Biology subjects

Sachsenberg, T.

Publications and source records attributed to Sachsenberg, T..

17 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↗

bioRxiv2026

onsite: An Integrated Framework for Phosphosite Localization and False Localization Rate Estimation

With the rapid development of mass spectrometry-based proteomics, the volume of phosphoproteomic data has increased substantially. However, accurate localization of phosphorylation sites and standardized statistical validation remain critical analytical bottlenecks. To address the lack of standardized cross-algorithm evaluation, we introduce onsite, a unified and open-source Python framework. onsite integrates an alanine-decoy strategy to estimate the false localization rate (FLR) across three algorithms: AScore, PhosphoRS, and pyLucXor. This modular architecture efficiently processes large-scale datasets and enables global FLR calculation. Benchmarking on the standard synthetic phosphopeptide dataset PXD000138 highlighted distinct inter-algorithmic variations. Using the same 5% global FLR threshold, pyLucXor localized the most target sites (28,353). It also reached a high accuracy (91.22%) against the known ground truth, resulting in the largest number of correctly localized sites (25,865). Reanalysis of the highly fractionated, large-scale PXD012255 dataset further demonstrated that native integration of onsite into the quantms pipeline enables scalable processing and provides a standardized framework for FLR control in large-scale phosphoproteomics. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=64 SRC="FIGDIR/small/737157v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@e4c85dorg.highwire.dtl.DTLVardef@1e8464org.highwire.dtl.DTLVardef@185cea1org.highwire.dtl.DTLVardef@1c0d1bc_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

π-MSNet: A billion-scale, AI-ready living proteomics data portal

Artificial intelligence (AI) is reshaping proteomics workflows, delivering remarkable gains in both peptide identification sensitivity and quantitative performance. However, the potential of deep learning models in proteomics has not been fully exploited due to the scarcity of large-scale, high-quality and consistently labeled datasets. Here, we present {pi}-MSNet, a billion-scale, AI-ready living mass spectrometry (MS) data portal. Using a uniform identification and quality control workflow, it comprises over 1.66 billion MS/MS spectra, 501 million peptide-spectrum matches (PSMs), and 9 million precursors from 36,356 LC-MS/MS runs across ten instrument types and 55 diverse species. Through community collaboration, the data are shared via international, interactive, and living web resources. Enabled by the built-in MSNetLoader Python API for seamless and scalable data access--with native support for PyTorch and TensorFlow--{pi}-MSNet provides an AI-ready data framework for efficient training and systematic benchmarking of multiple models across three representative tasks (e.g., MS/MS spectrum prediction, retention time prediction, and de novo peptide sequencing). In particular, by retraining multiple models on {pi}-MSNet, we achieved consistent performance improvements over their original versions. These improved models were subsequently integrated into the {pi}-MSNet agent to enable interactive, deployment-free use. Through SDRF (Sample and Data Relationship Format) metadata, an open-source cloud analysis workflow, and a community-driven interactive data portal that supports continuous data submission, {pi}-MSNet serves as a living, AI-ready resource for reproducible benchmarking, robust model training, and accelerated AI innovation in proteomics.

bioinformatics↗

bioRxiv2026

Deep learning enables direct HLA typing from immunopeptidomics data

The immune system eliminates malignant and infected cells through T-cell-mediated recognition of peptides presented by human leukocyte antigen molecules. Mass spectrometry-based immunopeptidomics enables unbiased identification of naturally presented HLA-restricted peptides and has become central to the development of T-cell-based immunotherapies. However, immunopeptidomics data reflects the combined peptide presentation of multiple HLA alleles, and determining which allotypes are represented in this multi-allelic complexity remains an unmet computational challenge. Here, we introduce immunotype, a deep learning-based ensemble predictor for HLA class I allotyping directly from immunopeptidomics data. Immunotype integrates peptide and HLA protein sequence information through transformer encoders and a graph neural network, complemented by a curated mono-allelic reference of known peptide-HLA binding preferences. Immunotype achieves an overall accuracy of 87.2% at protein-level resolution across diverse tissues and thereby enables rapid, cost-effective HLA typing of large-scale immunopeptidomics datasets.

bioinformatics↗

bioRxiv2026

quantms-rescoring enables deep proteome coverage across protein quantification, immunopeptidomics, and post-translational modifications experiments.

