Biology subjects

Prada-Luengo, I.

Publications and source records attributed to Prada-Luengo, I..

5 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↗

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↗

bioRxiv2023

N-of-one differential gene expression without control samples using a deep generative model

Differential gene expression analysis of bulk RNA sequencing data plays a major role in the diagnosis, prognosis, and understanding of disease. Such analyses are often challenging due to a lack of good controls and the heterogeneous nature of the samples. Here, we present a deep generative model that can replace control samples. The model is trained on RNA-seq data from healthy tissues and learns a low-dimensional representation that clusters tissues very well without supervision. When applied to cancer samples, the model accurately identifies representations close to the tissue of origin. We interpret these inferred representations as the closest normal to the disease samples and use the resulting count distributions to perform differential expression analysis of single cancer samples without control samples. In a detailed analysis of breast cancer, we demonstrate how our approach finds subtype-specific cancer driver and marker genes with high specificity and greatly outperforms the state-of-the-art method in detecting differentially expressed genes, DESeq2. We further show that the significant genes found using the model are highly enriched within cancer-specific driver genes across different cancer types. Our results show that the in silico closest normal provides a more favorable comparison than control samples.

bioinformatics↗

bioRxiv2020

Replicative aging is associated with loss of genetic heterogeneity from extrachromosomal circular DNA in Saccharomyces cerevisiae

Circular DNA of chromosomal origin form from all parts of eukaryotic genomes. In yeast, circular rDNA accumulates as cells divide, contributing to replicative aging. However, little is known about how other chromosome-deri ved circles segregate and contribute to geneticvariation as cells age. We identified circular DNA across the genome of young S. cerevisiae populations and their aged descendants. Young cells had highly diverse circular DNA populations, but lost 94% of the different circular DNA after 20 divisions. Circles present in both young and old cells were characterized by replication origins and included circles from unique regions of the genome, rDNA circles and telomeric Y circles. The loss in genetic heterogeneity in aged cells was accompanied by massive accumulation of rDNA circles >95% of all circular DNA. We discovered circles had flexible inherence patterns. Glucose limited conditions selected for cells with glucose-transporter gene circles, [HXT6/7circle], and up to 50% of cells in a population carried them. [HXT6/7circle] cells were eventually substituted by cells carrying stable chromosomal HXT6 HXT6/7 HXT7 amplifications, suggesting circular DNA were intermediates in chromosomal amplifications. In conclusion, DNA circles can offer a flexible adaptive solution but cells lose genetic heterogeneity from circular DNA as they undergo replicative aging.

cell biology↗

bioRxiv2019

Sensitive detection of circular DNA at single-nucleotide resolution using guided realignment of partially aligned reads

Circular DNA has recently been identified across different species including human normal and cancerous tissue, but short-read mappers are unable to align many of the reads crossing circle junctions and hence limits their detection from short-read sequencing data. Here, we propose a new method, Circle-Map, that guides the realignment of partially aligned reads using information from discordantly mapped reads. We demonstrate how this approach dramatically increases sensitivity for detection of circular DNA on both simulated and real data while retaining high precision.

bioinformatics↗