigated the complex interplay between ADF/cofilin and tropomyosin and propose a plausible mechanism that leads to spatial separation of, respectively, ADF/cofilin- or tropomyosin-dominated compartments. Tropomyosin was found to play an important role in stabilizing the lamellar actin network. Furthermore, the influence of filament severing and annealing on the network properties is explored, and simulation data are compared to existing experimental data. KW - Open Archive Y1 - 2008 UR - http://www.ncbi.nlm.nih.gov/pubmed/18708450 U6 - https://doi.org/10.1529/biophysj.108.134817 SN - 1542-0086 VL - 95 IS - 12 SP - 5508 EP - 5523 PB - Biophysical Society ER - TY - JOUR A1 - Huber, Florian A1 - Schnauß, Jörg A1 - Rönicke, S. A1 - Rauch, P. A1 - Müller, K. A1 - Fütterer, C. A1 - Käs, Josef T1 - Emergent complexity of the cytoskeleton: from single filaments to tissue JF - Advances in Physics N2 - Despite their overwhelming complexity, living cells display a high degree of internal mechanical and functional organization which can largely be attributed to the intracellular biopolymer scaffold, the cytoskeleton. Being a very complex system far from thermodynamic equilibrium, the cytoskeleton's ability to organize is at the same time challenging and fascinating. The extensive amounts of frequently interacting cellular building blocks and their inherent multifunctionality permits highly adaptive behavior and obstructs a purely reductionist approach. Nevertheless (and despite the field's relative novelty), the physics approach has already proved to be extremely successful in revealing very fundamental concepts of cytoskeleton organization and behavior. This review aims at introducing the physics of the cytoskeleton ranging from single biopolymer filaments to multicellular organisms. Throughout this wide range of phenomena, the focus is set on the intertwined nature of the different physical scales (levels of complexity) that give rise to numerous emergent properties by means of self-organization or self-assembly. Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-34812 UR - http://www.ncbi.nlm.nih.gov/pubmed/24748680 SN - 1460-6976 N1 - Acknowledgment: We thank Klaus Kroy, Dan Strehle, Tobias Kießling, Matti Gralka, Harald Herrmann, Denis Wirtz, and Michael Höckel for very helpful comments, discussions, and proof reading. We further like to thank Steve Pawlizak, Thomas Fuhs, Daniel Koch, BjöStuhrmann, and Franziska Wetzel for contributing data and additional illustrations. This work was supported by the graduate school “Building with Molecules and Nano-Objects” (BuildMoNa). VL - 62 IS - 1 SP - 1 EP - 112 PB - Taylor & Francis ER - TY - JOUR A1 - Huber, Florian A1 - Strehle, Dan A1 - Kaes, Josef T1 - Counterion-induced formation of regular actin bundle networks JF - Soft Matter Y1 - 2012 SN - 1744-6848 VL - 8 IS - 4 SP - 931 EP - 936 PB - Royal Society of Chemistry ER - TY - JOUR A1 - Huber, Florian A1 - Strehle, Dan A1 - Schnauß, Jörg A1 - Käs, Josef T1 - Formation of regularly spaced networks as a general feature of actin bundle condensation by entropic forces JF - New Journal of Physics KW - DOAJ Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-34833 SN - 1367-2630 VL - 17 IS - 4 PB - IOP Publishing ER - TY - JOUR A1 - Huber, Florian A1 - van der Burg, Sven A1 - van der Hooft, Justin J. J. A1 - Ridder, Lars T1 - MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra JF - Journal of Cheminformatics N2 - Mass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are generally considered to be characteristic signatures of the chemical compound they originate from, yet the chemical structure itself usually cannot be easily deduced from the spectrum. Often, spectral similarity measures are used as a proxy for structural similarity but this approach is strongly limited by a generally poor correlation between both metrics. Here, we propose MS2DeepScore: a novel Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra. Using a cleaned dataset of > 100,000 mass spectra of about 15,000 unique known compounds, we trained MS2DeepScore to predict structural similarity scores for spectrum pairs with high accuracy. In addition, sampling different model varieties through Monte-Carlo Dropout is used to further improve the predictions and assess the model's prediction uncertainty. On 3600 spectra of 500 unseen compounds, MS2DeepScore is able to identify highly-reliable structural matches and to predict Tanimoto scores for pairs of molecules based on their fragment spectra with a root mean squared error of about 0.15. Furthermore, the prediction uncertainty estimate can be used to select a subset of predictions with a root mean squared error of about 0.1. Furthermore, we demonstrate that MS2DeepScore outperforms classical spectral similarity measures in retrieving chemically related compound pairs from large mass spectral datasets, thereby illustrating its potential for spectral library matching. Finally, MS2DeepScore can also be used to create chemically meaningful mass spectral embeddings that could be used to cluster large numbers of spectra. Added to the recently introduced unsupervised Spec2Vec metric, we believe that machine learning-supported mass spectral similarity measures have great potential for a range of metabolomics data processing pipelines. KW - DOAJ Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-34847 UR - http://www.ncbi.nlm.nih.gov/pubmed/34715914 SN - 1758-2946 N1 - J.J.J.v.d.H. acknowledges funding from an ASDI eScience