Biology subjects

Panse, C.

Publications and source records attributed to Panse, C..

12 recordsLinked to original sources

bioRxiv2026

qg: Configuration-Driven, Multi-Vendor Acquisition Queue Generation with Reproducible Run-Order and QC Control for Mass Spectrometry

Mass spectrometry laboratories must turn lists of submitted samples into acquisition queues. The run order and the placement of quality-control (QC) injections determine whether a design controls batch effects and signal drift, and whether those effects stay correctable afterward. Yet operators usually set them by hand in vendor worklist editors that neither randomize run order nor offer configurable, pattern-driven QC. We present qg, an open-source tool that builds acquisition queues with systematic run-order handling: four run-order modes (none, simple, blocked/randomized-complete-block, and group-uniform blocked), pattern-driven QC and standard injections, and sampler- and plate-aware positioning. Unlike plate-design tools that stop at a generic sample sheet, qg writes the native vendor acquisition file directly, for three instrument ecosystems (Thermo Fisher XCalibur, Axel Semrau Chronos, Bruker HyStar) across proteomics, metabolomics, and lipidomics. It separates a small, stateless generation pipeline from a declarative configuration layer, so a laboratory adapts instruments, QC patterns, layouts, and naming by editing version-controlled configuration through a validating editor rather than changing code. qg runs from a reactive web interface or a scripted command-line interface, integrated with a LIMS (B-Fabric) or standalone from uploaded tables; randomized runs record their seed and reproduce from exported parameters. On an unbalanced design, group-uniform blocked randomization spreads biological groups evenly across acquisition time, whereas textbook block randomization leaves a tail of the largest group and can track acquisition time worse than a plain shuffle. qg is released under the Apache-2.0 license.

bioinformatics↗

bioRxiv2026

LFQ Benchmark Dataset - Generation Beta: Assessing Modern Proteomics Instruments and Acquisition Workflows with High-Throughput LC Gradients

Recent advances in liquid chromatography-mass spectrometry (LC-MS) have accelerated the adoption of high-throughput workflows that deliver deep proteome coverage using minimal sample amounts. This trend is largely driven by clinical and single-cell proteomics, where sensitivity and reproducibility are essential. Here, we extend our previous benchmark dataset (PXD028735) using next-generation LC-MS platforms optimized for rapid proteome analysis. We generated an extensive DDA/DIA dataset using a human-yeast-E. coli hybrid proteome. The proteome sample was distributed across multiple laboratories together with standardized analytical protocols specifying two short LC gradients (5 and 15 min) and low sample input amounts. This dataset includes data acquired on four different platforms, and features new scanning quadrupole-based implementations, extending coverage across different instruments and acquisition strategies. Our comprehensive evaluation highlights how technological advances and reduced LC gradients may affect proteome depth, quantitative precision, and cross-instrument consistency. The release of this benchmark dataset via ProteomeXchange (PXD070049 and PXD071205), allows for the acceleration of cross-platform algorithm development, enhance data mining strategies, and supports standardization of short-gradient, high-throughput LC-MS-based proteomics.

bioinformatics↗

bioRxiv2025

ProteoBench: the community-curated platform for comparing proteomics data analysis workflows

Mass spectrometry (MS)-based proteomics is a well-established strategy for analyzing complex biological mixtures. Many MS instruments and data acquisition strategies are available, and the data they acquire differ substantially, thus requiring tailored analysis algorithms. Hence, many dedicated bioinformatics workflows are developed. These are in constant evolution, and the community lacks a centralized platform for comparing their performance. Here, we propose ProteoBench, a single platform that brings together software developers and software users to provide an ever-evolving comparison of state-of-the-art proteomics data processing tools. ProteoBench is an open-source resource that enables the community to evaluate data analysis workflows, develop benchmarking modules dedicated to specific comparisons, and discuss the best methods to compare software tools. The platform ensures that the benchmark evolves alongside advances in proteomics data analysis workflows. ProteoBench guides researchers towards the best-suited tool and parameters for their specific project and data according to their needs, and developers can test their newly developed tools or workflows privately, before adding them as public references. This community-driven effort will increase transparency and reproducibility between MS data analysis workflows, as well as facilitate the development and publication of software workflows in the field.

bioinformatics↗

bioRxiv2025

A combination of phenotypic responses and genetic adaptations enables Staphylococcus aureus to withstand inhibitory molecules secreted by Pseudomonas aeruginosa

