TY - JOUR A1 - Altenburg, T. A1 - Giese, S. A1 - Wang, S. A1 - Muth, Thilo A1 - Renard, B.Y. T1 - Ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides JF - Nature Machine Intelligence N2 - Mass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of living cells on a molecular level. Currently, only a few deep learning approaches exist that involve peptide fragmentation spectra, which represent partial sequence information of proteins. Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge. Here, to elevate unrestricted learning from spectra, we introduce ‘ad hoc learning of fragmentation’ (AHLF), a deep learning model that is end-to-end trained on 19.2 million spectra from several phosphoproteomic datasets. AHLF is interpretable, and we show that peak-level feature importance values and pairwise interactions between peaks are in line with corresponding peptide fragments. We demonstrate our approach by detecting post-translational modifications, specifically protein phosphorylation based on only the fragmentation spectrum without a database search. AHLF increases the area under the receiver operating characteristic curve (AUC) by an average of 9.4% on recent phosphoproteomic data compared with the current state of the art on this task. Furthermore, use of AHLF in rescoring search results increases the number of phosphopeptide identifications by a margin of up to 15.1% at a constant false discovery rate. To show the broad applicability of AHLF, we use transfer learning to also detect cross-linked peptides, as used in protein structure analysis, with an AUC of up to 94%. KW - Mass spectrometry KW - Machine learning KW - Deep learning KW - Peptide identification PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-547580 DO - https://doi.org/10.1038/s42256-022-00467-7 SN - 2522-5839 VL - 4 SP - 378 EP - 388 PB - Springer Nature CY - London AN - OPUS4-54758 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Muth, Thilo T1 - QI-CLOUD Perspektiven zur Entwicklung einer digitalen Qualitätsinfrastruktur N2 - In diesem Vortrag wird die Perspektive einer digitalen Qualitätsinfrastruktur (QI) auf informatischer Seite vorgestellt. Eine zu entwickelnde QI-Cloud ist die Grundlage einer verteilten IT-Plattform über die digitalisierte Prozesse der QI abgewickelt, Daten sicher vorgehalten und ausgetauscht sowie digitale Zertifikate ausgestellt werden können. Dazu werden Methoden wie die Distributed Ledger Technologie sowie Smart Standards beschrieben, die das Potential haben, essentielle technologische Bestandteile einer digital transformierten QI zu werden. T2 - VMPA Tagung CY - Nuremberg, Germany DA - 31.05.2022 KW - Digital quality infrastructure KW - QI-Digital KW - QI-Cloud KW - smart standards PY - 2022 AN - OPUS4-56680 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kister, Alexander A1 - Wrobel, S. A1 - Wirtz, T. A1 - Paul, N. T1 - Multi-Agent Neural Rewriter for Vehicle Routing with Limited Disclosure of Costs N2 - We interpret solving the multi-vehicle routing problem as a team Markov game with partially observable costs. For a given set of customers to serve, the playing agents (vehicles) have the common goal to determine the team-optimal agent routes with minimal total cost. Each agent thereby observes only its own cost. Our multi-agent reinforcement learning approach, the so-called multi-agent Neural Rewriter, builds on the single-agent Neural Rewriter to solve the problem by iteratively rewriting solutions. Parallel agent action execution and partial observability require new rewriting rules for the game. We propose the introduction of a so-called pool in the system which serves as a collection point for unvisited nodes. It enables agents to act simultaneously and exchange nodes in a conflict-free manner. We realize limited disclosure of agent-specific costs by only sharing them during learning. During inference, each agents acts decentrally, solely based on its own cost. First empirical results on small problem sizes demonstrate that we reach a performance close to the employed OR-Tools benchmark which operates in the perfect cost information setting. T2 - Gamification and Multiagent Solutions Workshop (ICLR 2022) CY - Online meeting DA - 29.04.2022 KW - Reinforcement learning KW - Deep Learning KW - Vehicle Routing PY - 2022 AN - OPUS4-56687 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Holstein, Tanja A1 - Verschaffelt, P. A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - Probability based taxonomic profiling of viral and microbiome samples using PepGM and Unipept N2 - In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates taxonomic inference in samples of unknown taxonomic origin. PepGM uses a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets providing taxonomic confidence scores. To build the graphical model, a list of potentially present taxa needs to be inferred. To this end, we integrate Unipept, which enables the fast querying of potentially present taxa. Together, they allow for taxonomic inference with statistically sound confidence scores. T2 - HUPO 2022 world congress CY - Cancun, Mexico DA - 04.12.2022 KW - Bioinformatics KW - Mass spectrometry KW - Taxonomic analysis KW - Microbiomes PY - 2022 AN - OPUS4-56748 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Holstein, Tanja A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - PepGM: A graphical model for taxonomic profiling of viral proteomes N2 - In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates taxonomic inference. inference. We introduce PepGM, a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets. Using the graphical model, our approach computes statistically sound scores for taxa based on peptide scores from a previous database search, eliminating the need for commonly used heuristics. heuristics. T2 - International conference on systems biology 2022 CY - Berlin, Germany DA - 07.10.2022 KW - Bioinformatics KW - Virus protoemics KW - Taxonomic analysis KW - Graphical models PY - 2022 AN - OPUS4-56749 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Holstein, Tanja A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - PepGM: A graphical model for taxonomic profiling of viral proteomes N2 - In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates taxonomic inference. inference. We introduce PepGM, a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets. Using the graphical model, our approach computes statistically sound scores for taxa based on peptide scores from a previous database search, eliminating the need for commonly used heuristics. heuristics. T2 - European proteomics association conference 2022 CY - Leipzig, Germany DA - 03.04.2022 KW - graphical models KW - Taxonomic inference KW - Bioinformatics KW - Virus protoemics