bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/663765

NoRCE: Non-coding RNA Sets Cis Enrichment Tool

Olgun, G.·Nabi, A.·Tastan, O.
View original source ↗Export metadata ↓

Abstract

SummaryWhile some non-coding RNAs (ncRNAs) are assigned to critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic spatial proximity can lead to a functional association. Based on this idea, we present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. NoRCE allows incorporating other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information. NoRCE repository provides several data, such as cell-line specific TAD boundaries, functional gene sets, and expression data for coding and ncRNAs specific to cancer for the analysis. Additionally, users can utilize their custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast. NoRCE is a platform-independent, user-friendly, comprehensive R package that could be used to gain insight into the functional importance of a list of any type of interesting ncRNAs. Users can run the pipeline in a single function; also, the tool offers flexibility to conduct the users preferred analysis in a single base and design their pipeline. It is available in Bioconductor and https://github.com/guldenolgun/NoRCE.

Source connections

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Olgun, G., Nabi, A., Tastan, O.. 2019-06-07. NoRCE: Non-coding RNA Sets Cis Enrichment Tool. https://doi.org/10.1101/663765

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Classifying tumors from broad category to hierarchical molecular subtype: an interpretable hierarchical framework for precision oncology

Precise tumor subtype classification is important for guiding personalized treatment, characterizing tumor heterogeneity, and investigating molecular mechanisms. However, most existing multi-label classification models use flat architectures and do not fully exploit the inherent hierarchical relationships and biological constraints among tumor types, which limits their performance and interpretability in complex, fine-grained pan-cancer classification. Here, we present the cancer Hallmark-based Hierarchical Recursive Model (HHRM) for pan-cancer hierarchical multi-label classification. HHRM integrates hierarchical label structures with recursive neural networks, enabling simultaneous modeling of multi-level dependencies from broad cancer categories to specific subtypes. We trained the model on a comprehensive pan-cancer cohort of 11,476 samples spanning 26 primary cancer types and 405 subtypes across up to eight hierarchical levels. HHRM achieved an accuracy of 0.7924 for predicting hierarchical tumor subtypes and was further evaluated on a validation dataset of 887 samples. Compared with other hierarchical modeling frameworks, HHRM achieved higher accuracy in all three output modes. Ablation experiments showed that replacing the hallmark-based graph convolutional network reduced model performance. On an independent dataset of 1,608 breast cancer samples, HHRM also achieved an accuracy greater than 0.86. Finally, we combined SHAP with a network-based method to rank and analyze predictive genes at different levels of the tumor hierarchy, which identified both known and potentially novel biomarkers for cancer diagnosis. In conclusion, to our knowledge, HHRM is the first hierarchical model for multi-level tumor subtype classification. Its competitive performance, combined with post hoc biological interpretation, suggests potential value for precision cancer diagnosis.

bioinformatics↗
bioRxiv2026

A comparative transcriptomic framework across kidney organoid, assembloid, and transplantation models

Human kidney organoids recapitulate key aspects of renal development and have emerged as valuable platforms for disease modeling, nephrotoxicity screening, and therapeutic development. The field now includes a wide range of emerging protocols with distinct cellular compositions and maturation states, which complicates systematic comparison and model selection. Here, we integrated single-cell and single-nucleus transcriptomes from 11 studies spanning kidney organoid and assembloids, both in vitro and following transplantation, benchmarked against fetal and adult kidney references. Transcriptional maturation toward adult identity is compartment specific and does not correlate with protocol complexity. Assembloid protocols show the most adult-like podocyte and distal nephron signatures, transplantation is associated with enhanced tubular oxidative metabolic features, only protocols incorporating ureteric progenitors reliably generate collecting-duct lineages, and no protocol achieves mature adult proximal tubule identity. Annotating against either fetal or adult kidney reference alone leads to misassignment, because early fetal nephron epithelium transcriptionally resembles injury-dedifferentiated adult proximal tubule cells. Assembloid and transplantation models share elevated mechanosensing pathway expression independently of vascularization. Together, these findings provide a framework for kidney organoid model selection.

bioinformatics↗
bioRxiv2026

OncoSpot enables benchmarking of cancer spatial transcriptomics methods across tumor evolutionary modes

Spatial transcriptomics (ST) is increasingly used to map clonal architecture within tumors, yet methods for analyzing cancer ST data are difficult to evaluate because real tumors lack ground truth for copy-number alterations (CNAs), subclonal identity, and malignant cell fraction. Existing ST simulators model normal tissue and do not represent tumor evolution, CNA-driven expression, or the spatial organization of malignant subclones. We present OncoSpot, a cancer-aware simulator of Visium-like spot-based ST data. OncoSpot combines a spatial evolutionary scaffold from DEMON, CNAs generated along the resulting clonal phylogeny with CNAsim, cancer-type-specific malignant transcriptional programs, and spatially clustered nonmalignant cell types, and aggregates cells into spots with known malignant fraction, subclone, and copy-number profiles. We demonstrate OncoSpot on high-grade serous ovarian carcinoma, colorectal cancer, and hepatocellular carcinoma, which represent three distinct modes of tumor evolution. Simulated subclones were spatially clustered and showed stronger phylogeny-space concordance than those generated by scCube under matched subclone populations. Benchmarking CopyKAT, CancerFinder, Clonalscope, and RCTD revealed cancer-dependent performance: Clonalscope recovered copy-number signals more accurately in ovarian and colorectal cancer, whereas CopyKAT performed better in hepatocellular carcinoma, reflecting both the assumptions of each method and the copy-number landscapes of the three cancers. OncoSpot's ground truth also exposed a failure mode in Clonalscope's malignant spot identification, in which tumor and normal labels were inverted, and allowed us to trace its cause. These method-dependent behaviors would be difficult to detect in real data. OncoSpot provides a framework for benchmarking CNA inference, subclone detection, malignant spot identification, and normal spot deconvolution, and its modular design and guidelines allow extension to additional cancer types. OncoSpot is freely available at https://github.com/compbio-mallory/OncoSpot.

bioinformatics↗