The growing volume of public proteomics datasets and the advent of novel machine learning (ML)-based methods create unprecedented opportunities for discovery through large-scale reanalysis. However, traditional desktop tools are increasingly insufficient for processing and integrating data at this scale. To address this challenge, we present a novel package, quantms-rescoring, that extends the cloud-native quantms workflow with a machine learning-based rescoring module. Unlike prior tools that rescore single-engine outputs, quantms-rescoring seamlessly integrates multiple search engines (SAGE, COMET, and MSGF+), performs automatic model selection, model fine-tuning, and scales reproducibly on cloud infrastructures. In quantms-rescoring, we rely on multiple fragment-ion intensity (AlphaPeptDeep and MS2PIP) and retention-time prediction (DeepLC) methods to improve results from multiple peptide database search engines. It features automatic model selection, fine-tuning, and retraining for MS/MS intensity and retention time prediction to select the best model for a given dataset. We applied the novel workflow to five representative datasets spanning DDA label-free quantification, TMT 10-plex isobaric labelling of tumor proteomics data, immunopeptidomics, phospho-proteomics, and unseen lysine malonylation experiments. We achieved a 16-22.8% increase in identified spectra, along with the quantification of 2191 additional phosphorylated peptides and 1337 phosphosites. In the tandem mass tag (TMT)-labeled clear cell renal cell carcinoma dataset, 76 novel differentially expressed multiple search engines identified proteins with quantms-rescoring. Additionally, novel 11,688 HLA-II potential binders were detected in the immunopeptidomics dataset by multiple search engines with quantms-rescoring. For unseen malonylation data, we reported more than 58.8% malonylation PSMs and 30.5% modification sites than COMET alone. Together, these results show that integrating multi-engine searches with machine learning-derived features can be combined in a scalable workflow that enhances identification, PTM localization, and quantification performance.

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↗

bioRxiv2025

ibaqpy: A scalable Python package for baseline quantification in proteomics leveraging SDRF metadata

Intensity-based absolute quantification (iBAQ) is essential in proteomics as it allows for the assessment of a proteins absolute abundance in various samples or conditions. However, the computation of these values for increasingly large-scale and high-throughput experiments, such as those using DIA, TMT, or LFQ workflows, poses significant challenges in scalability and reproducibility. Here, we present ibaqpy (https://github.com/bigbio/ibaqpy), a Python package designed to compute iBAQ values efficiently for experiments of any scale. ibaqpy leverages the Sample and Data Relationship Format (SDRF) metadata standard to incorporate experimental metadata into the quantification workflow. This allows for automatic normalization and batch correction while accounting for key aspects of the experimental design, such as technical and biological replicates, fractionation strategies, and sample conditions. Designed for large-scale proteomics datasets, ibaqpy can also recompute iBAQ values for existing experiments when an SDRF is available. We showcased ibaqpys capabilities by reanalyzing 17 public proteomics datasets from ProteomeXchange, covering HeLa cell lines with 4,921 samples and 5,766 MS runs, quantifying a total of 11,014 proteins. In our reanalysis, ibaqpy is a key component in automating reproducible quantification, reducing manual effort and making quantitative proteomics more accessible while supporting FAIR principles for data reuse.

bioinformatics↗

bioRxiv2024

Chemical crosslinking extends and complements UV crosslinking in analysis of RNA/DNA nucleic acid-protein interaction sites by mass spectrometry

UV (ultra-violet) crosslinking with mass spectrometry (XL-MS) has been established for identifying RNA- and DNA-binding proteins along with their domains and amino acids involved. Here, we explore chemical XL-MS for RNA-protein, DNA-protein, and nucleotide-protein complexes in vitro and in vivo. We introduce a specialized nucleotide-protein-crosslink search engine, NuXL, for robust and fast identification of such crosslinks at amino acid resolution. Chemical XL-MS complements UV XL-MS by generating different crosslink species, increasing crosslinked protein yields in vivo almost four-fold, and thus it expands the structural information accessible via XL-MS. Our workflow facilitates integrative structural modelling of nucleic acid-protein complexes and adds spatial information to the described RNA-binding properties of enzymes, for which crosslinking sites are often observed close to their cofactor-binding domains. In vivo UV and chemical XL-MS data from E. coli cells analysed by NuXL establish a comprehensive nucleic acid-protein crosslink inventory with crosslink sites at amino acid level for more than 1500 proteins. Our new workflow combined with the dedicated NuXL search engine identified RNA crosslinks that cover most RNA-binding proteins, with DNA and RNA crosslinks detected in transcriptional repressors and activators.

biochemistry↗

bioRxiv2022

Unified and standardized mass spectrometry data processing in Python using spectrum_utils

spectrum_utils is a Python package for mass spectrometry data processing and visualization. Since its introduction, spectrum_utils has grown into a fundamental software solution that powers various applications in proteomics and metabolomics, ranging from spectrum preprocessing prior to spectrum identification and machine learning applications, to spectrum plotting from online data repositories, and assisting data analysis tasks for dozens of other projects. Here we present updates to spectrum_utils, which include new functionality to integrate mass spectrometry community data standards, enhanced mass spectral data processing, and unified mass spectral data visualization in Python. spectrum_utils is freely available as open source at https://github.com/bittremieux/spectrum_utils.

