Biology subjects

Altenburg, T.

Publications and source records attributed to Altenburg, T..

6 recordsLinked to original sources

bioRxiv2026

usiGrabber: Automating the curation of proteomics spectra data at scale, making large datasets ready for use in machine learning systems

MotivationAn unprecedented amount of mass spectrometry-based proteomics data is publicly available through repositories such as the PRoteomics IDEntifications Database (PRIDE), and the field is increasingly leveraging machine learning approaches. However, the available data is not ready to be reused in a scalable way beyond the original acquisition purpose. Existing machine learning models commonly rely on a few manually curated datasets that require deep domain expertise and tedious technical work to construct. Importantly, these datasets have not been updated in recent years, so that newly published data remains inaccessible. We present usiGrabber, a scalable framework for assembling large proteomic datasets. usiGrabber is designed around portability and extensibility. It extracts spectra identification data from mzIdentML files, stores additional project-level metadata retrieved through the PRIDE API, indexes raw spectra using Universal Spectrum Identifiers (USIs), and offers download utilities to retrieve spectra data at scale. ResultsWithin 49 hours, we parsed over 800 million peptide spectrum matches and corresponding USIs from over 1,200 projects. As a proof of concept, we used usiGrabber to construct a phosphorylation-specific training dataset of nearly 11 million spectra in under two days and used it to retrain a binary phosphorylation classifier based on the AHLF model architecture. With a balanced accuracy of 0.78, our model achieves comparable performance to the original model on an independent test set, showing that automated data extraction is an alternative to manual curation of static datasets. AvailabilityAll code is available at https://github.com/usiGrabber/usiGrabber; the data is available at https://zenodo.org/records/18853258.

bioinformatics↗

bioRxiv2026

Benchmarking Next-Generation Sequencing Platforms: A Comprehensive Comparison Of Single-Cell RNA-Seq from Ultima UG 100 vs. Illumina NovaSeq X Plus

Single-cell RNA sequencing (scRNA-seq) has become an essential technology for dissecting cellular heterogeneity in complex biological systems, with Illumina platforms serving as the dominant NGS provider1-3. The recent emergence of the Ultima Genomics UG 100 platform offers a high-throughput and potentially more cost-effective alternative, warranting a rigorous comparative analysis. Here, we conducted a comprehensive head-to-head comparison of the Ultima Genomics UG 100 and Illumina NovaSeq X Plus platforms, generating deep-coverage scRNA-seq data from human PBMCs and stimulated T-cell samples. Our multi-level analysis revealed highly concordant performance in both cell type identification and gene expression. We found that both platforms robustly captured all major immune lineages and demonstrated good transcriptional agreement. Minor observed discrepancies were primarily correlated with low UMI counts, suggesting stochastic biological effects rather than technical variance. Overall, our study establishes that the Ultima UG 100 delivers performance highly comparable to the Illumina NovaSeq X Plus for single-cell transcriptomics.

bioinformatics↗

bioRxiv2021

yHydra: Deep Learning enables an Ultra Fast Open Search by Jointly Embedding MS/MS Spectra and Peptides of Mass Spectrometry-based Proteomics

Mass spectrometry-based proteomics allows to study all proteins of a sample on a molecular level. However, mass spectra are noisy and contain complex patterns, making them inherently challenging to analyze with purely algorithmic approaches. In terms of the protein sequence landscape, most recent bottom-up MS-based proteomics studies consider either a diverse pool of post-translational modifications, employ large databases - as in metaproteomics or proteogenomics, study multiple isoforms of proteins, include unspecific cleavage sites or even combinations thereof. All this makes peptide and protein identifications challenging due to sheer size of the search space. To cope with this two-sided challenge, i.e. the complexity of real spectra and the search space size, we present a foundation model, called yHydra, that jointly embeds spectra and peptides. This allows us to implement various downstream tasks and search modes in Euclidean space. In particular, we implement an open search which allows to query multiple ten-thousands of spectra against millions of peptides. Furthermore, we implement an error-tolerant search for identifying additional proteoforms that are not included in off-the-shelf reference proteomes. Our foundation model provides meaningful embeddings, as we interpret learned peptide embeddings in comparison to the peptides physico-chemical properties. yHydras open search, assigns delta masses to each identification which allows to unrestrictedly characterize post-translational modifications. The error-tolerant mode of yHydra can be used as post-processing to existing search engines or as a standalone. yHydra is evaluated on several real life data sets for the identification of modified protein sequences and shows up to 25% increase in protein identification at constant false discovery rate compared to the current state-of-the-art. Availability(under MIT license) https://gitlab.com/dacs-hpi/yHydra Contactbernhard.renard@hpi.de

bioinformatics↗

bioRxiv2020

AHLF: ad hoc learning of fragmentation patterns in mass spectra detects post-translationally modified peptides prior to database search

Mass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of a living cell on a molecular level. Currently, only a few deep learning approaches that involve peptide fragmentation spectra, which represent partial sequence information of proteins, exist. Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge. To elevate unrestricted learning from spectra, we introduce AHLF, a deep learning model that is end-to-end trained on 19.2 million spectra from multiple phosphoproteomic data sets. AHLF is interpretable and we show that peak-level feature importances 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 to the current-state-of-the-art on this task. 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%. We expect our approach to directly apply to cell signaling and structural biology which use phosphoproteomic and cross-linking data, but in principal any mass spectrometry based study can benefit from an interpretable, end-to-end trained model like AHLF. Availabilityhttps://gitlab.com/dacs-hpi/ahlf Contactbernhard.renard@hpi.de

bioinformatics↗

bioRxiv2019

Probability-based detection of phosphoproteomic uncertainty reveals rare signaling events driven by oncogenic kinase gene fusion

We describe a novel Bayesian method for estimating protein concentration and phosphorylation site occupancy ratios from mass spectrometry experiments. Our variance model assigns standard deviations to all quantitative ratios, even when only a single peptide is observed, increasing the number of quantifiable observations in a sample compared to conventional methods. We further demonstrate the application of this method using a dataset investigating the impact of the PRKAR1A-RET gene fusion in immortalized thyroid cells.

systems biology↗

bioRxiv2019

Osmolyte homeostasis controls single-cell growth rate and maximum cell size of Saccharomyces cerevisiae

Cell growth is well described at the population level, but precisely how nutrient and water uptake and cell wall expansion drive the growth of single cells is poorly understood. Supported by measurements of single-cell growth trajectories and cell wall elasticity, we present a single-cell growth model for yeast. The model links the thermodynamic quantities turgor pressure, osmolarity, cell wall elasto-plasticity, and cell size, using concepts from rheology and thin shell theory. It reproduces cell size dynamics during single-cell growth, budding, and hyper- or hypoosmotic stress. We find that single-cell growth rate and final size are primarily governed by osmolyte uptake and consumption, while bud expansion depends additionally on different cell wall extensibilities of mother and bud. Based on first principles the model provides a more accurate description of size dynamics than previous attempts and its analytical simplification allows for easy combination with models for other cell processes.

systems biology↗