Biology subjects

Ascension, A. M.

Publications and source records attributed to Ascension, A. M..

10 recordsLinked to original sources

bioRxiv2026

Extrachromosomal circular DNA formation follows a 10.5-bp periodicity reflecting the minor groove orientation around nucleosomes

Extrachromosomal circular DNA is increasingly recognized as a central feature of somatic evolution in cancer, yet the structural determinants of eccDNA formation remain poorly understood. Here, we systematically analysed eccDNA length distributions and breakpoint organization across 25 datasets spanning multiple species, tissues and experimental approaches. Applying a short-time Fourier transform to eccDNA length distributions enabled us to resolve recurrent periodicities at approximately 180 bp and 10.5 bp, consistent with nucleosome length and the helical repeat of DNA, respectively. Mapping eccDNA breakpoints relative to nucleosome dyads revealed preferential breakpoint formation in linker DNA and, within nucleosome-bound DNA, at positions where the minor groove faces towards the histone surface. Our results show that eccDNA formation is shaped not only by nucleosome positioning but also by the rotational orientation of the DNA double helix around the nucleosome. Together, these findings identify chromatin geometry as a determinant of eccDNA biogenesis and provide a structural basis for the length periodicities that characterize eccDNA formation.

genomics↗

bioRxiv2026

Circular RNA-associated QTLs show stronger association with splicing-QTLs than with expression-QTLs

IntroductionCircular RNA quantitative trait locus (circQTLs) have emerged as a class of regulatory variants, but their mechanistic basis remains poorly characterized. Understanding how genetic variation influences circRNA biogenesis is essential to clarify their role in post-transcriptional gene regulation. MethodsWe systematically compared circQTLs with matched splicing (sQTL) and expression (eQTL) datasets. Using bootstrap-based Jaccard similarity analyses, we quantified genomic overlap patterns and assessed their statistical significance. We further validated these findings across independent circQTL studies. In addition, we analyzed the genomic distribution of circQTLs to identify enrichment patterns across functional genomic regions. ResultscircQTLs exhibited a statistically significant but modestly stronger genomic overlap with sQTLs compared to eQTLs. This pattern was consistent across independent datasets despite limited reproducibility of individual circQTL signals. Genomic annotation revealed distinct distributional patterns, including depletion in exonic regions and relative enrichment in non-coding genomic contexts compared to other QTL classes. DiscussionTogether, these results suggest that circRNA-associated regulatory variation is preferentially linked to splicing-related mechanisms rather than transcriptional control of host genes. However, the modest effect size indicates that this relationship is not exclusive, and likely reflects a mixture of shared splice-site regulatory effects and additional mechanisms specific to back-splicing that are not captured by conventional sQTL or eQTL frameworks. This dual architecture positions circRNA biogenesis at the interface between splicing dynamics, RNA structure, and higher-order genomic organization, supporting circQTLs as a distinct layer of post-transcriptional gene regulation.

genetics↗

bioRxiv2025

Unveiling the neuro-vascular interplay in the skeletal muscle in health, injury and disease

Neuromuscular junctions (NMJs) are complex multicellular structures that convey motor neuron-induced responses in the skeletal muscle. Their cellular composition is well characterized, but interactions between the different cell types and with the surrounding microenvironment remain underexplored. Here, by using a panel of newly discovered mouse cell lineage markers, single-cell RNA sequencing analyses and iDisco tissue clarification, we demonstrate the existence of contacts between adjacent NMJs through their kranocytes, assembling kranocyte network-like structures within the muscle interstitium, which is essential for regulating blood flow and supporting muscle regeneration. Indeed, kranocytes do contact with the surrounding microvasculature as well, both of them uncovering a previously unexplored, expansive interactive system. Moreover, unlike the robustness shown by their counterparts, the terminal Schwann cells, we strikingly observed that kranocytes rapidly detected stressful conditions, leading to structural changes and even the loss of their connections in response to acute damage, such as muscle injury or neuromuscular disease-induced denervation. Therefore, we propose kranocytes as the interactive sensory platforms of the NMJs, working in a perpendicular axis of nerve-transmission, as key NMJ-connectors by, interconnecting adjacent NMJs and interacting with the microvasculature and the microenvironment, and also as NMJ-sensors by detecting microenvironmental inputs. Together, our data postulate kranocytes as triple connectors of the nervous, musculoskeletal and vascular systems.

neuroscience↗

bioRxiv2025

Comparative analysis of eccDNA and circRNA tools shows increased accuracy of tool combination