grant, ASDI.2017.030, from the Netherlands eScience Center. VL - 13 IS - 1 PB - Cold Spring Harbor Laboratory ER - TY - JOUR A1 - Huber, Florian A1 - Verhoeven, Stefan A1 - Meijer, Christiaan A1 - Spreeuw, Hanno A1 - Castilla, Efraín A1 - Geng, Cunliang A1 - van der Hooft, Justin J. J. A1 - Rogers, Simon A1 - Belloum, Adam A1 - Diblen, Faruk A1 - Spaaks, Jurriaan H. T1 - matchms - processing and similarity evaluation of mass spectrometry data JF - Journal of Open Source Software KW - DOAJ Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-34856 SN - 2475-9066 VL - 5 IS - 52 PB - Cold Spring Harbor Laboratory ER - TY - JOUR A1 - Preciado López, Magdalena A1 - Huber, Florian A1 - Grigoriev, Ilya A1 - Steinmetz, Michel O. A1 - Akhmanova, Anna A1 - Dogterom, Marileen A1 - Koenderink, Gijsje H. T1 - In vitro reconstitution of dynamic microtubules interacting with actin filament networks JF - Methods in Enzymology Y1 - 2014 U6 - https://doi.org/10.1016/B978-0-12-397924-7.00017-0 SN - 1557-7988 VL - 540 SP - 301 EP - 320 PB - Elsevier ER - TY - JOUR A1 - Preciado López, Magdalena A1 - Huber, Florian A1 - Grigoriev, Ilya A1 - Steinmetz, Michel O. A1 - Akhmanova, Anna A1 - Koenderink, Gijsje H. A1 - Dogterom, Marileen T1 - Actin-microtubule coordination at growing microtubule ends JF - Nature Communications N2 - To power dynamic processes in cells, the actin and microtubule cytoskeletons organize into complex structures. Although it is known that cytoskeletal coordination is vital for cell function, the mechanisms by which cross-linking proteins coordinate actin and microtubule activities remain poorly understood. In particular, it is unknown how the distinct mechanical properties of different actin architectures modulate the outcome of actin-microtubule interactions. To address this question, we engineered the protein TipAct, which links growing microtubule ends via end-binding proteins to actin filaments. We show that growing microtubules can be captured and guided by stiff actin bundles, leading to global actin-microtubule alignment. Conversely, growing microtubule ends can transport, stretch and bundle individual actin filaments, thereby globally defining actin filament organization. Our results provide a physical basis to understand actin-microtubule cross-talk, and reveal that a simple cross-linker can enable a mechanical feedback between actin and microtubule organization that is relevant to diverse biological contexts. KW - DOAJ Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-34878 SN - 2041-1723 VL - 5 PB - Springer Nature ER - TY - JOUR A1 - Siccardi, Stefano A1 - Adamatzky, Andrew A1 - Tuszyński, Jack A1 - Huber, Florian A1 - Schnauß, Jörg T1 - Actin networks voltage circuits JF - Physical Review E Y1 - 2020 U6 - https://doi.org/10.1103/PhysRevE.101.052314 SN - 2470-0053 VL - 101 IS - 5-1 PB - American Physical Society (APS) ER - TY - JOUR A1 - Smith, David A1 - Gentry, Brian A1 - Stuhrmann, Björn A1 - Huber, Florian A1 - Strehle, D. A.N. A1 - Brunner, Claudia A1 - Koch, Daniel A1 - Steinbeck, Matthias A1 - Betz, Timo A1 - Käs, Josef A. T1 - The cytoskeleton: An active polymer-based scaffold JF - Biophysical Reviews and Letters Y1 - 2009 U6 - https://doi.org/10.1142/S1793048009000983 SN - 1793-7035 VL - 04 SP - 179 EP - 208 ER - TY - JOUR A1 - Stuhrmann, Björn A1 - Huber, Florian A1 - Käs, Josef T1 - Robust organizational principles of protrusive biopolymer networks in migrating living cells JF - Plos One N2 - Cell migration is associated with the dynamic protrusion of a thin actin-based cytoskeletal extension at the cell front, which has been shown to consist of two different substructures, the leading lamellipodium and the subsequent lamellum. While the formation of the lamellipodium is increasingly well understood, organizational principles underlying the emergence of the lamellum are just beginning to be unraveled. We report here on a 1D mathematical model which describes the reaction-diffusion processes of a polarized actin network in steady state, and reproduces essential characteristics of the lamellipodium-lamellum system. We observe a steep gradient in filament lengths at the protruding edge, a local depolymerization maximum a few microns behind the edge, as well as a differential dominance of the network destabilizer ADF/cofilin and the stabilizer tropomyosin. We identify simple and robust organizational principles giving rise to the derived network characteristics, uncoupled from the specifics of any molecular implementation, and thus plausibly valid across cell types. An analysis of network length dependence on physico-chemical system parameters implies that to limit array treadmilling to cellular dimensions, network growth has to be truncated by mechanisms other than aging-induced depolymerization, e.g., by myosin-associated network dissociation at the transition to the cell body. Our work contributes to the analytical understanding of the cytoskeletal extension's bisection into lamellipodium and lamellum and sheds light on how cells organize their molecular machinery to achieve motility. KW - DOAJ Y1 - 2011 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-34901 SN - 1932-6203 N1 - Funding: This work has been supported by the Leipzig Graduate College ‘‘GK 1097 InterNeuro - Interdisciplinary Approaches in Cellular Neuroscience’’ (http:// www.uni-leipzig.de/˜ineuro/) and the Graduate School ‘‘Leipzig School of Natural Sciences - Building with Molecules and Nano-objects (BuildMoNa)’’ (http://www. buildmona.de/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. VL - 6 IS - 1 PB - Public Library of Science (PLoS) ER - TY - JOUR A1 - Adamatzky, Andrew A1 - Huber, Florian A1 - Schnauß, Jörg T1 - Computing on actin bundles network JF - Scientific Reports N2 - Actin filaments are conductive to ionic currents, mechanical and voltage solitons. These travelling localisations can be utilised to generate computing circuits from actin networks. The propagation of localisations on a single actin filament is experimentally unfeasible to control. Therefore, we consider excitation waves propagating on bundles of actin filaments. In computational experiments with a two-dimensional slice of an actin bundle network we show that by using an arbitrary arrangement of electrodes, it is possible to implement two-inputs-one-output circuits. Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-34665 UR - http://www.ncbi.nlm.nih.gov/pubmed/31685834 SN - 2045-2322 VL - 9 IS - 1 PB - Springer Nature ER - TY - JOUR A1 - Adamatzky, Andrew A1 - Schnauß, Jörg A1 - Huber, Florian T1 - Actin droplet machine JF - Royal Society Open Science N2 - The actin droplet machine is a computer model of a three-dimensional network of actin bundles developed in a droplet of a physiological solution, which implements mappings of sets of binary strings. The actin bundle network is conductive to travelling excitations, i.e. impulses. The machine is interfaced with an arbitrary selected set of k electrodes through which stimuli, binary strings of length k represented by impulses generated on the electrodes, are applied and responses are recorded. The responses are recorded in a form of impulses and then converted to binary strings. The machine's state is a binary string of length k: if there is an impulse recorded on the ith electrode, there is a '1' in the ith position of the string, and '0' otherwise. We present a design of the machine and analyse its state transition graphs. We envisage that actin droplet machines could form an elementary processor of future massive parallel computers made from biopolymers. Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-34675 UR - http://www.ncbi.nlm.nih.gov/pubmed/31903204 SN - 2054-5703 N1 - A.A. was partially supported by EPSRC grant no. EP/P016677/1. The authors declare that they have no competing interests. VL - 6 IS - 12 PB - Royal Soc. Publ. ER - TY - JOUR A1 - Huber, Florian A1 - Ridder, Lars A1 - Verhoeven, Stefan A1 - Spaaks, Jurriaan H. A1 - Diblen, Faruk A1 - Rogers, Simon A1 - van der Hooft, Justin J. J. T1 - Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships JF - PLOS Computational Biology N2 - Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm-Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds. Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-34687 UR - http://www.ncbi.nlm.nih.gov/pubmed/33591968 SN - 1553-7358 N1 - Data Availability Statement: The pre-processed, cleaned dataset with all positive ionization mode spectra can be downloaded from https://doi.org/10.5281/zenodo.3978118, the original raw data can be accessed from https://doi.org/10.5281/zenodo.3979010. Funding: J.J.J.v.d.H. acknowledges funding from an ASDI eScience grant, ASDI.2017.030, from the Netherlands eScience Center—NLeSC, www.esciencecenter.nl, The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. VL - 17 IS - 2 PB - Cold Spring Harbor Laboratory ER - TY - JOUR A1 - Mullowney, Michael W. A1 - Duncan, Katherine R. A1 - Elsayed, Somayah S. A1 - Garg, Neha A1 - van der Hooft, Justin J. J. A1 - Martin, Nathaniel I. A1 - Meijer, David A1 - Terlouw, Barbara R. A1 - Biermann, Friederike A1 - Blin, Kai A1 - Durairaj, Janani A1 - Gorostiola González, Marina A1 - Helfrich, Eric J. N. A1 - Huber, Florian A1 - Leopold-Messer, Stefan A1 - Rajan, Kohulan A1 - de Rond, Tristan A1 - van Santen, Jeffrey A. A1 - Sorokina, Maria A1 - Balunas, Marcy J. A1 - Beniddir, Mehdi A. A1 - van Bergeijk, Doris A. A1 - Carroll, Laura M. A1 - Clark, Chase M. A1 - Clevert, Djork-Arné A1 - Dejong, Chris A. A1 - Du, Chao A1 - Ferrinho, Scarlet A1 - Grisoni, Francesca A1 - Hofstetter, Albert A1 - Jespers, Willem A1 - Kalinina, Olga V. A1 - Kautsar, Satria A. A1 - Kim, Hyunwoo A1 - Leao, Tiago F. A1 - Masschelein, Joleen A1 - Rees, Evan R. A1 - Reher, Raphael A1 - Reker, Daniel A1 - Schwaller, Philippe A1 - Segler, Marwin A1 - Skinnider, Michael A. A1 - Walker, Allison S. A1 - Willighagen, Egon L. A1 - Zdrazil, Barbara A1 - Ziemert, Nadine A1 - Goss, Rebecca J. M. A1 - Guyomard, Pierre A1 - Volkamer, Andrea A1 - Gerwick, William H. A1 - Kim, Hyun Uk A1 - Müller, Rolf A1 - van Wezel, Gilles P. A1 - van Westen, Gerard J. P. A1 - Hirsch, Anna K. H. A1 - Linington, Roger G. A1 - Robinson, Serina L. A1 - Medema, Marnix H. T1 - Artificial intelligence for natural product drug discovery JF - Nature Reviews Drug Discovery KW - Maschinelles Lernen KW - Arzneimittelforschung KW - Deep learning KW - Omics-Technologie Y1 - 2023 U6 - https://doi.org/10.1038/s41573-023-00774-7 SN - 1474-1776 VL - 22 IS - 11 SP - 895 EP - 916 PB - Springer Nature ER - TY - CHAP A1 - Gaudry, Arnaud A1 - Huber, Florian A1 - Flückiger, Julien A1 - Quirós, L A1 - Rutz, Adriano A1 - Kaiser, M A1 - Grondin, A A1 - Marcourt, Laurence A1 - Ferreira Queiroz, E A1 - Wolfender, Jean-Luc A1 - Allard, Pierre-Marie T1 - Short Lecture “Mass spectrometry-based sample vectorization for exploration of large chemodiverse datasets and efficient identification of new antiparasitic compounds” T2 - Planta Medica KW - Massenspektrometrie