PY - 2022 AN - OPUS4-56750 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Holstein, Tanja A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - PepGM: A graphical model for taxonomic profiling of viral proteomes N2 - In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates taxonomic inference. inference. We introduce PepGM, a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets. Using the graphical model, our approach computes statistically sound scores for taxa based on peptide scores from a previous database search, eliminating the need for commonly used heuristics. T2 - European bioinformatics community winter school 2022 CY - Lisbon, Portugal DA - 21.03.2022 KW - Bioinformatics KW - Virus protoemics KW - Taxonomic analysis KW - Graphical models PY - 2022 AN - OPUS4-56751 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Holstein, Tanja A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - PepGM: A graphical model for taxonomic profiling of viral proteomes N2 - In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates taxonomic inference. inference. We introduce PepGM, a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets. Using the graphical model, our approach computes statistically sound scores for taxa based on peptide scores from a previous database search, eliminating the need for commonly used heuristics. T2 - European bioinformatics community winter school 2022 CY - Lisbon, Portugal DA - 21.03.2022 KW - Bioinformatics KW - Virus protoemics KW - Taxonomic analysis KW - Graphical models PY - 2022 AN - OPUS4-56753 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Nowatzky, Yannek A1 - Benner, Philipp A1 - Reinert, K. A1 - Muth, Thilo T1 - Mistle: Metaproteomic index and spectral library search engine N2 - Introduction: With the introduction of accurate deep learning predictors, spectral matching applications might experience a renaissance in tandem mass spectrometry (MS/MS) driven proteomics. Deep learning models, e.g., Prosit, predict complete MS/MS spectra from peptide sequences and give the unprecedented ability to accurately predict mass spectra that may arise from any given proteome. However, the amount of spectral data is enormous when querying large search spaces, e.g., metaproteomes composed of many different species. Current spectral library search software, such as SpectraST, is not equipped to meet run time and memory constraints imposed by such large MS/MS databases, covering several millions of peptide spectrum predictions. Methods: Inspired by the fragment index data structure that had been introduced with MSFragger, we implement an efficient peak matching algorithm for computing spectral similarity between query and library spectra. Mistle (Metaproteomic index and spectral library search engine) uses index partitioning and SIMD (Single instruction, multiple data) intrinsics, which greatly improves speed and memory efficiency for searching large spectral libraries. Mistle is written in C++20 and highly parallelized. Results: We demonstrate the efficiency of Mistle on two predicted spectral libraries for the lab-assembled microbial communities 9MM and SIHUMIx. Compared to the spectral library search engine SpectraST, Mistle shows a >10-fold runtime improvement and is also faster than msSLASH, which uses locality-sensitive hashing. Although Mistle is slower than MSFragger, Mistle‘s memory footprint is an order of magnitude smaller. Furthermore, we find evidence that the spectral matching approach to predicted libraries identifies peptides with higher precision. Mistle detects peptides not found by database search via MSFragger and in turn uncovers unnoticed false discoveries among their matches. Conclusion: In this study, we show that predicted spectral libraries can enhance peptide identification for metaproteomics. Mistle provides the means to efficiently search large-scale spectral libraries, highlighted for the microbiota 9MM and SIHUMIx. T2 - HUPO2022 CY - Cancun, Mexico DA - 04.12.2022 KW - Proteomics KW - Mass spectrometry KW - Algorithms KW - Metaproteomics PY - 2022 AN - OPUS4-56695 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Abad Andrade, Carlos Enrique A1 - You, Zengchao A1 - Richter, Silke A1 - Benner, Philipp A1 - Recknagel, Sebastian T1 - The use of reference materials to improve the calibration strategy in glow discharge optical emission spectroscopy with machine learning N2 - Glow discharge optical emission spectroscopy (GD-OES) is a technique for the analysis of solids such as metals, semiconductors, and ceramics. A low-pressure glow discharge plasma is applied in this system, which ‘sputters’ and promotes the sample atoms to a higher energy state. When the atoms return to their ground state, they emit light with characteristic wavelengths, which a spectrometer can detect. Thus, GD-OES combines the advantages of ICP-OES with solid sampling techniques, which enables it to determine the bulk elemental composition and depth profiles. However, direct solid sampling methods such as glow-discharge spectroscopy require reference materials for calibration due to the strong matrix effect. Reference materials are essential when the accuracy and reliability of measurement results need to be guaranteed to generate confidence in the analysis. These materials are frequently used to determine measurement uncertainty, validate methods, suitability testing, and quality assurance. In addition, they guarantee that measurement results can be compared to recognized reference values. Unfortunately, the availability of certified reference materials suited to calibrate all elements in different matrix materials is limited. Therefore various calibration strategies and the preparation of traceable matrix-matched calibration standards will be discussed. Machine learning is an essential component of the growing field of data science. Through statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. Therefore, it was tried in our work to combine GD-OES with machine learning strategies to establish a new and robust calibration model, which can be used to identify the elemental composition and concentration of metals from a single spectrum. For this purpose, copper reference materials from different manufacturers, which contain various impurity elements, were investigated using GD-OES. The obtained spectra information are evaluated with different algorithms (e.g., gradient boosting and artificial neural networks), and the results are compared and discussed in detail. T2 - Winter Conference on Plasma Chemistry 2022 CY - Tucson, AZ, USA DA - 17.01.2022 KW - GDOES KW - Machine learning KW - Reference materials KW - Calibration KW - Cooper PY - 2022 AN - OPUS4-56497 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -