Biology subjects

Schwammle, V.

Publications and source records attributed to Schwammle, V..

6 recordsLinked to original sources

bioRxiv2025

Missing Values Are Valuable: Shifting Focus from Amount to Form of Missing Data

Missing data is often treated as a nuisance, routinely imputed or excluded from statistical analyses, especially in nominal datasets where its structure cannot be easily modeled. However, the form of missingness itself can reveal hidden relationships, substructures, and biological or operational constraints within a dataset. In this study, we present a graph-theoretic approach that reinterprets missing values not as gaps to be filled, but as informative signals. By representing nominal variables as nodes and encoding observed or missing associations as edges, we construct both weighted and unweighted bipartite graphs to analyze modularity, nestedness, and projection-based similarities. This framework enables downstream clustering and structural characterization of nominal data based on the topology of observed and missing associations; edge prediction via multiple imputation strategies is included as an optional downstream analysis to evaluate how well inferred values preserve the structure identified in the non-missing data. Across a series of biological, ecological, and social case studies, including proteomics data, the BeatAML drug screening dataset, ecological pollination networks, and HR analytics, we demonstrate that the structure of missing values can be highly informative. These configurations often reflect meaningful constraints and latent substructures, providing signals that help distinguish between data missing at random and not at random. When analyzed with appropriate graph-based tools, these patterns can be leveraged to improve the structural understanding of data and provide complementary signals for downstream tasks such as clustering and similarity analysis. Our findings support a conceptual shift: missing values are not merely analytical obstacles but valuable sources of insight that, when properly modeled, can enrich our understanding of complex nominal systems across domains. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/670516v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@99c5eaorg.highwire.dtl.DTLVardef@1909d8corg.highwire.dtl.DTLVardef@1578c93org.highwire.dtl.DTLVardef@ce2e90_HPS_FORMAT_FIGEXP M_FIG C_FIG Shiny app address https://ehsan-zangene.shinyapps.io/nimaa_app/

bioinformatics↗

bioRxiv2025

Isolation and characterization of synaptic structures from human neural organoids

Human neural organoids (NOs) provide a powerful platform for investigating synaptic development and dysfunction during early neurodevelopment. However, methodologies for isolating functional synaptic structures from these models remain limited. Here, we present a differential centrifugation protocol enabling the enrichment of growth cone particles (GCPs) and immature synaptosomes from air-liquid interface cerebral organoids (ALI-COs) at distinct developmental stages (day 90 and 150). Notably, the method avoids density gradients, requires minimal starting material while maintaining reproducibility across human and murine tissues. Quantitative proteomic profiling revealed significant enrichment of growth cone markers (e.g. GAP43) and classical synaptosomal proteins (e.g. PCLO, BSN, SYN1). Transmission electron microscopy (TEM) confirmed the presence of membrane-enclosed GCPs with fibrous content and mitochondria in day 90 isolates, and immature synaptosomes containing synaptic vesicles on day 150. Functional viability of both types of synaptic structures was demonstrated through KCl-induced depolarization, which triggered phosphorylation changes in growth cone proteins (GAP43, MARCKS, MARCKSL1), cytoskeletal regulators (DCLK1, SHTN1, MARK4, MAP1B) and protein kinases (CAMK2G, PRKCE) in day 90 GCPs, as well as classical synaptic vesicle cycle proteins (SYN1, DNM1, RPH3A) at day 150. Overall, this study establishes a centrifugation-based protocol for isolating growth cones and immature synapses from human organoids, capturing key stages of synaptic development and enabling scalable, patient-compatible models to study synaptic function and dysfunction in neurodevelopmental and neurodegenerative disorders.

neuroscience↗

bioRxiv2024

Data mining antibody sequences for database searching in bottom-up proteomics

Mass spectrometry (MS)-based proteomics allows identifying and quantifying thousands of proteins but suffers from challenges when measuring human antibodies due to their vast variety. The mainly used bottom-up proteomics approaches rely on database searches that compare experimental values of peptides and their fragments to theoretical values derived from protein sequences in a database. While the human body can produce millions of distinct antibodies, the current databases for human antibodies such as UniProtKB/Swiss-Prot are limited to only 1095 sequences (as of 2024 Jan). This limitation may hinder the identification of new antibodies using mass spectrometry. Therefore, extending the database for mass spectrometry is an important task for discovering new antibodies. Recent genomic studies have compiled millions of human antibody sequences publicly accessible through the Observed Antibody Space (OAS) database. However, this data has yet to be exploited to confirm the presence of these antibodies. In this study, we adopted this extensive collection of antibody sequences for conducting efficient database searches in publicly available proteomics data with a focus on the SARS-CoV-2 disease. Thirty million heavy antibody sequences from 146 SARS-CoV-2 patients in the OAS database were digested in silico to obtain 18 million unique peptides. These peptides were then used to create new databases for bottom-up proteomics. We used those databases for searching new antibody peptides in publicly available SARS-CoV-2 human plasma samples in the Proteomics Identification Database (PRIDE). This approach avoids false positives in antibody peptide identification as confirmed by searching against negative controls (brain samples) and employing different database sizes. We show that the found sequences provide valuable information to distinguish diseased from healthy and expect that the newly discovered antibody peptides can be further employed to develop therapeutic antibodies. The method will be broadly applicable to find characteristic antibodies for other diseases.