KW - Naturstoffchemie KW - Antiparasitäres Mittel KW - Arzneimittelforschung Y1 - 2022 U6 - https://doi.org/10.1055/s-0042-1758983 SN - 1439-0221 VL - 88 IS - 15 PB - Thieme ER - TY - CHAP A1 - Adamatzky, Andrew A1 - Huber, Florian A1 - Schnauß, Jörg ED - Adamatzky, Andrew T1 - Computing on Actin Bundles Network T2 - Actin Computation: Unlocking the Potential of Actin Filaments for Revolutionary Computing System KW - Actin-Filament KW - Informationsverarbeitung KW - Bioinformatik Y1 - 2024 SN - 9789811285066 U6 - https://doi.org/10.1142/9789811285073_0013 SN - 2737-520X VL - Wspc Book Series in Unconventional Computing, Vol. 3 SP - 245 EP - 261 PB - WORLD SCIENTIFIC ER - TY - CHAP A1 - Siccardi, Stefano A1 - Adamatzky, Andrew A1 - Tuszyński, Jack A1 - Huber, Florian A1 - Schnauß, Jörg ED - Adamatzky, Andrew T1 - Actin Networks Voltage Circuits T2 - Actin Computation: Unlocking the Potential of Actin Filaments for Revolutionary Computing Systems KW - Actin-Filament KW - Informationsverarbeitung KW - Bioinformatik Y1 - 2024 SN - 9789811285066 U6 - https://doi.org/10.1142/9789811285073_0006 SN - 2737-520X VL - Wspc Book Series in Unconventional Computing, Vol. 3 SP - 123 EP - 143 PB - WORLD SCIENTIFIC ER - TY - CHAP A1 - Adamatzky, Andrew A1 - Huber, Florian A1 - Schnauß, Jörg ED - Adamatzky, Andrew T1 - Actin Droplet Machine T2 - Actin Computation: Unlocking the Potential of Actin Filaments for Revolutionary Computing Systems Y1 - 2024 SN - 9789811285066 U6 - https://doi.org/10.1142/9789811285073_0014 SN - 2737-520X VL - Wspc Book Series in Unconventional Computing, Vol. 3 SP - 263 EP - 284 PB - WORLD SCIENTIFIC ER - TY - BOOK A1 - Huber, Florian T1 - Hands-on Introduction to Data Science with Python N2 - In today’s world, data is generated at an unprecedented pace, and our ability to harness it is changing the way we live, work, and even think. Data science, the interdisciplinary field that blends statistics, computer science, and domain-specific knowledge, empowers us to extract insights from this vast ocean of data. As data science becomes increasingly essential across various industries and sectors, there is a growing need for skilled professionals who can make sense of data and transform it into actionable information. This book is designed to give you a very broad and at the same time a very practical hands-on tour through the full spectrum of data science approaches KW - Data Science KW - Python (Programmiersprache) KW - Lehrmittel Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-49548 N1 - The DOI represents all versions, and will always resolve to the latest one. The online version of this book can be found here: https://florian-huber.github.io/data_science_course/book/intro.html All materials and source code to render the book can be found on GitHub: https://github.com/florian-huber/data_science_course PB - Zenodo ET - v0.21 ER - TY - JOUR A1 - Haberfehlner, Helga A1 - van de Ven, Shankara S. A1 - van der Burg, Sven A. A1 - Huber, Florian A1 - Georgievska, Sonja A1 - Aleo, Ignazio A1 - Harlaar, Jaap A1 - Bonouvrié, Laura A. A1 - van der Krogt, Marjolein M. A1 - Buizer, Annemieke I. T1 - Towards automated video-based assessment of dystonia in dyskinetic cerebral palsy: A novel approach using markerless motion tracking and machine learning JF - Frontiers in Robotics and AI Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-40592 SN - 2296-9144 VL - 10 PB - Frontiers ER - TY - JOUR A1 - Bittremieux, Wout A1 - Schmid, Robin A1 - Huber, Florian A1 - van der Hooft, Justin J. J. A1 - Wang, Mingxun A1 - Dorrestein, Pieter C. T1 - Comparison of Cosine, Modified Cosine, and Neutral Loss Based Spectrum Alignment For Discovery of Structurally Related Molecules JF - Journal of the American Society for Mass Spectrometry KW - Modification KW - Precursors KW - Mass spectrometry KW - Anatomy KW - Peptides and proteins Y1 - 2022 U6 - https://doi.org/10.1021/jasms.2c00153 SN - 1044-0305 N1 - Eingereichte Version ist online verfügbar (Open Access Grün) https://doi.org/10.1101/2022.06.01.494370 unter CC BY 4.0 VL - 33 IS - 9 SP - 1733 EP - 1744 PB - American Chemical Society (ACS) ER - TY - JOUR A1 - Bittremieux, Wout A1 - Levitsky, Lev A1 - Pilz, Matteo A1 - Sachsenberg, Timo A1 - Huber, Florian A1 - Wang, Mingxun A1 - Dorrestein, Pieter C. T1 - Unified and Standardized Mass Spectrometry Data Processing in Python Using spectrum_utils JF - Journal of proteome research KW - proteomics KW - mass spectrometry KW - metabolomics KW - Python KW - open source Y1 - 2023 U6 - https://doi.org/10.1021/acs.jproteome.2c00632 SN - 1535-3893 N1 - Eingereichte Version ist online verfügbar (Open Access Grün) unter: https://www.biorxiv.org/content/10.1101/2022.10.04.510894v1 VL - 22 IS - 2 SP - 625 EP - 631 PB - American Chemical Society (ACS) ER - TY - JOUR A1 - de Jonge, Niek F. A1 - Mildau, Kevin A1 - Meijer, David A1 - Louwen, Joris J. R. A1 - Bueschl, Christoph A1 - Huber, Florian A1 - van der Hooft, Justin J. J. T1 - Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools JF - Metabolomics N2 - Background Untargeted metabolomics approaches based on mass spectrometry obtain comprehensive profiles of complex biological samples. However, on average only 10% of the molecules can be annotated. This low annotation rate hampers biochemical interpretation and effective comparison of metabolomics studies. Furthermore, de novo structural characterization of mass spectral data remains a complicated and time-intensive process. Recently, the field of