Biology subjects

Schulz, M. H.

Publications and source records attributed to Schulz, M. H..

At least 19 recordsLinked to original sources

bioRxiv2026

AtlasLens: Metadata-centric exploration and analysis of single-cell atlases

MotivationThe rapid expansion of single-cell RNA sequencing (scRNA-seq) atlases has generated datasets comprising millions of cells annotated with increasingly rich metadata, including tissue, cell type, disease status, sex, age, treatment, and temporal information. Biological questions frequently require simultaneous interrogation of multiple metadata dimensions, such as identifying specific cell populations within defined tissues, disease states, demographic groups, and time points. While existing interactive platforms facilitate visualization and analysis of scRNA-seq data, deep metadata-driven exploration and downstream analysis of atlas-scale datasets remain insufficiently supported. ResultsWe developed AtlasLens, an open-source R/Shiny application for interactive exploration of scRNA-seq datasets and integrated cellular atlases. AtlasLens enables iterative filtering across arbitrary metadata combinations, allowing users to define biologically meaningful cellular subsets and immediately perform downstream analyses. The platform integrates interactive visualization, differential expression analysis, Gene Ontology enrichment with redundancy reduction, temporal expression analysis, and context-dependent gene function profiling through GeneCOCOA. AtlasLens additionally records analysis history and automatically generates corresponding R code to enhance reproducibility. The application is distributed through Docker for simple local deployment, preserving data privacy and eliminating dependency-management challenges. We demonstrate AtlasLens using the Tabula Muris and a time-resolved whole-lung single-cell atlas of bleomycin-induced lung injury and fibrosis, highlighting its ability to support complex metadata-driven biological investigations. AvailabilitySource code is available at https://github.com/SchulzLab/AtlasLens. Contactmarcel.schulz@em.uni-frankfurt.de

bioinformatics↗

bioRxiv2026

Computing tumor specificity of cancer antigen targets by k-mer indexing of healthy tissue transcriptomes

Individualized cancer immunotherapies rely on tumor-specific T-cell antigens, often predicted from somatic mutations as neoantigens. For tumors with low mutational burden, mRNA transcript variants, including gene fusions and novel splice junctions, can serve as important alternative targets. A main challenge in their identification from tumor RNA-seq is to confirm that their expression is tumor-restricted. Although large public collections of healthy-tissue RNA-seq exist, verifying tumor-specific expression requires computationally expensive re-analysis of these data for every novel candidate. To address this, we benchmarked nine k-mer indexing algorithms and developed k4neo, which leverages k-mer indexing of raw RNA-seq reads to compute the tumor specificity of any transcript variant. This mapping-free and transcript-class agnostic approach screens any candidate sequence against 18,960 samples across 51 healthy tissue types. We confirmed k4neos detection accuracy with qRT-PCR and showed that k4neo accurately classifies somatic and germline variants, gene fusions, and isoforms by tumor specificity. Applied to nine tumor cohorts, it nominated a median of 4-80 tumor-specific splice junctions per patient, including recurrent, long-read-confirmed novel antigen candidates. Together, k4neo enables efficient access to large-scale sequencing cohorts and accurately computes tumor specificity for any input transcript sequence, thereby expanding the repertoire of individual and shared cancer antigen targets.

bioinformatics↗

bioRxiv2026

RANKOR: Direct Drug Prioritization from Bulk and Single-Cell Transcriptomic Signatures

