Biology subjects

Wallmann, G.

Publications and source records attributed to Wallmann, G..

8 recordsLinked to original sources

bioRxiv2026

FastaLake selects compact metaproteomic databases from large sequence reservoirs by de novo peptide lookup

Metaproteomics analyses require protein sequence databases, yet the microbial proteins present in complex communities are not known in advance. Searching reference catalogs containing hundreds of millions of candidate sequences reduces identification sensitivity, while generating sample-specific databases through metagenomic sequencing adds time and cost. We introduce FastaLake, a computational workflow that uses full-length de novo peptide predictions by AlphaNovo to retrieve complete proteins from broad sequence resources and selects compact databases for conventional searching. In a defined eight-member community, FastaLake approached reference-search peptide yields without knowing its members. Using public sequence catalogs, FastaLake identified about as many peptides as matched metagenomic databases in two stool cohorts and 20-35% more in intestinal fluid at the same nominal 1% peptide false discovery rate. The resulting proteomes reveal personal functional profiles that persist across intestinal regions despite differences in leading species between intestinal fluid and stool. DNA, RNA and protein profiles agreed on broad differences between patients but only weakly on individual functions. All 726 acquisition-specific databases were smaller than the human reference proteome. Compact databases allowed non-specific searches of all 347 CapScan acquisitions, revealing histidine enrichment at peptide C termini in intestinal fluid. FastaLake enables metaproteomics analysis without matched metagenomic sequencing by constructing search databases from sequence resources and de novo peptide sequencing.

bioinformatics↗

bioRxiv2026

AlphaPeptTools: scverse-native analysis of mass spectrometry-based proteomics

Mass spectrometry (MS)-based proteomics now routinely profiles proteomes at scale, but extracting biological insight and integrating complementary modalities remains challenging. Here, we present AlphaPeptTools, a Python package for the analysis of MS-proteomics data built on the AnnData data structure of the scverse. AlphaPeptTools implements scalable and modular strategies for MS-data ingestion, quality control, preprocessing, and statistical analysis while seamlessly integrating with the scverse ecosystem, unlocking multi-level, spatial, and multimodal analyses.

bioinformatics↗

bioRxiv2026

Deep Learning-Driven Fragment Ion Selection for Improved Quantification in MS based Proteomics

Quantitative proteomics relies on accurate selection of fragment ions for quantification, yet most current algorithms apply simple strategies such as median intensity or single quality filters. Modern data-independent acquisition (DIA) searches generate rich features such as fragment ion correlations, retention time and many others that could be leveraged to assess fragment quality. We introduce QuantSelect, a novel strategy to select optimal fragments by systematically integrating these features via self-supervised deep learning. QuantSelect uses a regularized, weighted-variance loss on intensity traces normalized via our directLFQ algorithm. This allows learning a fragment quality score without ground truth labels, enabling on-the-fly training on label-free DIA datasets. Integrated within our alphaDIA pipeline, QuantSelect significantly improves quantitative accuracy and in some cases substantially corrects protein intensity estimation. Sensitivity in differential expression improved by 68% in a mixed-species benchmarking dataset and by 18% in single-cell data. QuantSelect provides a practical framework for data-driven fragment selection that improves accuracy, precision and downstream inference in DIA proteomics.

bioinformatics↗

bioRxiv2025

scPortrait integrates single-cell images into multimodal modeling

Machine learning increasingly uncovers rules of biology directly from data, enabled by large, standardized datasets. Microscopy images provide rich information on cellular architecture and are accessible at scale across biological systems, making them an ideal foundation for modeling cell behavior. However, a standardized image format does not exist at the single-cell level. Here we present scPortrait, an scverse software package for generation, storage, and application of single-cell image datasets. scPortrait reads, stitches and segments raw fields of view with out-of-core computation scaling to larger-than-memory datasets. Parallelization enables rapid extraction of individual cells into a standardized single-cell image format with fast access to accelerate machine learning. scPortrait enables analysis across modalities including images, proteomics and transcriptomics, identifying cancer-associated macrophage subpopulations by morphology and embedding single-cell images into transcriptome atlases. scPortrait turns microscopy images into a reusable resource for integrative cell modeling, establishing single-cell images as a core modality in systems biology.