Staphylococcus aureus and Pseudomonas aeruginosa frequently co-occur in infections, and there is evidence that their interactions can negatively affect disease outcomes. P. aeruginosa is known to be dominant, often compromising S. aureus through the secretion of inhibitory compounds. We previously demonstrated that S. aureus can become resistant to growth-inhibitory compounds during experimental evolution. While resistance arose rapidly, the underlying mechanisms were not obvious as only a few genetic mutations were associated with resistance, while ample phenotypic changes occurred. We thus hypothesize that resistance may result from a combination of phenotypic responses and genetic adaptation. Here, we tested this hypothesis using proteomics. We first focused on an evolved strain that acquired a single mutation in tcyA (encoding a transmembrane transporter unit) upon exposure to P. aeruginosa supernatant. We show that this mutation leads to a complete abolishment of transporter synthesis, which confers moderate protection against PQS and selenocystine, two toxic compounds produced by P. aeruginosa. However, this genetic effect was minor compared to the fundamental phenotypic changes observed at the proteome level when both ancestral and evolved S. aureus strains were exposed to P. aeruginosa supernatant. Major changes involved the downregulation of virulence factors, metabolic pathways, membrane transporters, and the upregulation of ROS scavengers and an efflux pump. Our results suggest that the observed multi-variate phenotypic response is a powerful adaptive strategy, offering instant protection against competitors in fluctuating environments and reducing the need for hard-wired genetic adaptations. ImportanceDifferent bacterial pathogens can co-occur in infections, where they interact with one another and influence disease severity. Previous research showed that pathogens can evolve and adapt to co-infecting species. Here, we show that evolution through genetic mutations and selection are not necessarily required to change pathogen behavior. Instead, we found that the human pathogen Staphylococcus aureus is able to plastically respond to the presence of Pseudomonas aeruginosa, a competing pathogen. Through proteomics and metabolomics, we demonstrate that S. aureus undergoes substantial proteomic alterations in response to P. aeruginosa by down-regulating virulence factor expression, changing metabolism, and mounting protective measures against toxic compounds. Our work highlights that pathogens possess sophisticated mechanisms to respond to competitors to secure growth and survival in polymicrobial infections. We predict such plastic responses to have significant impacts on infection outcomes.

microbiology↗

bioRxiv2024

prolfquapp - A User-Friendly Command-Line Tool Simplifying Differential Expression Analysis in Quantitative Proteomics

AbstractMass spectrometry is a cornerstone of quantitative proteomics, enabling relative protein quantification and differential expression analysis (DEA) of proteins. As experiments grow in complexity, involving more samples, groups, and identified proteins, traditional interactive data analysis methods become impractical. The prolfquapp addresses this challenge by providing a command-line interface that simplifies DEA, making it accessible to non-programmers and seamlessly integrating it into workflow management systems. Prolfquapp streamlines data processing and result visualization by generating dynamic HTML reports that facilitate the exploration of differential expression results. These reports allow for investigating complex experiments, such as those involving repeated measurements and multiple explanatory variables. Additionally, prolfquapp supports various output formats, including XLSX files, SummarizedExperiment objects and rank files, for further interactive analysis using spreadsheet software, the exploreDE Shiny application, or gene set enrichment analysis software. By leveraging advanced statistical models from the prolfqua R package, prolfquapp offers a user-friendly, integrated solution for large-scale quantitative proteomics studies, combining efficient data processing with insightful, publication-ready outputs. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/617391v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@10930a2org.highwire.dtl.DTLVardef@57f5dborg.highwire.dtl.DTLVardef@ce0d73org.highwire.dtl.DTLVardef@1d531da_HPS_FORMAT_FIGEXP M_FIG C_FIG This visual table of contents illustrates the workflow and key features of the prolfquapp tool for differential expression analysis in proteomics. On the left are the inputs, like the CSV for annotation and quantification results, YAML for parameters, and FASTA files for protein information. In the center are the prolfquapp and prolfqua R packages and supporting tools like crosstalk and knitr, representing the core processing components. On the right side, the figure highlights the various outputs generated by prolfquapp O_LIXLSX files containing protein abundances, group summaries, and differential expression results. C_LIO_LIHTML reports with text, graphs, interactive volcano plots, and dynamic tables for data exploration. C_LIO_LIPDF documents with detailed protein boxplots and peptide-level matrix plots. C_LIO_LIIntegration with exploreDE for interactive data visualization. C_LI This diagram concisely summarizes the flow from data input to the creation of analysis-ready outputs, offering a clear overview of the prolfquapp toolset.

bioinformatics↗

bioRxiv2024

Koina: Democratizing machine learning for proteomics research

Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and peptide properties are frequently published, the rate at which these models are adopted by the community is slow, which is mostly due to technical challenges. We believe that, for the community to make better use of state-of-the-art models, more attention should be spent on making models easy to use and accessible by the community. To facilitate this, we developed Koina, an open-source containerized, decentralized and online-accessible high-performance prediction service that enables ML/DL model usage in any pipeline. Using the widely used FragPipe computational platform as example, we show how Koina can be easily integrated with existing proteomics software tools and how these integrations improve data analysis.

bioinformatics↗

bioRxiv2024

A multi-year longitudinal harmonization of quality controls in mass spectrometry proteomics core facilities