BackgroundPrioritizing therapeutics from transcriptomic data remains a key challenge in precision medicine. Signature reversal approaches, most commonly implemented through Gene Set Enrichment Analysis (GSEA), have been widely used to match disease signatures to candidate drugs. However, enrichment-based methods can be sensitive to noise and are restricted to previously profiled compounds MethodsWe developed RANKOR, a machine-learning framework designed to rank candidate drugs directly from transcriptomic signatures. Rather than predicting full expression profiles, RANKOR learns structured latent representations of transcriptional responses alongside chemical structure, enabling prioritization from standardized signatures derived from disease states or treatment perturbations. The framework is applicable to both bulk and single-cell transcriptomic data. ResultsAcross large-scale perturbational datasets, RANKOR achieved consistently lower median ranks than similarity- and distance-based approaches, while showing performance comparable to, and in some settings improved over, GSEA. The model generalized across unseen cell types and retained performance in single-cell settings, where it provided more consistent prioritization than existing approaches, such as ASGARD. RANKOR further enabled prioritization of transcriptionally unseen compounds through chemical-space embedding and achieved substantially reduced computation times. Robustness analyses demonstrated stable performance under moderate noise and degradation under extreme perturbation or gene shuffling. Gene attribution analyses indicated that prioritization decisions are driven by coherent and mechanism-relevant transcriptional programs. ConclusionsRANKOR provides a scalable framework for transcriptomics-guided drug prioritization that can complement and extend existing approaches, such as GSEA. It can also support therapeutic hypothesis generation from bulk and single-cell data while leveraging the generalisability and computational efficiency of machine learning models.

bioinformatics↗

bioRxiv2026

Identification of Parkinson's disease-associated regulatory variants in human dopaminergic neurons reveals modulators of SCARB2 and BAG3 expression

A hallmark of Parkinsons disease (PD) is the degeneration of midbrain dopaminergic neurons (mDANs). Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with PD, but causal variants and mechanisms remain unknown. Many PD-associated SNPs reside in regulatory regions, where they may disrupt transcription factor binding sites (TFBS) and alter gene expression. To assess how non-coding PD SNPs affect gene regulation in mDANs, we identify variants predicted to alter TF binding and functionally validate their effects in a cell type-specific context. We integrate time-series transcriptome and chromatin accessibility data from iPSC-derived neurons with chromatin topology and genetic variants. We profile 3D chromatin conformation in neuronal progenitors (smNPCs) and mDANs using LowC, identifying changes in A/B compartments and topologically associated domains. PD SNPs are enriched near genes expressed in mDANs, and we predict 254 regulatory variants that create or disrupt TFBS. Using chromatin conformation data, we link variants to target genes. At the BAG3 and SCARB2 loci, reporter assays in mDANs show reduced transcription driven by PD-associated alleles. Knock-down of NR2C2, a putative SCARB2 regulator, increases SCARB2 expression in differentiating neurons. The PD-associated SCARB2 allele shows reduced chromatin accessibility in mDANs and is associated with decreased expression in brain eQTL data. Insertion of PD-associated BAG3 allele by prime editing reduces chromatin accessibility across cell types, consistent with altered binding of LIM-homeodomain transcription factors. Together, these results prioritize functional PD SNPs and show that variants at SCARB2 and BAG3 modulate gene expression in mDANs, providing mechanistic insight into PD.

molecular biology↗

bioRxiv2026

miRXplain: explainable isomiR-aware microRNA target prediction using CLIP-L experiments and hybrid attention transformers

MicroRNAs (miRNAs) are ~22 nt long noncoding RNAs that repress genes by base-pairing with complementary sequences in target mRNAs. Variants known as isomiRs arise from alternative hairpin processing and often shift the canonical seed region, thereby altering their target repertoire. Yet, the mRNA features that enable differential miRNA and isomiR target selection remain poorly understood, largely due to limited high-throughput assays that capture the exact miRNA bound to each target. Existing deep learning methods for miRNA-target prediction have neither leveraged such datasets nor investigated isomiR-specific interactions. To fill this gap, we developed miRXplain, an isomiR-aware transformer that predicts miRNA-mRNA interactions using miRNA and target sequences derived from CLIP-L data, directly linking precise miRNA variants to their target sites. These data however revealed a 5'-end nucleotide bias in target sites, which we corrected to generate high-quality, miRNA-target pairs for training, preserving isomiR-specific signal. miRXplain outperformed all benchmarked models, surpassing TEC-miTarget, in auROC and auPRC with 15x fewer parameters. Attention maps highlighted distinct sequence determinants for canonical versus isomiR interactions, and in silico saturation mutagenesis highlighted the importance of seed and 3' supplementary regions. miRXplain effectively prioritizes pathogenic single-nucleotide variants affecting mRNA-miRNA interactions and reveals isomiR targeting principles that deepen our understanding of miRNA biology.

