Biology subjects

Seifert, N.

Publications and source records attributed to Seifert, N..

5 recordsLinked to original sources

bioRxiv2025

TCRanalyzer: A user-friendly tool for comprehensive analysis of T-cell diversity, dynamics and potential antigen targets

T cells are critical for immune responses, recognizing antigens via their unique T-cell receptors (TCRs). Analyzing the diverse TCR repertoires, especially the hypervariable CDR3 region, is essential for understanding immune function in health and disease. Current TCR analysis tools often require specialized expertise, computational resources, or sacrifice biological information for efficiency. To address these limitations, we developed TCRanalyzer, a fast and comprehensive TCR analysis pipeline within a user-friendly graphical interface. TCRanalyzer covers all steps from data loading, aggregation and optional sequence clustering, to the analysis of TCR diversity metrics, clonal expansion and antigen specificity. Applied to datasets from patients with either benign or malignant tumors, TCRanalyzer identified changes in TCR clonality, clonal expansion and shifts in antigen specificity across different cohorts or following immunotherapy, thereby demonstrating its potential to dissect critical immunological processes. TCRanalyzer provides a robust and user-friendly tool for TCR sequence analysis, enhancing research in immunology and related fields. AvailabilityTCRanalyzer is available at https://hub.docker.com/r/tcranalyzer/application. Contactnicole.seifert@bioinf.med.uni-goettingen.de

bioinformatics↗

bioRxiv2025

DynaMiCs - Dynamic cell-type deconvolution ensembles for Mapping in mixed Conditions

Single-cell techniques facilitate the molecular analysis of individual cells, providing insights into cellular diversity, function, and the complexity of biological systems. However, their application is typically limited to small-scale studies involving individual or a few dozen samples, as a consequence of costs and experimental requirement. This complicates the inference of robust conclusions about populations. Bulk transcriptomics offers cost-efficient measurements with low experimental requirements. However, the cellular resolution is lost and only a complex linear combination of signals from multiple cells is observed. Thus, gene expression changes cannot be attributed to individual cells or cell populations. Cell-type deconvolution methods infer cellular compositions from bulk transcriptomics data. State-of-the-art approaches use single-cell data to build molecular reference profiles and identify powerful cell-type markers for improved deconvolution. In this context, we propose Dynamic cell-type deconvolution ensembles for Mapping in mixed Conditions (DynaMiCs) for the integration of single-cell and bulk transcriptomics data. Specifically, DynaMiCs dynamically extracts information from single-cell experiments to (1) provide more accurate estimates of cellular compositions, and (2) establish a mapping between bulk and single-cell data. Consequently, DynaMiCs enables the investigation of how cell populations change in both quantity and molecular characteristics between different phenotypes, informed by single-cell experiments.

bioinformatics↗

bioRxiv2025

HIDE: Hierarchical cell-type Deconvolution

MotivationCell-type deconvolution is a computational approach to infer cellular distributions from bulk transcriptomics data. Several methods have been proposed, each with its own advantages and disadvantages. Reference based approaches make use of archetypic transcriptomic profiles representing individual cell types. Those reference profiles are ideally chosen such that the observed bulks can be reconstructed as a linear combination thereof. This strategy, however, ignores the fact that cellular populations arise through the process of cellular differentiation, which entails the gradual emergence of cell groups with diverse morphological and functional characteristics. ResultsHere, we propose Hierarchical cell-type Deconvolution (HIDE), a cell-type deconvolution approach which incorporates a cell hierarchy for improved performance and interpretability. This is achieved by a hierarchical procedure that preserves estimates of major cell populations while inferring their respective subpopulations. We show in simulation studies that this procedure produces more reliable and more consistent results than other state-of-the-art approaches. Finally, we provide an example application of HIDE to explore breast cancer specimens from TCGA. AvailabilityA python implementation of HIDE is available at zenodo (doi:10.5281/zenodo.14724906). Supplementary informationSupplementary material is available at Bioinformatics online.

bioinformatics↗

bioRxiv2024

Deconvolution of omics data in Python with Deconomix -- cellular compositions, cell-type specific gene regulation, and background contributions

BackgroundGene expression profiles derived from heterogeneous bulk samples contain signals from various cell populations. Cell-type deconvolution approaches are computational tools to reverse engineer the composition of bulks in term of cell populations. Accurate estimates of cell compositions are crucial for identifying cell populations relevant for disease. Moreover, analyses, such as the identification of differentially expressed genes, can be confounded by cellular composition, as differences in gene expression may arise from both variations in cellular composition and gene regulation. ResultsWe present Deconvolution of omics data (Deconomix) - a comprehensive toolbox for the cell-type deconvolution of bulk transcriptomics data, available as a Python package and standalone graphical user interface. Deconomix stands apart from competing solutions with rich functionality and highly efficient implementations. It facilitates (A) the inference of cellular compositions from bulk transcriptomics data, (B) the machine learning-based optimization of gene weights to resolve small cell populations and to disentangle phenotypically related cells, (C) the inference of background contributions which otherwise would deteriorate cell-type deconvolution, and (D) population estimates of cell-type specific gene regulation. To showcase the application of Deconomix, we present a case study on breast cancer data from TCGA, highlighting subtype-specific cellular compositions and cell-type-specific gene-regulatory programs. ConclusionWe present Deconomix, a comprehensive Python package including a graphical user interface for the inference of cellular compositions, cell-type-specific gene regulation, and background contributions from bulk transcriptomics data. O_TEXTBOXKey PointsO_LIDeconomix optimizes gene weights to disentangle small cell populations and phenotypically related cells. C_LIO_LIDeconomix estimates cell compositions, unknown background contributions and cell-type specific gene regulation from bulk transcriptomics data. C_LIO_LIA Python package is available as code repository and installable via PyPI [1]. C_LIO_LIA standalone graphical user interface is available as code repository [2]. C_LIO_LIAn exemplary analysis of a breast cancer case study is provided as tutorial [3]. C_LI C_TEXTBOX

bioinformatics↗

bioRxiv2023

PriOmics: integration of high-throughput proteomic data with complementary omics layers using mixed graphical modeling with group priors

Mass spectrometry (MS)-based high-throughput proteomics data cover abundances of 1,000s of proteins and facilitate the study of co- and post-translational modifications (CTMs/PTMs) such as acetylation, ubiquitination, and phosphorylation. Yet, it remains an open question how to holistically explore such data and their relationship to complementary omics layers or phenotypical information. Network inference methods aim for a holistic analysis of data to reveal relationships between molecular variables and to resolve underlying regulatory mechanisms. Among those, graphical models have received increased attention as they can distinguish direct from indirect relationships, aside from their generalizability to diverse data types. We propose PriOmics as a graphical modeling approach to integrate proteomics data with complementary omics layers and pheno- and genotypical information. PriOmics models intensities of individual peptides and incorporates their protein affiliation as prior knowledge in order to resolve statistical relationships between proteins and CTMs/PTMs. We show in simulation studies that PriOmics improves the recovery of statistical associations compared to the state of the art and demonstrate that it can disentangle regulatory effects of protein modifications from those of respective protein abundances. These findings are substantiated in a dataset of Diffuse Large B-Cell Lymphomas (DLBCLs) where we integrate SWATH-MS-based proteomics data with transcriptomic and phenotypic information. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/566517v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@19bcac0org.highwire.dtl.DTLVardef@11c126forg.highwire.dtl.DTLVardef@1fe6631org.highwire.dtl.DTLVardef@e73187_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