About me
Nicolò Alagna
Postdoctoral researcher
nalagna@uni-mainz.de
Anselm-Franz-von-Bentzel-Weg 3, 55128 Mainz
Current project
My work focus on applying machine learning, deep learning and mathematical methods in biophysics, biochemistry and biology for quantitative analysis and modelling of systems that requires bid data analysis. In detail, I work with time series and meta data to extract fundamental system proprieties using advance and self designed algorithms for the analysis of the data.
I am currently working in two main projects: 1) Automatic Extraction of kinetic proprieties and polymer systems key features 2) Decoding of nanopore RNA signal for the detection of RNA modifications.
In addition, I have a side project on the identification and analysis of RNA methylation patterns in ageing for tissue age prediction.
The first main project focused on the analysis of time resolved signal using deep learning and mathematical methods for the automatized analysis of time resolved data and extraction of physical proprieties of the system (rate constants, kinetic model, etc). The aim of this project is to have deep learning support to understand complex kinetics and to understand both how to what control the system evolution.
In the second project, I am working with machine learning / deep learning techniques, but in this case applied to the analysis of the RNA signal obtained from Oxford Nanopore technologies. In this case, my goal is to find a method that can analyze the RNA signal with high precision for the identification of RNA modifications, which are linked to several diseases including cancer formation.
Publications
- Nicolò Alagna, Stefan Mündnich, Johannes Miedema, Stefan Pastore, Lioba Lehmann, Anna Wierczeiko, Johannes Friedrich, Lukas Walz, Marko Jörg, Tamer Butto, Kristina Friedland, Mark Helm, Susanne Gerber:ModiDeC: a multi-RNA modification classifier for direct nanopore sequencing bioRxiv 2025.01.04.631307; doi: https://doi.org/10.1101/2025.01.04.631307 Accepted for publication in Nucleic Acids Research (2025)
- Nicolò Alagna, Brigitta Dúzs, Heinz Köppl, Andreas Walther and Susanne Gerber: Deep Learning Reaction Network: a machine learning framework for modeling time resolved data. Communications Chemistry, 8 Article 153, (2025)
- Jannes Spangenberg, Stefan Mündnich, Anne Busch, Stefan Pastore, Anna Wierczeiko, Winfried Goettsch, Vincent Dietrich, Leszek Pryszcz, Sonia Cruciani, Eva Maria Novoa, Kandarp Joshi, Ranjan Perera, Salvatore Di Giorgio, Paola Arrubarrena, Irem Tellioglu, Chi-Lam Poon, Yuk Wan, Jonathan Göke, Andreas Hildebrand*, Christoph Dieterich*, Mark Helm*, Manja Marz*, Susanne Gerber* and Nicolò Alagna*: Predicting RNA modifications by nanopore sequencing: The RMaP challenge. Communications Chemistry 8, 115 (2025)
- Vincent Dietrich, Nicolò Alagna, Mark Helm, Susanne Gerber* & Tamer Butto* : Pod5Viewer: a GUI for inspecting raw nanopore sequencing data. Bioinformatics, btae665 (2024)
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Hainer F, Alagna N, Reddy Marri A, Penfold TJ, Gros PC, Haacke S, Buckup T. Vibrational Coherence Spectroscopy Identifies Ultrafast Branching in an Iron(II) Sensitizer. J Phys Chem Lett. 2021 Sep 9;12(35):8560-8565. Epub 2021 Sep 1. PMID: 34468159.
- Alagna, Nicolò; Lustres, Jose Luis Pérez; Roozbeh, Ashkan; Han, Jie; Hahn, Sebastian; Berger, Felix J.; et al. Ultrafast Singlet Fission in Rigid Azaarene Dimers with Negligible Orbital Overlap. J. Phys. Chem. B, 2020, 124, 41, 9163–9174.
- Nicolò Alagna, J. Luis Pérez Lustres, Nikolaus Wollscheid, Qingqing Luo, Jie Han, Andreas Dreuw, Florian L. Geyer, Victor Brosius, Uwe H. F. Bunz, Tiago Buckup, and Marcus Motzkus. Singlet Fission in Tetraaza-TIPS-Pentacene Oligomers: From fs Excitation to μs Triplet Decay via the Biexcitonic State. The Journal of Physical Chemistry B (2019) 123 (50), 10780-10793
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Nicolò, Alagna., Jie, Han., Nikolaus, Wollscheid., J., Luis, Pérez, Lustres., Julia, Herz., Sebastian, Hahn., Silke, Koser., Fabian, Paulus., Uwe, H., F., Bunz., Andreas, Dreuw., Tiago, Buckup., Marcus, Motzkus. Tailoring Ultrafast Singlet Fission by the Chemical Modification of Phenazinothiadiazoles.. Journal of the American Chemical Society, 2019, 141(22):8834-8845.