Quality control procedures play a pivotal role in ensuring the reliability and consistency of data generated in mass spectrometry-based proteomics laboratories. However, the lack of standardized quality control practices across laboratories poses challenges for data comparability and reproducibility. In response, we conducted a harmonization study within proteomics laboratories of the Core for Life alliance with the aim to establish a common quality control framework, which facilitates comprehensive quality assessment and identification of potential sources of performance drift. Through collaborative efforts, we developed a consensus quality control standard for longitudinal assessment and we adopted a common processing software. We generated a 4-year longitudinal dataset from multiple instruments and laboratories, which enabled us to assess intra- and inter-laboratory variability, to identify causes of performance drift, and to establish community reference values for several quality control parameters. Our study enhances data comparability and reliability and fosters a culture of collaboration and continuous improvement within the proteomics community to ensure the integrity of proteomics data.

biochemistry↗

bioRxiv2022

prolfqua: A Comprehensive R-package for Proteomics Differential Expression Analysis

Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. Nevertheless, there is a need for a flexible and easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures. The prolfqua package can model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. It integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The application programmer interface strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis easy and exciting. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard dataset. Finally, we show that the implemented methods allow sensitive and specific differential expression analysis. The prolfqua R package is available on GitHub https://github.com/fgcz/prolfqua, distributed under the MIT licence, and runs on all platforms supported by the R free software environment for statistical computing and graphics.

bioinformatics↗

bioRxiv2020

rawR - Direct access to raw mass spectrometry data in R

The Bioconductor project has shown that the R statistical environment is a highly valuable tool for genomics data analysis1, but with respect to proteomics we are still missing low level infrastructure to enable performant and robust analysis workflows in R. Fundamentally important are libraries that provide raw data access. Our R package rawDiag has provided the proof-of-principle how access to mass spectromerty raw files can be realized by wrapping vendor-provided APIs, but rather focused on meta data analysis and visualization2. Our novel package rawR now provides complete, OS independent access to all spectral data logged in Thermo Fisher Scientific raw files. In this technical note we present implementation details and describe the main functionality provided by the rawR package. In addition, we report two use cases inspired by real-word research task that demonstrate the application of the package. Availabilityhttps://github.com/fgcz/rawR

bioinformatics↗

bioRxiv2020

Universal Spectrum Explorer: A standalone (web-)application for cross-resource spectrum comparison

Here we present the Universal Spectrum Explorer (USE), a web-based tool based on IPSA for cross-resource (peptide) spectrum visualization and comparison (https://www.proteomicsdb.org/use/). Mass spectra under investigation can either be provided manually by the user (table format), or automatically retrieved from online repositories supporting access to spectral data via the universal spectrum identifier (USI), or requested from other resources and services implementing a newly designed REST interface. As a proof of principle, we implemented such an interface in ProteomicsDB thereby allowing the retrieval of spectra acquired within the ProteomeTools project or real-time prediction of tandem mass spectra from the deep learning framework Prosit. Annotated mirror spectrum plots can be exported from the USE as editable scalable high quality vector graphics. The USE was designed and implemented with minimal external dependencies allowing local usage and integration into other websites (https://github.com/kusterlab/universal_spectrum_explorer).

bioinformatics↗

bioRxiv2018

rawDiag - an R package supporting rational LC-MS method optimization for bottom-up proteomics

Optimizing methods for liquid chromatography coupled to mass spectrometry (LC-MS) is a non-trivial task. Here we present rawDiag, a software tool supporting rational method optimization by providing MS operator-tailored diagnostic plots of scan level metadata. rawDiag is implemented as R package and can be executed on the command line, or through a graphical user interface (GUI) for less experienced users. The code runs platform independent and can process a hundred raw files in less than three minutes on current consumer hardware as we show by our benchmark. In order to demonstrate the functionality of our package, we included a real-world example taken from our daily core facility business.

bioinformatics↗

bioRxiv2018

Engineered Peptide Barcodes for In-Depth Analyses of Binding Protein Ensembles

Binding protein generation relies on laborious screening cascades that process candidate molecules individually. To break with this paradigm, we developed NestLink, a binder selection and identification technology able to biophysically characterize thousands of library members at once without handling individual clones at any stage of the process. NestLink builds on genetically fused barcoding peptides, termed flycodes, which are designed for maximal detectability by mass spectrometry and serve as unique molecular identifiers for accurate deep sequencing. We applied NestLink to overcome current limitations of binder generation. Rare binders against an integral membrane protein were identified directly in the cellular environment of a human pathogen. Hundreds of binder candidates were simultaneously ranked according to kinetic parameters. Adverse effects of target immobilization were overcome by selecting nanobodies against an ABC transporter entirely in solution. NestLink may provide a basis for the selection of tailored binder characteristics directly in tissues or in living organisms.

bioengineering↗