computational metabolomics has gained traction and novel methods have started to enable large-scale and reliable metabolite annotation. Molecular networking and machine learning-based in-silico annotation tools have been shown to greatly assist metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery. Aim of review We highlight recent advances in computational metabolite annotation workflows with a special focus on their evaluation and comparison with other tools. Whilst the progress is substantial and promising, we also argue that inconsistencies in benchmarking different tools hamper users from selecting the most appropriate and promising method for their research. We summarize benchmarking strategies of the different tools and outline several recommendations for benchmarking and comparing novel tools. Key scientific concepts of review This review focuses on recent advances in mass spectral library-based and machine learning-supported metabolite annotation workflows. We discuss large-scale library matching and analogue search, the current bloom of mass spectral similarity scores, and how molecular networking has changed the field. In addition, the potentials and challenges of machine learning-supported metabolite annotation workflows are highlighted. Overall, recent developments in computational metabolomics have started to fundamentally change metabolomics workflows, and we expect that as a community we will be able to overcome current method performance ambiguities and annotation bottlenecks. KW - Metabolomik KW - Massenspektrometrie KW - Maschinelles Lernen KW - Benchmarking KW - Mass fragmentation spectra Y1 - 2022 U6 - https://doi.org/10.1007/s11306-022-01963-y SN - 1573-3890 VL - 18 IS - 12 PB - Springer Nature ER - TY - JOUR A1 - de Jonge, Niek F. A1 - Louwen, Joris J. R. A1 - Chekmeneva, Elena A1 - Camuzeaux, Stephane A1 - Vermeir, Femke J. A1 - Jansen, Robert S. A1 - Huber, Florian A1 - van der Hooft, Justin J. J. T1 - MS2Query: reliable and scalable MS2 mass spectra-based analogue search JF - Nature Communications N2 - Metabolomics-driven discoveries of biological samples remain hampered by the grand challenge of metabolite annotation and identification. Only few metabolites have an annotated spectrum in spectral libraries; hence, searching only for exact library matches generally returns a few hits. An attractive alternative is searching for so-called analogues as a starting point for structural annotations; analogues are library molecules which are not exact matches but display a high chemical similarity. However, current analogue search implementations are not yet very reliable and relatively slow. Here, we present MS2Query, a machine learning-based tool that integrates mass spectral embedding-based chemical similarity predictors (Spec2Vec and MS2Deepscore) as well as detected precursor masses to rank potential analogues and exact matches. Benchmarking MS2Query on reference mass spectra and experimental case studies demonstrate improved reliability and scalability. Thereby, MS2Query offers exciting opportunities to further increase the annotation rate of metabolomics profiles of complex metabolite mixtures and to discover new biology. KW - Metabolomik KW - Massenspektrometrie KW - Maschinelles Lernen KW - Reproduzierbarkeit KW - Metabolomics KW - Mass Spectrometry KW - Machine Learning KW - Complex Mixtures Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-46105 SN - 2041-1723 VL - 14 PB - Springer ER - TY - JOUR A1 - Kamiloğlu, Roza G. A1 - Sun, Rui A1 - Bos, Patrick A1 - Huber, Florian A1 - Attema, Jisk Jakob A1 - Sauter, Disa A. T1 - Tickling induces a unique type of spontaneous laughter JF - Biology Letters N2 - Laughing is ubiquitous in human life, yet what causes it and how it sounds is highly variable. Considering this diversity, we sought to test whether there are fundamentally different kinds of laughter. Here, we sampled spontaneous laughs (n = 887) from a wide range of everyday situations (e.g. comedic performances and playful pranks). Machine learning analyses showed that laughs produced during tickling are acoustically distinct from laughs triggered by other kinds of events (verbal jokes, watching something funny or witnessing someone else’s misfortune). In a listening experiment (n = 201), participants could accurately identify tickling-induced laughter, validating that such laughter is not only acoustically but also perceptually distinct. A second listening study (n = 210) combined with acoustic analyses indicates that tickling-induced laughter involves less vocal control than laughter produced in other contexts. Together, our results reveal a unique acoustic and perceptual profile of laughter induced by tickling, an evolutionarily ancient play behaviour, distinguishing it clearly from laughter caused by other triggers. This study showcases the power of machine learning in uncovering patterns within complex behavioural phenomena, providing a window into the evolutionary significance of ticking-induced laughter. KW - Lachen KW - Kitzel KW - Akustik KW - Maschinelles Lernen KW - Spontaneität KW - Evolutionsbiologie KW - Verhalten Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-47446 SN - 1744-957X VL - 20 IS - 11 PB - The Royal Society ER - TY - JOUR A1 - Mildau, Kevin A1 - Ehlers, Henry A1 - Meisenburg, Mara A1 - Del Pup, Elena A1 - Koetsier, Robert A. A1 - Torres Ortega, Laura Rosina A1 - de Jonge, Niek F. A1 - Singh, Kumar Saurabh A1 - Ferreira, Dora A1 - Othibeng, Kgalaletso A1 - Tugizimana, Fidele A1 - Huber, Florian A1 - van der Hooft, Justin J. J. T1 - Effective data visualization strategies in untargeted metabolomics JF - Natural Product Reports N2 - LC-MS/MS-based untargeted metabolomics is a rapidly developing research field spawning increasing numbers of computational metabolomics tools assisting researchers with their complex data processing, analysis, and interpretation tasks. In this article, we review the entire untargeted metabolomics workflow from the perspective of information visualization, visual analytics and visual data integration. Data visualization is a crucial step at every stage of the metabolomics workflow, where it provides core components of data inspection, evaluation, and sharing capabilities. However, due to the large number of available data analysis tools and corresponding visualization components, it is hard for both users and developers to get an overview of what is already available and which tools are suitable for their analysis. In addition, there is little cross-pollination between the fields of data visualization and metabolomics, leaving visual tools to be designed in a secondary and mostly ad hoc fashion. With this review, we aim to bridge the gap between the fields of untargeted metabolomics and data visualization. First, we introduce data visualization to the untargeted metabolomics field as a topic worthy of its own dedicated research, and provide a primer on cutting-edge visualization research into data visualization for both researchers as well as developers active in metabolomics. We extend this primer with a discussion of best practices for data visualization as they have emerged from data visualization studies. Second, we provide a practical roadmap to the visual tool landscape and its use within the untargeted metabolomics field. Here, for several computational analysis stages within the untargeted metabolomics workflow, we provide an overview of commonly used visual strategies with practical examples. In this context, we will also outline promising areas for further research and development. We end the review with a set of recommendations for developers and users on how to make the best use of visualizations for more effective and transparent communication of results. KW - Metabolomik KW - Visualisierung Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-47596 SN - 0265-0568 PB - Royal Society of Chemistry ER - TY - INPR A1 - de Jonge, Niek F. A1 - Joas, David A1 - Truong, Lem-Joe A1 - van der Hooft, Justin J.J. A1 - Huber, Florian T1 - Reliable cross-ion mode chemical similarity prediction between MS2 spectra T2 - biorxiv N2 - Mass spectrometry is commonly used to characterize metabolites in untargeted metabolomics. This can be done in positive and negative ionization mode, a choice typically guided by the fraction of metabolites a researcher is interested in. During analysis, mass spectral comparisons are widely used to enable annotation through reference libraries and to facilitate data organization through networking. However, until now, such comparisons between mass spectra were restricted to mass spectra of the same ionization mode, as the two modes generally result in very distinct fragmentation spectra. To overcome this barrier, here, we have implemented a machine learning model that can predict chemical similarity between spectra of different ionization modes. Hence, our new MS2DeepScore 2.0 model facilitates the seamless integration of positive and negative ionization mode mass spectra into one analysis pipeline. This creates entirely new options for data exploration, such as mass spectral library searching of negative ion mode spectra in positive ion mode libraries or cross-ionization mode molecular networking. Furthermore, to improve the reliability of predictions and better cope with unseen data, we have implemented a method to estimate the quality of prediction. This will help to avoid false predictions on spectra with low information content or spectra that substantially differ from the training data. We anticipate that the MS2DeepScore 2.0 model will extend our current capabilities in organizing and annotating untargeted metabolomics profiles. KW - Massenspektrometrie KW - Metabolomik KW - Maschinelles Lernen Y1 - 2024 U6 - https://doi.org/10.1101/2024.03.25.586580 PB - Cold Spring Harbor Laboratory ER - TY - JOUR A1 - Gaudry, Arnaud A1 - Huber, Florian A1 - Nothias, Louis-Félix A1 - Cretton, Sylvian A1 - Kaiser, Marcel A1 - Wolfender, Jean-Luc A1 - Allard, Pierre-Marie T1 - MEMO: Mass Spectrometry-Based Sample Vectorization to Explore Chemodiverse Datasets JF - Frontiers in Bioinformatics N2 - In natural products research, chemodiverse extracts coming from multiple organisms are explored for novel bioactive molecules, sometimes over extended periods. Samples are usually analyzed by liquid chromatography coupled with fragmentation mass spectrometry to acquire informative mass spectral ensembles. Such data is then exploited to establish relationships among analytes or samples (e.g., via molecular networking) and annotate metabolites. However, the comparison of samples profiled in different batches is challenging with current metabolomics methods since the experimental variation—changes in chromatographical or mass spectrometric conditions - hinders the direct comparison of the profiled samples. Here we introduce MEMO—MS2 BasEd SaMple VectOrization—a method allowing to cluster large amounts of chemodiverse samples based on their LC-MS/MS profiles in a retention time agnostic manner. This method is particularly suited for heterogeneous and chemodiverse sample sets. MEMO demonstrated similar clustering performance as state-of-the-art metrics considering fragmentation spectra. More importantly, such performance was achieved without the requirement of a prior feature alignment step and in a significantly shorter computational time. MEMO thus allows the comparison of vast ensembles of samples, even when analyzed over long periods of time, and on different chromatographic or mass spectrometry platforms. This new addition to the computational metabolomics toolbox should drastically expand the scope of large-scale comparative analysis. KW - Computational chemistry KW - Massenspektrometrie KW - Metabolomik KW - Naturstoffchemie Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-54660 SN - 2673-7647 VL - 2 PB - Frontiers ER - TY - JOUR A1 - Szwarc, Sarah A1 - Rutz, Adriano A1 - Lee, Kyungha A1 - Mejri, Yassine A1 - Bonnet, Olivier A1 - Hazni, Hazrina A1 - Jagora, Adrien A1 - Mbeng Obame, Rany B. A1 - Noh, Jin Kyoung A1 - Otogo N’Nang, Elvis A1 - Alaribe, Stephenie C. A1 - Awang, Khalijah A1 - Bernadat, Guillaume A1 - Choi, Young Hae A1 - Courdavault, Vincent A1 - Frederich, Michel A1 - Gaslonde, Thomas A1 - Huber, Florian A1 - Kam, Toh-Seok A1 - Low, Yun Yee A1 - Poupon, Erwan A1 - van der Hooft, Justin J. J. A1 - Kang, Kyo Bin A1 - Le Pogam, Pierre A1 - Beniddir, Mehdi A. T1 - Translating community-wide spectral library into actionable chemical knowledge: a proof of concept with monoterpene indole alkaloids JF - Journal of Cheminformatics N2 - With over 3000 representatives, the monoterpene indole alkaloids (MIAs) class is among the most diverse families of plant natural products. The MS/MS spectral space exploration of these complex compounds using chemoinformatic and computational mass spectrometry tools offers a valuable opportunity to extract and share chemical insights from this emblematic family of natural products (NPs). In this work, we first present a substantially updated version of the MIADB, a database now containing 422 MS/MS spectra of MIAs that has been uploaded to the GNPS library versus 172 initial entries. We then introduce an innovative workflow that leverages hundreds of fragmentation spectra to support the FAIRification, extraction and dissemination of chemical knowledge. This workflow aims at the extraction of spectral patterns matching finely defined MIA skeletons. These extracted signatures can then be queried against complex biological extract datasets using MassQL. By applying this strategy to an LC-MS/MS dataset of 75 plant extracts, our results demonstrated the efficiency of this approach in identifying the diversity of MIA skeletons present in the analyzed samples. Additionally, our work enabled the digitization of structural data for diverse MIA skeletons by converting them into machine-readable formats and thereby enhancing their dissemination for the scientific community. Scientific contribution A comprehensive investigation of the monoterpene indole alkaloid chemical space, aiming to highlight skeleton-dependent fragmentation similarity trends and to generate valuable spectrometric signatures that could be used as queries. KW - Computational chemistry KW - Massenspektrometrie KW - Naturstoffchemie Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-54607 SN - 1758-2946 VL - 17 IS - 1 PB - Springer Nature ER - TY - INPR A1 - Huber, Florian A1 - Pollmann, Julian T1 - Count your bits: more subtle similarity measures using larger radius count vectors N2 - Quantifying molecular similarity is a cornerstone of cheminformatics, underpinning applications from virtual screening to chemical space visualization. A wide range of molecular fingerprints and similarity metrics, most notably Tanimoto scores, are employed, but their effectiveness is highly context-dependent. In this study, we systematically evaluate several 2D fingerprint types, including circular, path-based, and distance-encoded variants, using both binary and count representations. We highlight the consequences of fingerprint choice, vector folding, and similarity metric selection, revealing critical issues such as fingerprint duplication, mass dependent score biases, and high bit collision rates. Sparse and count-based fingerprints consistently outperform fixed-size binary vectors in preserving structural distinctions. Furthermore, we introduce percentile-based normalization, propose inverse-document-frequency (IDF) weighting, and benchmark all methods against graph-based MCES similarities. Our results offer practical guidance for selecting molecular similarity measures, emphasizing the need for conscious, task-aware fingerprinting choices in large-scale chemical analyses. KW - Computational chemistry KW - Metabolomik KW - Naturstoffchemie Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-54632 PB - bioRXiv ER - TY - JOUR A1 - Hunke, Til A1 - Huber, Florian A1 - Steffens, Jochen T1 - The Evolution of Song Lyrics: An NLP-Based Analysis of Popular Music in Germany from 1954 to 2022 JF - Music & Science N2 - Music is an indispensable cultural product, reflecting changes in social, psychological, and cultural contexts. This study analyzes the historical evolution of topics and conveyed affect in popular music lyrics in Germany from 1954 to 2022, using LDA-based topic modeling and transformer-based sentiment analysis. These results show that Love & Relationships is the most referenced topic, with