bioinformatics↗

bioRxiv2026

Hierarchical Representation Learning for Drug Mechanism-of-Action Prediction from Gene Expression Data

Deciphering drug mechanisms of action (MoAs) from transcriptional responses is key for discovery and repurposing. While recent machine learning approaches improve prediction accuracy beyond traditional similarity metrics, they often lack biological structure and interpretability in the learned space. We introduce a hierarchical representation learning framework that explicitly enforces mechanistically coherent organization using dual ArcFace objectives, yielding an interpretable latent space that captures both MoA-level separation and compound-level substructure. Gene importance and pathway enrichment analyses confirm that the learned representations recover established signaling programs. Trained on LINCS L1000 data, the model also improves F1 performance over state-of-the-art baselines and generalizes to unseen compounds and cell types. Additionally, the latent space generalizes to CRISPR knockdowns without the need for retraining, indicating it captures pathway-level perturbations independently of modality.

bioinformatics↗

bioRxiv2025

Ischemic stroke induces persistent alteration to brain stromal progenitor cells linked to chronic vascular dysfunction

Fibrotic scar formation after stroke serves a dual role: while essential for providing structural support during post-ischemic recovery, excessive fibrosis in the chronic phase of stroke impairs regenerative processes including axonal regrowth and neovascularization. The temporal dynamics of fibrosis are critical determinants of functional outcomes, as the balance between protective scarring and regenerative capacity differs across distinct stroke phases. Consequently, strategic modulation of fibrotic processes to preserve regenerative potential represents a promising therapeutic approach in stroke recovery. To understand the cellular mechanisms underlying this fibrotic response, we investigated stromal progenitor cell composition in the post-stroke brain. The vast majority of stromal progenitor cells (SPCs) are pericytes, with minorities comprising perivascular fibroblasts (PVFs) and vascular smooth muscle cells. We demonstrate that ischemic stroke drives a long-term shift in this composition, characterized by sustained expansion of the PVF population and excessive laminin deposition in the peri-infarct region, effects that persist for at least six months post-stroke. Single-cell RNA sequencing revealed sustained transcriptional and compositional alterations in the SPC population throughout chronic post-stroke phase, driven by AP-1-mediated signaling via TNF in both PVFs and pericytes. These changes correlate with long-term vasomotor dysfunction and capillary constriction in the peri-infarct region at six weeks post-stroke. Ischemic stroke drives aberrant, persistent PVF accumulation at the capillary bed with implications for post-stroke cerebrovascular dysfunction and recurrent stroke. Taken together, these findings reveal that ischemic stroke drives an aberrant long-term mis-localization of PVFs to the capillary bed that may have clinically-relevant implications for post-stroke cerebrovascular function as well as potential ramifications for recurrent stroke.

neuroscience↗

bioRxiv2025

Haematopoietic loss of KDM6A impairs cardiac recovery in heartfailure via epigenetic reprogramming of myeloid cells