systems biology↗

bioRxiv2024

AlphaDIA enables End-to-End Transfer Learning for Feature-Free Proteomics

Mass spectrometry (MS)-based proteomics continues to evolve rapidly, opening more and more application areas. The scale of data generated on novel instrumentation and acquisition strategies pose a challenge to bioinformatic analysis. Search engines need to make optimal use of the data for biological discoveries while remaining statistically rigorous, transparent and performant. Here we present alphaDIA, a modular open-source search framework for data independent acquisition (DIA) proteomics. We developed a feature-free identification algorithm particularly suited for detecting patterns in data produced by sensitive time-of-flight instruments. It naturally adapts to novel, more eTicient scan modes that are not yet accessible to previous algorithms. Rigorous benchmarking demonstrates competitive identification and quantification performance. While supporting empirical spectral libraries, we propose a new search strategy named end-to-end transfer learning using fully predicted libraries. This entails continuously optimizing a deep neural network for predicting machine and experiment specific properties, enabling the generic DIA analysis of any post-translational modification (PTM). AlphaDIA provides a high performance and accessible framework running locally or in the cloud, opening DIA analysis to the community.

bioinformatics↗

bioRxiv2023

SPARCS, a platform for genome-scale CRISPR screening for spatial cellular phenotypes

Forward genetic screening associates phenotypes with genotypes by randomly inducing mutations and then identifying those that result in phenotypic changes of interest. Here we present spatially resolved CRISPR screening (SPARCS), a platform for microscopy-based genetic screening for spatial cellular phenotypes. SPARCS uses automated high-speed laser microdissection to physically isolate phenotypic variants in situ from virtually unlimited library sizes. We demonstrate the potential of SPARCS in a genome-wide CRISPR-KO screen on autophagosome formation in 40 million cells. Coupled to deep learning image analysis, SPARCS recovered almost all known macroautophagy genes in a single experiment and discovered a role for the ER-resident protein EI24 in autophagosome biogenesis. Harnessing the full power of advanced imaging technologies, SPARCS enables genome-wide forward genetic screening for diverse spatial phenotypes in situ.

systems biology↗

bioRxiv2023

Data-Driven Optimization of DIA Mass-Spectrometry by DO-MS

Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of mass spectrometry methods (DO-MS). The DO-MS app v2.0 (do-ms.slavovlab.net) allows to optimize and evaluate results from both label free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant for single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication quality figures, that can be easily shared. The source code is available at: github.com/SlavovLab/DO-MS. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=68 SRC="FIGDIR/small/526809v3_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@1334corg.highwire.dtl.DTLVardef@cec8bcorg.highwire.dtl.DTLVardef@1dcc07eorg.highwire.dtl.DTLVardef@1e1abd2_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2022

Synchro-PASEF allows precursor-specific fragment ion extraction and interference removal in data-independent acquisition

Data-independent acquisition (DIA) methods have become increasingly popular in mass spectrometry (MS)-based proteomics because they enable continuous acquisition of fragment spectra for all precursors simultaneously. However, these advantages come with the challenge of correctly reconstructing the precursor-fragment relationships in these highly convoluted spectra for reliable identification and quantification. Here we introduce a scan mode for the combination of trapped ion mobility spectrometry (TIMS) with parallel accumulation - serial fragmentation (PASEF) that seamlessly and continuously follows the natural shape of the ion cloud in ion mobility and peptide precursor mass dimensions. Termed synchro-PASEF, it increases the detected fragment ion current several-fold at sub-second cycle times. Consecutive quadrupole selection windows move synchronously through the mass and ion mobility range, defining precursor-quadrupole relationships. In this process, the quadrupole slices through the peptide precursors, which separates fragment ion signals of each precursor into adjacent synchro-PASEF scans. This precisely defines precursor - fragment relationships in ion mobility and mass dimensions and effectively deconvolutes the DIA fragment space. Importantly, the partitioned parts of the fragment ion transitions provide a further dimension of specificity via a lock and key mechanism. This is also advantageous for quantification, where signals from interfering precursors in the DIA selection window do not affect all partitions of the fragment ion, allowing to retain only the specific parts for quantification. Overall, we establish the defining features of synchro-PASEF and explore its potential for proteomic analyses.

biochemistry↗