Biology subjects

Wolski, W. E.

Publications and source records attributed to Wolski, W. E..

6 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↗

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↗

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↗

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↗

bioRxiv2021

APOE2, E3 and E4 differentially modulate cellular homeostasis, cholesterol metabolism and inflammatory response in isogenic iPSC-derived astrocytes

Apolipoprotein E (APOE) is the principal lipid carrier in the CNS and mainly expressed by astrocytes. The three different APOE alleles (E2, E3, and E4) impose differential risk to Alzheimers disease (AD); E2 is protective, E3 is defined as average risk, while E4 is the major genetic risk factor for sporadic AD. Despite recent advances, the fundamental role of different APOE alleles in brain homeostasis is still poorly understood. To uncover the functional role of APOE in human astrocytes, we differentiated human APOE-isogenic iPSCs (E4, E3, E2 and APOE-knockout (KO)) to functional astrocytes (hereafter "iAstrocytes"), with a resting, non-proliferating phenotype. Functional assays indicated that polymorphisms in APOE (APOE4>E3>E2=KO) reduced iAstrocyte metabolic and clearance functions including glutamate uptake and receptor-mediated uptake of {beta}-amyloid aggregates. We performed unlabelled mass spectrometry-based proteomic analysis of iAstrocytes at baseline and after activation with interleukin-1{beta} (IL-1{beta}) showing a reduction of cholesterol and lipid metabolic and biosynthetic pathways, and an increase of immunoregulatory pathways at baseline (E4>E3>E2). Cholesterol efflux and biosynthesis were reduced in E4 iAstrocytes, and subcellular localization of cholesterol in lysosomes was increased. In APOE-KO iAstrocytes, APOE-independent mechanisms showed to be proficient in mediating cholesterol biosynthesis and efflux. Proteomic analysis of IL-1{beta}-treated iAstrocytes showed an increase of cholesterol/lipid metabolism and biosynthesis as well as inflammatory pathways. Furthermore, cholesterol efflux, which was reduced in APOE4 iAstrocytes at baseline, was alleviated in activated E4 iAstrocytes. Inflammatory cytokine release was exacerbated upon IL-1{beta} treatment in E4 iAstrocytes (E4>E3>E2>KO), in line with the proteomic data. Taken together, we show that APOE plays a major role in several physiological and metabolic processes in human astrocytes with APOE4 pushing iAstrocytes to a disease-relevant phenotype, causing dysregulated cholesterol/lipid homeostasis, increased inflammatory signalling and reduced {beta}-amyloid uptake while APOE2 iAstrocytes show opposing effects. Our study provides a new reference for AD-relevant proteomic and metabolic changes, mediated by the three main APOE isoforms in human astrocytes.

neuroscience↗

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↗