Clonal haematopoiesis (CH) is recognized as a potent independent risk factor for cardiovascular disease (CVD). While mutations in common CH-associated genes, such as DNMT3A and TET2, have been extensively studied, the pathological roles of other CH mutations remain poorly understood. Among these is KDM6A (UTX), an X-linked histone demethylase recently found to be commonly mutated in patients with heart failure. The mechanistic implications of KDM6A mutations in cardiac dysfunction remain largely unknown. Here, using multi-omics profiling and functional characterisation of murine models and patient-derived data, we demonstrate that haematopoietic loss of KDM6A substantially impairs cardiac recovery following myocardial infarction (MI). KDM6A deficiency enhances systemic and cardiac inflammation, characterized by augmented myeloid cell infiltration into the infarcted murine heart. Single-cell chromatin accessibility and single-cell RNA sequencing analyses revealed profound epigenetic and transcriptional reprogramming in KDM6A-deficient myeloid cells, notably CCR2 recruited macrophages and neutrophils. These cells exhibited heightened inflammatory (Il1b, Nlpr3, Saa3) and chemotactic signatures (Ccr2, Mif, Cxcl12), increased activation of inflammatory transcription factor networks (AP-1, C/EBP), disrupted chromatin architecture, and enhanced glycolytic activity. Clinically, patients with heart failure harbouring KDM6A-driven CH exhibited increased pro-inflammatory monocyte signatures (CCR2, NLPR3, NFKB1, FOS, JUN, IL6R, IL32), underscoring the translational relevance. Integrative analyses further predicted pathogenic crosstalk between KDM6A-mutated monocytes and cardiac resident cells and was experimentally validated by demonstrating that KDM6A-silenced macrophages drive cardiomyocyte hypertrophy and cardiac fibroblast activation. Our findings establish a critical mechanistic link between KDM6A-driven CH, immune dysregulation, and worsened cardiac outcomes post-MI, highlighting novel avenues for personalized therapeutic strategies in heart failure.

cell biology↗

bioRxiv2025

Predicting gene-specific regulation with transcriptomic andepigenetic single-cell data

MotivationAnalysis of single cell ATAC-seq and RNA-seq data has allowed to gain unprecedented insights into gene regulation by allowing to define cell type specific regulatory regions and their effects on gene expression. While powerful, such analysis is challenging due to the inherent sparsity of single cell data. ResultsWe present the MetaFR approach to learn gene-specific models that link open-chromatin variation from scATAC-seq data to gene expression from scRNA-seq. Using efficient regression trees, we illustrate that accurate expression prediction models can be learned on the single-cell or meta-cell level. Validation was done using fine-mapped eQTLs. Meta-cell models were found to outperform single-cell models for most genes. Comparison to the SOTA method SCARlink revealed advantages of MetaFR in terms of runtime and prediction performance. MetaFR thus allows time-efficient analysis and obtains reliable models of gene expression prediction, which can be used to study gene regulation in any organism for which scRNA-seq and scATAC-seq data is available. Availability and implementationMetaFR is freely available under https://github.com/SchulzLab/MetaFR.

bioinformatics↗

bioRxiv2025

Harnessing machine learning models for epigenome to transcriptome association studies

Understanding how epigenome variation contributes to gene expression in disease and development is a fundamental challenge. Regulatory regions show cell type-specific epigenome activity and differ in their location, size, and distance to their target genes, complicating discovery and analysis. Recent machine learning models have been proposed to address these problems by learning functions for the prediction of gene expression from epigenomic data. Here, we use the large IHEC EpiATLAS dataset to benchmark state-of-the-art linear and non-linear approaches. Each approach is optimized for over 28,000 human genes, providing a comprehensive regulatory catalog of gene models. In-depth comparison reveals that gene characteristics and the epigenomic complexity of the locus influence the difficulty of predicting the epigenome-to-transcriptome association. The model performance is further evaluated using CRISPRi and eQTL validation data. Based on these models, we conduct histone-acetylation association studies in a systematic way to investigate how epigenomic variation impacts gene expression. The model-based analysis revealed genes and regulatory regions linked to B-cell leukemia in patient data with known disease-related functions. Our work provides a foundation for applications that link epigenome variation to gene expression in human cells, by benchmarking methods on a per-gene basis, illustrating their use in a disease context and making trained models available to the community.

bioinformatics↗

bioRxiv2025

Vascular niches are the primary hotspots for aging within the multicellular architecture of cardiac tissue