IntroductionCircular nucleic acids such as extrachromosomal circular DNA (eccDNA) and circular RNA (circRNA) are increasingly recognized for their biological relevance and potential as biomarkers in disease contexts. Despite their growing importance, their detection remains challenging due to tool-specific biases, limited validation frameworks, and high variability in performance across datasets. MethodsWe benchmarked 10 circle detection tools across diverse conditions using both simulated and biological datasets. Our evaluation included classical performance metrics and a novel internal measure of read distribution symmetry ({Delta}CJ) to assess circle prediction confidence. We explored the impact of sequencing protocols, filtering strategies, and combined tool consensus. ResultsWe found that detection accuracy was highly influenced by sequencing depth, alignment algorithm, and experimental enrichment protocols. {Delta}CJ proved effective in flagging potential false positive circles, showing improved accuracy of Intersect (circles detected by all tools) and Rosette (circles detected by[≥] 2 tools) combinations. DiscussionThis study offers a broad evaluation of circular detection tools, suggesting that the combination of[≥] 3 tools is necessary for a correct prediction. These insights will inform future experimental design and data analysis pipelines in both experimental and clinical settings.

bioinformatics↗

bioRxiv2024

A consensus single-cell transcriptomic atlas of dermal fibroblast heterogeneity

Single-cell RNA sequencing (scRNAseq) studies have unveiled large transcriptomic heterogeneity within both human and mouse dermal fibroblasts, but a consensus atlas that spans both species is lacking. Here, by studying 25 human and 9 mouse datasets through a semi-supervised procedure, we categorize 15 distinct human fibroblast populations across 5 main axes. Analysis of human fibroblast markers characteristic of each population suggested diverse functions, such as position-dependent ECM synthesis, association with immune responses or structural roles in skin appendages. Similarly, mouse fibroblasts were categorized into 17 populations across 5 axes. Comparison of mouse and human fibroblast populations highlighted similarities suggesting a degree of functional overlap, though nuanced differences were also noted: transcriptomically, human axes seem to segregate by function, while mouse axes seem to prioritize positional information over function. Importantly, addition of newer datasets did not significantly change the defined population structure. This study enhances our understanding of dermal fibroblast diversity, shedding light on species-specific distinctions as well as shared functionalities.

bioinformatics↗

bioRxiv2024

A proposed workflow to analyze bacterial transcripts in RNAseq from blood extracellular vesicles of people with Multiple Sclerosis

MotivationThe taxonomical characterisation of bacterial species derived from genetic material blood, including reads derived from bacterial extracellular vesicles (bEVs) poses certain challenges, such as the proper discrimination of "true" reads from contaminating reads. This is a common issue in taxa profiling and can lead to the false discovery of species that are present in the sample. To avoid such biases a careful approximation to taxa profiling is necessary. ResultsIn this work we propose a workflow to analyze the presence of bacterial transcripts as indicative of putative bEVs circulating in the blood of people with MS (pwMS). The workflow includes several reference mapping steps against the host genome and a consensus selection of genera based on different taxa profilers. The consensus selection is performed with a flagging system that removes species with low abundance or with high variation across profilers. Additionally, the inclusion of biological samples from known cultured species as well as the generation of artificial reads constitute two key aspects of this workflow. AvailabilityThe workflow is available at the following repository: https://github.com/NanoNeuro/EV_taxprofiling. Contactdavidangel.otaeguibichot@osakidetza.eus

bioinformatics↗

bioRxiv2022

Lack of evidence for increased transcriptional noise in aged tissues

Aging is often associated with a loss of cell type identity that results in an increase in transcriptional noise in aged tissues. If this phenomenon reflects a fundamental property of aging remains an open question. Transcriptional changes at the cellular level are best detected by single-cell RNA sequencing (scRNAseq). However, the diverse computational methods used for the quantification of age-related loss of cellular identity have prevented reaching meaningful conclusions by direct comparison of existing scRNAseq datasets. To address these issues we created Decibel, a Python toolkit that implements side-to-side four commonly used methods for the quantification of age-related transcriptional noise in scRNAseq data. Additionally, we developed Scallop, a novel computational method for the quantification of membership of single cells to their assigned cell type cluster. Cells with a greater Scallop membership score are transcriptionally more stable. Application of these computational tools to seven aging datasets showed large variability between tissues and datasets, suggesting that increased transcriptional noise is not a universal hallmark of aging. To understand the source of apparent loss of cell type identity associated with aging, we analyzed cell type-specific changes in transcriptional noise and the changes in cell type composition of the mammalian lung. No robust pattern of cell type-specific transcriptional noise alteration was found across aging lung datasets. In contrast, age-associated changes in cell type composition of the lung were consistently found, particularly of immune cells. These results suggest that claims of increased transcriptional noise of aged tissues should be reformulated.