bioinformatics↗

bioRxiv2022

A comprehensive evaluation of consensus spectrum generation methods in proteomics

Spectrum clustering is a powerful strategy to minimize redundant mass spectral data by grouping highly similar mass spectra corresponding to repeatedly measured analytes. Based on spectrum similarity, near-identical spectra are grouped in clusters, after which each cluster can be represented by its so-called consensus spectrum for downstream processing. Although several algorithms for spectrum clustering have been adequately benchmarked and tested, the influence of the consensus spectrum generation step is rarely evaluated. Here, we present an implementation and benchmark of common consensus spectrum algorithms, including spectrum averaging, spectrum binning, the most similar spectrum, and the best-identified spectrum. We have analyzed diverse public datasets using two different clustering algorithms (spectra-cluster and MaRaCluster) to evaluate how the consensus spectrum generation procedure influences downstream peptide identification. The BEST and BIN methods were found the most reliable methods for consensus spectrum generation, including for datasets with post-translational modifications (PTM) such as phosphorylation. All source code and data of the present study are freely available on GitHub at https://github.com/statisticalbiotechnology/representative-spectra-benchmark.

bioinformatics↗

bioRxiv2021

Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides.

SummaryWe have implemented the pypgatk package and the pgdb workflow to create proteogenomics databases based on ENSEMBL resources. The tools allow the generation of protein sequences from novel protein-coding transcripts by performing a three-frame translation of pseudogenes, lncRNAs, and other non-canonical transcripts, such as those produced by alternative splicing events. It also includes exonic out-of-frame translation from otherwise canonical protein-coding mRNAs. Moreover, the tool enables the generation of variant protein sequences from multiple sources of genomic variants including COSMIC, cBioportal, gnomAD, and mutations detected from sequencing of patient samples. pypgatk and pgdb provide multiple functionalities for database handling, notably optimized target/decoy generation by the algorithm DecoyPyrat. Finally, we perform a reanalysis of four public datasets in PRIDE by generating cell-type specific databases for 65 cell lines using the pypgatk and pgdb workflow, revealing a wealth of non-canonical or cryptic peptides amounting to more than 10% of the total number of peptides identified (43,501 out of 402,512). AvailabilityThe software is freely available. pypgatk: (https://github.com/bigbio/py-pgatk/), and pgdb: (https://github.com/nf-core/pgdb) ContactYasset Perez-Riverol (yperez@ebi.ac.uk), Rui Branca (rui.mamede-branca@ki.se) Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2021

A proteomics sample metadata representation for multiomics integration, and big data analysis.

The amount of public proteomics data is increasing at an extraordinary rate. Hundreds of datasets are submitted each month to ProteomeXchange repositories, representing many types of proteomics studies, focusing on different aspects such as quantitative experiments, post-translational modifications, protein-protein interactions, or subcellular localization, among many others. For every proteomics dataset, two levels of data are captured: the dataset description, and the data files (encoded in different file formats). Whereas the dataset description and data file formats are supported by all ProteomeXchange partner repositories, there is no standardized format to properly describe the sample metadata and their relationship with the dataset files in a way that fully allows their understanding or re-analysis. It is left to the users choice whether to provide or not an ad hoc document containing this information. Therefore, in many cases, understanding the study design and data requires going back to the associated publication. This can be tedious and may be restricted in the case of non-open access publications. In many cases, this problem limits the generalization and reuse of public proteomics data. Here we present a standard representation for sample metadata tailored to proteomics datasets produced by the HUPO Proteomics Standards Initiative and supported by ProteomeXchange resources. We repurposed the existing data format MAGE-TAB used routinely in the transcriptomics field to represent and annotate proteomics datasets. MAGETAB-Proteomics defines a set of annotation rules that the datasets submitted to ProteomeXchange should follow, ranging from sample properties to data analysis protocols. We also introduce a crowdsourcing project that enabled the manual curation of over 200 public datasets using MAGE-TAB-Proteomics. In addition, we describe an ecosystem of tools and libraries that were developed to validate and submit sample metadata-related information to ProteomeXchange. We expect that these tools will improve the reproducibility of published results and facilitate the reanalysis and integration of public proteomics datasets.

bioinformatics↗

bioRxiv2020

DIAproteomics: A multi-functional data analysis pipeline for data-independent-acquisition proteomics and peptidomics