BackgroundAging is a major, yet unmodifiable risk factor for cardiovascular diseases, leading to vascular alterations, increased cardiac fibrosis, and inflammation, all of which contribute to impaired cardiac function. However, the microenvironment inciting age-related alterations withing the multicellular architecture of the cardiac tissue is unknown. MethodsWe investigated local microenvironments in aged mice hearts applying an integrative approach combining single-nucleus RNA sequencing and spatial transcriptomics in 12-week-old and 18-month-old mice. We defined distinct cardiac niches and studied changes in their cellular composition and functional characteristics. ResultsIntegration of spatial transcriptomics data across young and aged hearts allowed us to identify 11 cardiac niches, which were characterized by distinct cellular composition and functional signatures. Aging did not alter the overall proportions of cardiac niches but leads to distinct regional changes, particularly in the left ventricle. Whereas cardiomyocyte-enriched niches show disrupted circadian clock gene expression, vascular niches showed major changes in pro-inflammatory and pro-fibrotic signatures and altered cellular composition. We particularly identified larger vessel-associated cellular niches as key hotspots for activated fibroblasts and macrophages in aged hearts, with interactions of both cell types through the C3:C3ar1 axis. These niches were also enriched in senescence cells exhibiting high expression of immune evasion mechanisms that may impair senescent cell clearance. ConclusionOur findings indicate that the microenvironment around the vasculature is particularly susceptible to age-related changes and serves as a primary site for inflammation-driven aging, so called "inflammaging". This study provides new insights into how aging reshapes cardiac cellular architecture, highlighting vessel-associated niches as potential therapeutic targets for age-related cardiac dysfunction.

physiology↗

bioRxiv2025

Decoding heart failure subtypes with neural networks via differential explanation analysis

Single-cell transcriptomics offers critical insights into the molecular mechanisms of heart failure with reduced or preserved ejection fraction. However, understanding these mechanisms is hindered by the growing complexity of single-cell data and the difficulty in unmasking meaningful differential genes signatures among heart failure types. Machine learning, particularly deep neural networks, address these challenges by learning transcriptional patterns, reconstructing expression profiles and effectively classifying cells but often lacks interpretability. Recent advances in explainable AI (XAI) offer tools to clarify model decisions. Yet pinpointing differentially regulated genes with these tools remains challenging. In this study, we introduce a novel method to identify differentially explained genes (DXGs) based on importance scores derived from custom-built neural networks. We highlight the superiority of DXGs in identifying heart failure subtypes-specific pathways that provide new insights into different types of heart failure. Offering a robust foundation for future research and therapeutic exploration in expanding transcriptome atlases.

bioinformatics↗

bioRxiv2025

Cell type-specific epigenetic regulatory circuitry of coronary artery disease loci

Coronary artery disease (CAD) is the leading cause of death worldwide. Recently, hundreds of genomic loci have been shown to increase CAD risk, however, the molecular mechanisms underlying signals from CAD risk loci remain largely unclear. We sought to pinpoint the candidate causal coding and non-coding genes of CAD risk loci in a cell type-specific fashion. We integrated the latest statistics of CAD genetics from over one million individuals with epigenetic data from 45 relevant cell types to identify genes whose regulation is affected by CAD-associated single nucleotide variants (SNVs) via epigenetic mechanisms. Applying two statistical approaches, we identified 1,580 genes likely involved in CAD, about half of which have not been associated with the disease so far. Enrichment analysis and phenome-wide association studies linked the novel candidate genes to disease-specific pathways and CAD risk factors, corroborating their disease relevance. We showed that CAD-SNVs are enriched to regulate gene expression by affecting the binding of transcription factors (TFs) with cellular specificity. Of all the candidate genes, 23.5% represented non-coding RNAs (ncRNA), which likewise showed strong cell type specificity. We conducted a proof-of-concept biological validation for the novel CAD ncRNA gene IQCH-AS1. CRISPR/Cas9-based gene knockout of IQCH-AS1, in a human preadipocyte strain, resulted in reduced preadipocyte proliferation, less adipocyte lipid accumulation, and atherogenic cytokine profile. The cellular data are in line with the reduction of IQCH-AS1 in adipose tissues of CAD patients and the negative impact of risk alleles on its expression, suggesting IQCH-AS1 to be protective for CAD. Our study not only pinpoints CAD candidate genes in a cell type-specific fashion but also spotlights the roles of the understudied ncRNA genes in CAD genetics.