bioinformatics↗

bioRxiv2021

VIQoR: a web service for Visually supervised protein Inference and protein Quantification

MotivationIn quantitative bottom-up mass spectrometry (MS)-based proteomics the reliable estimation of protein concentration changes from peptide quantifications between different biological samples is essential. This estimation is not a single task but comprises the two processes of protein inference and protein abundance summarization. Furthermore, due to the high complexity of proteomics data and associated uncertainty about the performance of these processes, there is a demand for comprehensive visualization methods able to integrate protein with peptide quantitative data including their post-translational modifications. Hence, there is a lack of a suitable tool that provides post-identification quantitative analysis of proteins with simultaneous interactive visualization. ResultsIn this article, we present VIQoR, a user-friendly web service that accepts peptide quantitative data of both labeled and label-free experiments and accomplishes the processes for relative protein quantification, along with interactive visualization modules, including the novel VIQoR plot. We implemented two parsimonious algorithms to solve the protein inference problem, while protein summarization is facilitated by a well established factor analysis algorithm called fast-FARMS followed by a weighted average summarization function that minimizes the effect of missing values. In addition, summarization is optimized by the so-called Global Correlation Indicator (GCI). We test the tool on three publicly available ground truth datasets and demonstrate the ability of the protein inference algorithms to handle degenerate peptides. We furthermore show that GCI increases the accuracy of the quantitative analysis in data sets with replicated design. Availability and implementationVIQoR is accessible at: http://computproteomics.bmb.sdu.dk:8192/app_direct/VIQoR/ The source code is available at: https://bitbucket.org/vtsiamis/viqor/ Contactveits@bmb.sdu.dk Supplementary informationSupplementary data are available at Bioinformatics 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↗

bioRxiv2019

ComplexBrowser: a tool for identification and quantification of protein complexes in large scale proteomics datasets

We have developed ComplexBrowser, an open source, online platform for supervised analysis of quantitative proteomics data that focuses on protein complexes. The software uses information from CORUM and Complex Portal databases to identify protein complex components. Based on the expression changes of individual complex subunits across the proteomics experiment it calculates Complex Fold Change (CFC) factor that characterises the overall protein complex expression trend and the level of subunit co-regulation. Thus up- and down-regulated complexes can be identified. It provides interactive visualisation of protein complexes composition and expression for exploratory analysis. It also incorporates a quality control step that includes normalisation and statistical analysis based on Limma test. ComplexBrowser performance was tested on two previously published proteomics studies identifying changes in protein expression in human adenocarcinoma tissue and during activation of mouse T-cells. The analysis revealed 1519 and 332 protein complexes, of which 233 and 41 were found co-ordinately regulated in the respective studies. The adopted approach provided evidence for a shift to glucose-based metabolism and high proliferation in adenocarcinoma tissues and identification of chromatin remodelling complexes involved in mouse T-cell activation. The results correlate with the original interpretation of the experiments and also provide novel biological details about protein complexes affected. ComplexBrowser is, to our knowledge, the first tool to automate quantitative protein complex analysis for high-throughput studies, providing insights into protein complex regulation within minutes of analysis.\n\nA fully functional demo version of ComplexBrowser v1.0 is available online via http://computproteomics.bmb.sdu.dk/Apps/ComplexBrowser/\n\nThe source code can be downloaded from: https://bitbucket.org/michalakw/complexbrowser\n\nHighlightsO_LIAutomated analysis of protein complexes in proteomics experiments\nC_LIO_LIQuantitative measure of the coordinated changes in protein complex components\nC_LIO_LIInteractive visualisations for exploratory analysis of proteomics results\nC_LI\n\nIn briefComplexBrowser is capable of identifying protein complexes in datasets obtained from large scale quantitative proteomics experiments. It provides, in the form of the CFC factor, a quantitative measure of the coordinated changes in complex components. This facilitates assessing the overall trends in the processes governed by the identified protein complexes providing a new and complementary way of interpreting proteomics experiments.

bioinformatics↗