Data-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. Main advantages include greater reproducibility, sensitivity and dynamic range compared to data-dependent acquisition (DDA). However, data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics a multi-functional, automated high-throughput pipeline implemented in Nextflow that allows to easily process proteomics and peptidomics DIA datasets on diverse compute infrastructures. Central components are well-established tools such as the OpenSwathWorkflow for DIA spectral library search and PyProphet for false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and carry out retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from statistical post-processing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is open-source software and available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/.

bioinformatics↗

bioRxiv2020

SmartPeak automates targeted and quantitative metabolomics data processing

SmartPeak is an application that encapsulates advanced algorithms to enable fast, accurate, and automated processing of CE-, GC- and LC-MS(/MS) data, and HPLC data for targeted and semi-targeted metabolomics, lipidomics, and fluxomics experiments. HighlightsO_LINovel algorithms for retention time alignment, calibration curve fitting, and peak integration C_LIO_LIEnables reproducibility by reducing operator bias and ensuring high QC/QA C_LIO_LIAutomated pipeline for high throughput targeted and/or quantitative metabolomics, lipidomics, and fluxomics data processing from multiple analytical instruments C_LIO_LIManually curated data set of LC-MS/MS, GC-MS, and HPLC integrated peaks for further algorithm development and benchmarking C_LI

bioinformatics↗

bioRxiv2019

EPIFANY - A method for efficient high-confidence protein inference

Accurate protein inference under the presence of shared peptides is still one of the key problems in bottom-up proteomics. Most protein inference tools employing simple heuristic inference strategies are efficient, but exhibit reduced accuracy. More advanced probabilistic methods often exhibit better inference quality but tend to be too slow for large data sets.\n\nHere we present a novel protein inference method, EPIFANY, combining a loopy belief propagation algorithm with convolution trees for efficient processing of Bayesian networks. We demonstrate that EPIFANY combines the reliable protein inference of Bayesian methods with significantly shorter runtimes. On the 2016 iPRG protein inference benchmark data EPIFANY is the only tested method which finds all true-positive proteins at a 5% protein FDR without strict pre-filtering on PSM level, yielding an increase in identification performance (+10% in the number of true positives and +35% in partial AUC) compared to previous approaches. Even very large data sets with hundreds of thousands of spectra (which are intractable with other Bayesian and some non-Bayesian tools) can be processed with EPIFANY within minutes. The increased inference quality including shared peptides results in better protein inference results and thus increased robustness of the biological hypotheses generated.\n\nEPIFANY is available as open-source software for all major platforms at https://OpenMS.de/epifany.

bioinformatics↗

bioRxiv2019

ThermoRawFileParser: modular, scalable and cross-platform RAW file conversion

The field of computational proteomics is approaching the big data age, driven both by a continuous growth in the number of samples analysed per experiment, as well as by the growing amount of data obtained in each analytical run. In order to process these large amounts of data, it is increasingly necessary to use elastic compute resources such as Linux-based cluster environments and cloud infrastructures. Unfortunately, the vast majority of cross-platform proteomics tools are not able to operate directly on the proprietary formats generated by the diverse mass spectrometers. Here, we presented ThermoRawFileParser, an open-source, crossplatform tool that converts Thermo RAW files into open file formats such as MGF and to the HUPO-PSI standard file format mzML. To ensure the broadest possible availability, and to increase integration capabilities with popular workflow systems such as Galaxy or Nextflow, we have also built Conda and BioContainers containers around ThermoRawFileParser. In addition, we implemented a user-friendly interface (ThermoRawFileParserGUI) for those users not familiar with command-line tools. Finally, we performed a benchmark of ThermoRawFileParser and msconvert to verify that the converted mzML files contain reliable quantitative results.

bioinformatics↗

bioRxiv2018

A computational platform for high-throughput analysis of RNA sequences and modifications by mass spectrometry

The field of epitranscriptomics is growing in importance, with chemical modification of RNA being associated with a wide variety of biological phenomena. A pivotal challenge in this area is the identification of modified RNA residues within their sequence contexts. Next-generation sequencing approaches are generally unable to capture modifications, although workarounds for some epigenetic marks exist. Mass spectrometry (MS) offers a comprehensive solution by using analogous approaches to shotgun proteomics. However, software support for the analysis of RNA MS data is inadequate at present and does not allow high-throughput processing. In particular, existing software solutions lack the raw performance and statistical grounding to efficiently handle the large variety of modifications present on RNA. We present a free and open-source database search engine for RNA MS data, called NucleicAcidSearchEngine (NASE), that addresses these shortcomings. We demonstrate the capability of NASE to reliably identify a wide range of modified RNA sequences in three original datasets of varying complexity. In a human tRNA sample, we characterize over 20 different modification types simultaneously and find many cases of incomplete modification.

bioinformatics↗