bioinformatics↗

bioRxiv2024

Detection of statistically robust interactions from diverse RNA-DNA ligation data

Chromatin-localized RNAs play diverse roles in gene regulation and nuclear architecture. Mapping genome-wide RNA-DNA interactions is possible using a variety of molecular methods, including using bridging oligonucleotides to ligate RNA and DNA in proximity. While molecular methods have progressed, a robust computational method for calling biologically meaningful RNA-DNA interactions from these data is lacking. Herein, we present RADIAnT, a reads-to-interactions pipeline for analyzing RNA-DNA ligation data. RADIAnT calls interactions against a dataset-specific, unified background which considers RNA binding site-TSS distance and genomic region bias. By scaling the background by RNA abundance, RADIAnT is sensitive enough to detect specific interactions of lowly expressed transcripts, while remaining specific enough to discount false positive interactions of highly abundant RNAs. RADIAnT outperforms previously proposed methods in the accurate recall of genome-wide Malat1-DNA interactions, and in a use case, was utilized to identify dynamic chromatin-associated RNAs in the physiologically- and pathologically-relevant process of endothelial-to-mesenchymal transition.

bioinformatics↗

bioRxiv2024

Metabolic alterations drive inflammatory phenotypes in CHIP-associated heart failure

Mutations in DNA methyltransferase 3 alpha (DNMT3A) are the most frequent driver of clonal hematopoiesis of indeterminate potential (CHIP), and associated with higher risk of cardiovascular disease and pro-inflammatory activation of immune cells. Here, we investigated the mechanisms underlying DNMT3A CHIP-associated inflammatory phenotypes in macrophages. We show that monocytes of DNMT3A CHIP-driver mutation carriers are associated with DNA hypomethylation of succinate dehydrogenase A (SDHA) and an altered tricarboxylic acid cycle metabolite profile. Silencing of DNMT3A in monocytes increased SDHA and elevated mitochondria complex II activity. The secreted complex II product, malate, further increased inflammatory activation in wild type monocytes to further augment inflammation in a paracrine manner. Pharmacological inhibition of SDHA (using dimethyl malonate) in mice harboring DNMT3A mutations in hematopoietic stem cells ameliorated the inflammatory response and improved cardiac function after myocardial infarction. Thus, interfering with the altered metabolic state may provide a new therapeutic option to dampen inflammatory activation in DNMT3A CHIP carrying patients.

molecular biology↗

bioRxiv2024

Epigenetic control of metabolic identity across cell types

BackgroundConstraint-based network modeling is a powerful genomic-scale approach for analyzing cellular metabolism, capturing metabolic variations across tissues and cell types, and defining the metabolic identity essential for identifying disease-associated transcriptional states. ResultsUsing RNA-seq and epigenomic data from the EpiATLAS resource of the International Human Epigenome Consortium (IHEC), we reconstructed metabolic networks for 1,555 samples spanning 58 tissues and cell types. Analysis of these networks revealed the distribution of metabolic functionalities across human cell types and provides a compendium of human metabolic activity. This integrative approach allowed us to define, across tissues and cell types, i) reactions that fulfil the basic metabolic processes (core metabolism), and ii) cell type-specific functions (unique metabolism), that shape the metabolic identity of a cell or a tissue. Integration with EpiATLAS-derived cell-type-specific gene-level chromatin states and enhancer-gene interactions identified enhancers, transcription factors, and key nodes controlling core and unique metabolism. Transport and first reactions of pathways were enriched for high expression, active chromatin state, and Polycomb-mediated repression in cell types where pathways are inactive, suggesting that key nodes are targets of repression. DiscussionThis integrative analysis forms the basis for identifying regulation points that control metabolic identity in human cells.

systems biology↗

bioRxiv2024

GeneCOCOA: Detecting context-specific functions of individual genes using co-expression data

