Biology subjects

Bielow, C.

Publications and source records attributed to Bielow, C..

6 recordsLinked to original sources

bioRxiv2026

mzQC: a versatile way to communicate quality information for biological mass spectrometry

Reliable quality control (QC) is essential for reproducible biological mass spectrometry, yet QC information is still typically stored in tool-specific, poorly interoperable formats that hinder comparison, reuse, and transparent reporting. We present mzQC, a community-developed standard from the HUPO Proteomics Standards Initiative for the compact, machine-readable representation of QC metrics and their associated metadata across mass spectrometry workflows. mzQC is implemented in JSON, organized to support both individual mass spectrometry acquisitions and collections thereof, with formal QC metrics definitions supported by controlled vocabularies to ensure unambiguous metric interpretation and semantic consistency. The format is supported by validation tools and reference libraries in Python, R, and Java, facilitating integration into existing software ecosystems. We demonstrate the utility of mzQC across diverse analytical modalities and QC settings, spanning longitudinal instrument monitoring, acquisition-method evaluation, study-level quality assessment, repository-scale data characterization, and spatially resolved mass spectrometry imaging. Together, these use cases show that mzQC provides not merely a file format, but a shared foundation for interoperable QC reporting, large-scale data assessment, and more rigorous, reproducible, and reusable mass spectrometry-based research.

bioinformatics↗

bioRxiv2025

pmultiqc: An open-source, lightweight, and metadata-oriented QC reporting library for MS proteomics

The increasing scale and complexity of proteomics data demand robust, scalable, and interpretable quality control (QC) frameworks to ensure data reliability and reproducibility. Here, we present pmultiqc, an open-source Python package that standardizes and generates web-based QC reports across multiple proteomics data analysis platforms. Built on top of the widely adopted MultiQC framework, pmultiqc offers specialized modules tailored to mass spectrometry workflows, with full initial support for quantms, DIA-NN, MaxQuant/MaxDIA, and mzIdentML/mzML-based pipelines. The package computes a wide range of QC metrics, including raw intensity distributions, identification rates, retention time consistency, and missing value patterns, and presents them in interactive, publication-ready reports. By leveraging sample metadata in the SDRF format, pmultiqc enables metadata-aware QC and introduces, for the first time in proteomics, QC reports and metrics guided by standardized sample metadata. Its modular architecture allows easy extension to new workflows and formats. Alongside comprehensive documentation and examples for running pmultiqc locally or integrated into existing workflows, we offer a cloud-based service that enables users to generate QC reports from their own data or public PRIDE datasets.

bioinformatics↗

bioRxiv2024

CMAPLE: efficient phylogenetic inference in the pandemic era

We have recently introduced MAPLE (MAximum Parsimonious Likelihood Estimation), a new pandemic-scale phylogenetic inference method exclusively designed for genomic epidemiology. In response to the need for enhancing MAPLEs performance and scalability, here we present two key components: (1) CMAPLE software, a highly optimized C++ reimplementation of MAPLE with many new features and advancements; and (2) CMAPLE library, a suite of Application Programming Interfaces to facilitate the integration of the CMAPLE algorithm into existing phylogenetic inference packages. Notably, we have successfully integrated CMAPLE into the widely used IQ-TREE 2 software, enabling its rapid adoption in the scientific community. These advancements serve as a vital step towards better preparedness for future pandemics, offering researchers powerful tools for large-scale pathogen genomic analysis.

evolutionary biology↗

bioRxiv2024

Communicating mass spectrometry quality information in mzQC with Python, R, and Java

Mass spectrometry is a powerful technique for analyzing molecules in complex biological samples. However, inter- and intra-laboratory variability and bias can affect the data due to various factors, including sample handling and preparation, instrument calibration and performance, and data acquisition and processing. To address this issue, the Quality Control (QC) working group of the Human Proteome Organizations Proteomics Standards Initiative has established the standard mzQC file format for reporting and exchanging information relating to data quality. mzQC is based on the JavaScript Object Notation (JSON) format and provides a lightweight yet versatile file format that can be easily implemented in software. Here, we present open-source software libraries to process mzQC data in three programming languages: Python, using pymzqc; R, using rmzqc; and Java, using jmzqc. The libraries follow a common data model and provide shared functionality to operate on mzQC files, including the (de)serialization and validation of mzQC files. We demonstrate use of the software libraries for extracting, analyzing, and visualizing QC metrics from different sources and show how these libraries can be integrated with each other, with existing software tools, and in automated workflows for the QC of mass spectrometry data. All software libraries are available as open source under the MS-Quality-Hub organization on GitHub (https://github.com/MS-Quality-Hub).

bioinformatics↗

bioRxiv2019

Integrative functional genomics decodes herpes simplex virus 1

Since the genome of herpes simplex virus 1 (HSV-1) was first sequenced more than 30 years ago, its predicted 80 genes have been intensively studied. Here, we unravel the complete viral transcriptome and translatome during lytic infection with base-pair resolution by computational integration of multi-omics data. We identified a total of 201 viral transcripts and 284 open reading frames (ORFs) including all known and 46 novel large ORFs. Multiple transcript isoforms expressed from individual gene loci explain translation of the vast majority of novel viral ORFs as well as N-terminal extensions (NTEs) and truncations thereof. We show that key viral regulators and structural proteins possess NTEs, which initiate from non-canonical start codons and govern subcellular protein localization and packaging. We validated a novel non-canonical large spliced ORF in the ICP0 locus and identified a 93 aa ORF overlapping ICP34.5 that is thus also deleted in the FDA-approved oncolytic virus Imlygic. Finally, we extend the current nomenclature to include all novel viral gene products. Taken together, this work provides a valuable resource for future functional studies, vaccine design and oncolytic therapies.

microbiology↗

bioRxiv2018

Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer

Metabolic alterations can serve as targets for diagnosis and therapy of cancer. Due to the highly complex regulation of cellular metabolism, definite identification of metabolic pathway alterations remains challenging and requires sophisticated experimentation. Here, we applied a comprehensive kinetic model of the central carbon metabolism (CCM) to characterize metabolic reprogramming in murine liver cancer. We show that relative differences of protein abundances of metabolic enzymes obtained by mass spectrometry can be used to scale maximal enzyme capacities. Model simulations predicted tumor - specific alterations of various components of the CCM, a selected number of which were subsequently verified by in vitro and in vivo experiments. Furthermore, we demonstrate the ability of the kinetic model to identify metabolic pathways whose inhibition results in selective tumor cell killing. Our systems biology approach establishes that combining cellular experimentation with computer simulations of physiology-based metabolic models enables a comprehensive understanding of deregulated energetics in cancer.

systems biology↗