Dreams & Longings prominent until the mid-1960s and Society & Status rising from 2017. The sentiment analysis reveals a significant decline in positive sentiment since the mid-1960s, accompanied by increases in negative, ambiguous, and neutral sentiments. These trends may reflect broader societal changes, including shifts in cultural values, rising individualism, and increasing mental health issues. The study highlights the evolving nature of popular music and its reflection of social dynamics. KW - Popmusik KW - Maschinelles Lernen KW - Automatische Sprachanalyse Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-52618 SN - 2059-2043 VL - 8 PB - Sage Publications ER - TY - JOUR A1 - Kok, Maurits A1 - Huber, Florian A1 - Kalisch, Svenja-Marei A1 - Dogterom, Marileen T1 - EB3-informed dynamics of the microtubule stabilizing cap during stalled growth JF - Biophysical Journal N2 - Microtubule stability is known to be governed by a stabilizing GTP/GDP-Pi cap, but the exact relation between growth velocity, GTP hydrolysis, and catastrophes remains unclear. We investigate the dynamics of the stabilizing cap through in vitro reconstitution of microtubule dynamics in contact with microfabricated barriers, using the plus-end binding protein GFP-EB3 as a marker for the nucleotide state of the tip. The interaction of growing microtubules with steric objects is known to slow down microtubule growth and accelerate catastrophes. We show that the lifetime distributions of stalled microtubules, as well as the corresponding lifetime distributions of freely growing microtubules, can be fully described with a simple phenomenological 1D model based on noisy microtubule growth and a single EB3-dependent hydrolysis rate. This same model is furthermore capable of explaining both the previously reported mild catastrophe dependence on microtubule growth rates and the catastrophe statistics during tubulin washout experiments. KW - Mikrotubulus Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-53164 SN - 1542-0086 VL - 124 IS - 2 SP - 227 EP - 244 PB - Elsevier ER - TY - INPR A1 - Bushuiev, Roman A1 - Bushuiev, Anton A1 - de Jonge, Niek F. A1 - Young, Adamo A1 - Kretschmer, Fleming A1 - Samusevich, Raman A1 - Heirman, Janne A1 - Wang, Fei A1 - Zhang, Luke A1 - Dührkop, Kai A1 - Ludwig, Marcus A1 - Haupt, Nils A. A1 - Kalia, Apurva A1 - Brungs, Corinna A1 - Schmid, Robin A1 - Greiner, Russell A1 - Wang, Bo A1 - Wishart, David S. A1 - Liu, Li-Ping A1 - Rousu, Juho A1 - Bittremieux, Wout A1 - Röst, Hannes A1 - Mak, Tytus D. A1 - Hassoun, Soha A1 - Huber, Florian A1 - van der Hooft, Justin J.J. A1 - Stravs, Michael A. A1 - Böcker, Sebastian A1 - Sivic, Josef A1 - Pluskal, Tomáš T1 - MassSpecGym: A benchmark for the discovery and identification of molecules T2 - arXiv N2 - The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at this https URL [https://github.com/pluskal-lab/MassSpecGym]. KW - Maschinelles Lernen KW - Benchmark KW - Computational chemistry KW - Massenspektrometrie KW - Molekülstruktur Y1 - 2025 U6 - https://doi.org/10.48550/arXiv.2410.23326 N1 - Open Review: https://web.archive.org/web/20250521010648/https://openreview.net/forum?id=AAo8zAShX3#discussion PB - arXiv ET - v3 ER - TY - CHAP A1 - Steffens, Jochen A1 - Joschko, Marcel A1 - Giang, Hien A1 - Huber, Florian T1 - Using machine learning to identify acoustic fingerprints of concert halls in classical audio recordings [Abstract] T2 - DAS|DAGA 2025: 51st Annual Meeting on Acoustics, March 17-20, 2025, Copenhagen KW - Datenanalyse KW - Maschinelles Lernen KW - Raumakustik KW - Konzertsaal Y1 - 2025 UR - https://pub.dega-akustik.de/DAS-DAGA_2025/konferenz-1559.html?article=348 PB - Deutsche Gesellschaft für Akustik e.V. CY - Berlin ER - TY - JOUR A1 - de Jonge, Niek F. A1 - Hecht, Helge A1 - Strobel, Michael A1 - Wang, Mingxun A1 - van der Hooft, Justin J. J. A1 - Huber, Florian T1 - Reproducible MS/MS library cleaning pipeline in matchms JF - Journal of Cheminformatics N2 - Mass spectral libraries have proven to be essential for mass spectrum annotation, both for library matching and training new machine learning algorithms. A key step in training machine learning models is the availability of high-quality training data. Public libraries of mass spectrometry data that are open to user submission often suffer from limited metadata curation and harmonization. The resulting variability in data quality makes training of machine learning models challenging. Here we present a library cleaning pipeline designed for cleaning tandem mass spectrometry library data. The pipeline is designed with ease of use, flexibility, and reproducibility as leading principles.Scientific contributionThis pipeline will result in cleaner public mass spectral libraries that will improve library searching and the quality of machine-learning training datasets in mass spectrometry. This pipeline builds on previous work by adding new functionality for curating and correcting annotated libraries, by validating structure annotations. Due to the high quality of our software, the reproducibility, and improved logging, we think our new pipeline has the potential to become the standard in the field for cleaning tandem mass spectrometry libraries. KW - Massenspektrometrie KW - Metabolomik KW - Metadaten KW - Python (Programmiersprache) KW - Library cleaning KW - HSD Publikationsfonds KW - DFG Publikationskosten Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-46491 SN - 1758-2946 VL - 16 IS - 1 PB - Springer Nature ER -