Extraction of meaningful biological insight from gene expression profiling often focuses on the identification of statistically enriched terms or pathways. These methods typically use gene sets as input data, and subsequently return overrepresented terms along with associated statistics describing their enrichment. This approach does not cater to analyses focused on a single gene-of-interest, particularly when the gene lacks prior functional characterization. To address this, we formulated GeneCOCOA, a method which utilizes context-specific gene co-expression and curated functional gene sets, but focuses on a user-supplied gene-of-interest. The co-expression between the gene-of-interest and subsets of genes from functional groups (e.g. pathways, GO terms) is derived using linear regression, and resulting root-mean-square error values are compared against background values obtained from randomly selected genes. The resulting p values provide a statistical ranking of functional gene sets from any collection, along with their associated terms, based on their co-expression with the gene of interest in a manner specific to the context and experiment. GeneCOCOA thereby provides biological insight into both gene function, and putative regulatory mechanisms by which the expression of the gene-of-interest is controlled. Despite its relative simplicity, GeneCOCOA outperforms similar methods in the accurate recall of known gene-disease associations. GeneCOCOA is formulated as an R package for ease-of-use, available at https://github.com/si-ze/geneCOCOA. Author summaryUnderstanding the biological functions of different genes and their respective products is a key element of modern biological research. While one can examine the relative abundance of a gene product in transcriptomics data, this alone does not provide any clue to the biological relevance of the gene. Using a type of analysis called co-expression, it is possible to identify other genes which have similar patterns of regulation to a gene-of-interest, but again, this cannot tell you what a gene does. Genes whose function has previously been studied are often assembled into groups (e.g. pathways, ontologies), which can be used to annotate gene sets of interest. However, if a gene has not yet been characterized, it will not appear in these gene set enrichment analyses. Here, we propose a new method - GeneCOCOA - which uses co-expression of a single gene with genes in functional groups to identify which functional group a gene is most similar too, resulting in a putative function for the gene, even if it has not been studied before. We tested GeneCOCOA by using it to find gene-disease links which have already been scientifically studied, and showed that GeneCOCOA can do this more effectively than other available methods.

bioinformatics↗

bioRxiv2023

Improved integration of single cell transcriptome data demonstrated on heart failure in mice and men

Biomedical research frequently uses murine models to study disease mechanisms. However, the translation of these findings to human disease remains a significant challenge. In order to improve the comparability of mouse and human data, we present a cross-species integration pipeline for single-cell transcriptomic assays. The pipeline merges expression matrices and assigns clear orthologous relationships. Starting from Ensembl ortholog assignments, we allocated 82% of mouse genes to unique orthologs by using additional publicly available resources such as Uniprot, and NCBI databases. For genes with multiple matches, we employed the Needleman-Wunsch global alignment based on either amino acid or nucleotide sequence to identify the ortholog with the highest degree of similarity. The workflow was tested for its functionality and efficiency by integrating scRNA-seq datasets from heart failure patients with the corresponding mouse model. We were able to assign unique human orthologs to up to 80% of the mouse genes, utilizing the known 17,492 orthologous pairs. Curiously, the integration process enabled the identification of both common and unique regulatory pathways between species in heart failure. In conclusion, our pipeline streamlines the integration process, enhances gene nomenclature alignment and simplifies the translation of mouse models to human disease. We have made the OrthoIntegrate R-package accessible on GitHub (https://github.com/MarianoRuzJurado/OrthoIntegrate), which includes the assignment of ortholog definitions for human and mouse, as well as the pipeline for integrating single cells. KeypointsO_LINovel integration workflow for scRNA-seq data from different species in an easy to use R-package ("OrthoIntegrate"). C_LIO_LIImproved one-to-one ortholog assignment via sequence similarity scores and string similarity calculations. C_LIO_LIValidation of "OrthoIntegrate" results with a case study of snRNA-seq from human heart failure with reduced ejection fraction and its corresponding mouse model C_LI

bioinformatics↗