Biology subjects

Riva, S. G.

Publications and source records attributed to Riva, S. G..

12 recordsLinked to original sources

bioRxiv2026

Learning quality scores for chromatin accessibility bigWig tracks using Machine Learning

High-throughput chromatin accessibility assays such as bulk and single-cell ATAC-seq have generated large collections of processed signal tracks in bigWig format, which are widely used for visualisation, data integration, and Machine Learning (ML)-based analyses. Despite their central role, systematic quality control (QC) frameworks operating directly at the level of bigWig signal tracks remain underdeveloped. This gap limits the ability to assess data reliability and hampers robust downstream analyses. Here, we present a biologically grounded QC framework for chromatin accessibility bigWig files that integrates peak-level information, background noise estimation, and recovery of stable genomic reference features. Using an ML-based peak caller (LanceOtron), we derive complementary quality metrics capturing signal structure and signal-to-noise properties. We further define constant promoter and CTCF regions as internal biological controls and show that their recovery provides a sensitive measure of data quality across diverse cellular contexts. We apply this framework to a collection of 502 human chromatin accessibility bigWig tracks spanning a wide range of tissues and cell types. The proposed metrics capture related but non-redundant aspects of signal quality and motivate the use of constant promoter and CTCF recovery as biologically meaningful targets. An XGBoost model trained on LanceOtron-derived features accurately predicts recovery of these stable genomic elements on held-out data (R2 = 0.97), yielding a continuous and interpretable quality score. Feature importance analysis using SHAP values highlights that model decisions are driven by biologically relevant signal properties rather than arbitrary heuristics. Quantile-based stratification of the quality score is further supported by clear qualitative differences in genome browser visualisations. Together, this work provides a principled and extensible frame-work for assessing the quality of chromatin accessibility bigWig tracks, enabling more reliable data integration and supporting downstream ML applications in regulatory genomics.

bioinformatics↗

bioRxiv2026

CREP: Cis-Regulatory Element Predictor Based on Fine-Tuned Enformer

A substantial fraction of disease-associated genetic variants reside in non-coding regions of the genome, where they act by perturbing cis-regulatory elements (CREs) such as enhancers, promoters, and insulators. While recent sequence-based deep learning models, such as Enformer, accurately predict continuous epigenomic signals from DNA sequence, they do not directly provide discrete and interpretable CRE annotations. Here, we present CREP (Cis-Regulatory Element Predictor), a fine-tuned version of Enformer trained to predict regulatory element identity from sequence using REgulamentary-derived annotations across multiple human cell-types. Through a controlled experimental framework, we show that incorporating diverse cell-types improves model performance. CREP leverages cell-type-specific training data to learn regulatory representations while producing a unified prediction of CRE identity from sequence. This is demonstrated by the Vanuatu SNP, a non-coding variant that creates a de novo erythroid regulatory element, which is correctly detected only when erythroid data are included during training. Error analysis further reveals that apparent misclassifications between enhancers and promoters reflect their shared regulatory architecture, supporting the view of CREs as a functional continuum rather than strictly discrete classes. Together, these results demonstrate that CREP enables interpretable prediction of regulatory element identity from sequence and provides a framework for the functional interpretation of non-coding genetic variation.

bioinformatics↗

bioRxiv2026

Expert-Guided Supervised Annotation of Erythroid Differentiation in Single-Cell RNA-seq

Accurate annotation of intermediate cell states remains a major challenge in single-cell RNA sequencing (scRNA-seq), particularly in continuous differentiation systems such as erythropoiesis. Existing reference-based methods often lack the resolution required to distinguish early and transitional erythroid progenitors and may generalise poorly across datasets and modalities. Here, we present a supervised framework for erythroid lineage annotation based on expert-curated training data that integrates bulk and single-cell transcriptomic information. Starting from a human bone marrow scRNA-seq atlas, we refined erythroid annotations by introducing previously unresolved progenitor stages, including burst-forming unit-erythroid (BFU-E), colony-forming unit-erythroid (CFU-E), and pro-erythroblast (ProE), guided by canonical marker genes and bulk RNA-seq references. We trained and benchmarked four classical machine learning models and identified LightGBM as the best-performing approach, achieving a validation macro F1-score of 0.821 and balanced accuracy of 0.826. On a held-out test set, the model showed strong performance across most erythroid stages, with errors largely confined to adjacent differentiation states. The classifier was further transferred to independent bulk RNA-seq samples and an external bone marrow scRNA-seq dataset, where it recovered expected erythroid progression and refined coarse-grained annotations into higher-resolution cell states. Together, these results show that expert-curated supervised learning can improve erythroid cell state annotation in scRNA-seq and provide a practical framework for studying differentiation hierarchies in settings where finely resolved public references are limited.