bioinformatics↗

bioRxiv2021

Triku: a feature selection method based on nearest neighbors for single-cell data

Feature selection is a relevant step in the analysis of single-cell RNA sequencing datasets. Triku is a feature selection method that favours genes defining the main cell populations. It does so by selecting genes expressed by groups of cells that are close in the nearest neighbor graph. Triku efficiently recovers cell populations present in artificial and biological benchmarking datasets, based on mutual information and silhouette coefficient measurements. Additionally, gene sets selected by triku are more likely to be related to relevant Gene Ontology terms, and contain fewer ribosomal and mitochondrial genes. Triku is available at https://gitlab.com/alexmascension/triku.

bioinformatics↗

bioRxiv2019

FOntCell: Fusion of Ontologies of Cells

High-throughput cell-data technologies such as single-cell RNA-Seq create a demand for algorithms for automatic cell classification and characterization. There exist several classification ontologies of cells with complementary information. However, one needs to merge them in order to combine synergistically their information. The main difficulty in merging is to match the ontologies since they use different naming conventions. To overcome this obstacle we developed an algorithm that merges ontologies by integrating the name-matching search between class label names with the structure mapping between the ontology elements. To implement our algorithms, we developed FOntCell, a software module in Python for efficient automatic parallel-computed fusion of ontologies in the same or similar knowledge domains. It processes the ontology attributes to extract relations and class synonyms. FOntCell integrates the semantic, name with synonyms, mapping with a structure mapping based on graph convolution. Since the structure mapping assessment is time consuming process, we designed two methods to perform the graph convolution: vectorial structure matching and constraint-based structure matching. To perform the vectorial structure matching we designed a general method to calculate the similarities between vectors of different lengths for different metrics. Additionally, we adapted the slower Blondel method to work for structure matching. These functionalities of FOntCell allow the unification of dispersed knowledge in one domain into a unique ontology. FOntCell produces the results of the merged ontology in OBO format that can be iteratively reused by FOntCell to adapt continuously the ontologies with the new data, such of the Human Cell Atlas, endlessly produced by data-driven classification methods. To navigate easily across the fused ontologies, it generates HTML files with tabulated and graphic summaries, and an interactive circular Directed Acyclic Graphs of the merged results. We used FOntCell to fuse CELDA, LifeMap and LungMAP Human Anatomy cell ontologies to produce comprehensive cell ontology. Author SummaryThere is a strong belief in the research community that there exist more cell types than the described in the literature, therefore new technologies were developed to produce a high volume of data to discover new cells. One issue that arises once the cells are discovered is how to classify them. One way to perform such classification is to use already existing cell classifications from different ontology sources but it is difficult to merge them. An ontology has semantic information providing the meaning of each term and structural information providing the relationship between terms as a graph. We developed a new Python module, FOntCell that merges efficiently cell ontologies and integrates semantic and structure information with our own graph convolution technique. Since the structure mapping assessment is time-consuming process we designed two methods to optimize the graph convolution: vectorial and constraint-based structure matching. To perform the vectorial structure matching we designed a method that calculates the similarities between vectors describing the graphs of different sizes. The functionalities of FOntCell allow the unification of dispersed knowledge into a unique ontology, to adapt continuously from new data, and to navigate across the fused ontologies by a graphic use interface.

bioinformatics↗

bioRxiv2019

BigMPI4py: Python module for parallelization of Big Data objects

Big Data analysis is a discipline with a growing number of areas where huge amounts of data is extracted and analyzed. Parallelization in Python integrates Message Passing Interface via mpi4py module. Since mpi4py does not support parallelization of objects greater than 231 bytes, we developed BigMPI4py, a Python module that wraps mpi4py, supporting object sizes beyond this boundary. BigMPI4py automatically determines the optimal object distribution strategy, and also uses vectorized methods, achieving higher parallelization efficiency. BigMPI4py facilitates the implementation of Python for Big Data applications in multicore workstations and HPC systems. We validated BigMPI4py on whole genome bisulfite sequencing (WGBS) DNA methylation ENCODE data of 59 samples from 27 human tissues. We categorized them on the three germ layers and developed a parallel implementation of the Kruskall-Wallis test to find CpGs with differential methylation across germ layers. We observed a differentiation of the germ layers, and a set of hypermethylated genes in ectoderm and mesoderm-related tissues, and another set in endoderm-related tissues. The parallel evaluation of the significance of 55 million CpG achieved a 22x speedup with 25 cores. BigMPI4py is available at https://gitlab.com/alexmascension/bigmpi4py and the Jupyter Notebook with WGBS analysis at https://gitlab.com/alexmascension/wgbs-analysis

bioinformatics↗