bioinformatics↗

bioRxiv2026

Enformer-Based Phylogenetic Tree Reconstruction

Enformer is a deep learning model trained on human and mouse genomes to predict regulatory activity from 196,608 bp DNA windows. Its trunk embeddings capture long-range cis-regulatory interactions, but whether this signal generalises across the tree of life has not been assessed. We embed universal single-copy orthologous groups (OGs) from OrthoDB v12 across three taxonomic scales and evaluate reconstructed trees against TimeTree5 using Mantel r and Normalised Robinson-Foulds (NRF). On 702 OGs across 34 Primate species ([≤] 74 Mya), the consensus tree achieves Mantel r = 0.902 and NRF = 0.481, correctly recovering major clades. A key finding is that flanking regulatory context -- not the gene locus itself -- carries the phylogenetic signal: restricting pooling to central 448 bins collapses Mantel r to 0.355. Applying the same fixed configuration to Vertebrates ([≤] 450 Mya, 83 OGs, 150 species) and Plants ([≤] 1,500 Mya, 92 OGs, 40 species) yields consensus Mantel r of 0.752 and 0.803 respectively, with NRF worsening monotonically across tiers. Distance-ordering fidelity degrades smoothly with evolutionary distance while topological accuracy declines steadily, with no sharp taxonomic boundary. These results show that an unmodified regulatory deep learning model encodes robust phylogenetic signal well beyond its training distribution, reaching across 1,500 million years of divergence.

bioinformatics↗

bioRxiv2025

Zone Equalisation Normalisation For Improved Alignment of Epigenetic Signal

High-throughput genomic technologies have transformed our understanding of biological systems, but analysing these complex datasets remains challenging. Existing normalisation methods often struggle to accurately correct for technical variations across different experimental conditions, potentially leading to misleading biological interpretations. We introduce Zone Equalisation Normalisation (ZEN), a novel approach designed to improve signal normalisation across genomic datasets. By developing a flexible method that reduces technical noise whilst preserving biological signals, ZEN addresses critical limitations in current normalisation techniques. By applying ZEN to diverse genomic techniques, we reveal its robust performance in managing technical variations across different experimental platforms. ZEN provides researchers with a robust tool for more accurate and reliable genomic data analysis, potentially revealing biological insights that were previously obscured by technical variations.

bioinformatics↗

bioRxiv2025

REnformer, a single-cell ATAC-seq predicting model to investigate open chromatin sites

Genome regulatory elements are fundamental to cellular identity and cell type specific gene expression. Understanding how the underlying genetic code is differentially utilised by different cell types is central to understanding human health and disease. To better understand how DNA encodes genome regulatory elements such as promoters, enhancers, and boundary elements, we leverage the Enformer gene expression and epigenetic prediction model. We used transfer learning with high quality single cell ATAC datasets to develop REnformer, a model to predict chromatin accessibility. By introducing a bench-mark for comparing performances against Enformer model, REnformer significantly outperformed Enformer in terms of higher prediction outcomes and lower error rates in all extensive analyses shown; introducing these benchmarks allowed us, and possible future works, to fairly compare such models. We further tested REnformer by predicting the effects of a well characterised -thalassemia variant and found that the prediction aligned with the observed change in genome regulatory element, previously validated. We conclude that REnformer is and can be a state-of-the-art tool to predict cell type specific regulatory elements, and interrogate the effect of genome variation in health and disease.

bioinformatics↗

bioRxiv2025

GENESIS: Generating scRNA-Seq data from Multiome Gene Expression

Single-cell technologies have significantly advanced our understanding of cellular heterogeneity by allowing the examination of individual cells at high resolution. Traditional single-cell RNA sequencing (scRNA-Seq) methods, which utilise whole cells, capture comprehensive RNA content. In contrast, emerging Multiome technologies, which simultaneously profile multiple omics such as gene expression (GEX) and chromatin accessibility, rely on nuclear RNA, potentially missing key cytoplasmic information. This discrepancy leads to substantial technical and biological differences between GEX and scRNA-Seq datasets, making it difficult to integrate data and perform downstream tasks like cell-type classification. To address this challenge, we introduce GENESIS (Gene Expression Normalisation and Enhancement for Single-cell Integrated Sequencing), a novel computational framework designed to transform GEX data from Multiome experiments into enhanced, scRNA-Seq like profiles. Utilising advanced generative models--including Variational Autoencoders, Generative Adversarial Networks, and a tailored VAE UNet architecture--GENESIS can generate high-quality data by modelling and compensating for the inherent differences between nuclear and cytoplasmic RNA. Our comprehensive evaluations show that GENESIS, particularly through the VAE UNet model, generates synthetic scRNA-Seq data that closely resembles the resolution and biological accuracy of whole-cell sequencing, improving downstream tasks, especially cell-type classification.

bioinformatics↗

bioRxiv2024

Deciphering cis-regulatory elements using REgulamentary

With the boom in Genome-Wide Association Studies (GWAS), it has become apparent that many disease-associated genetic variants lie in the non-coding regions of the genome. In order to prioritise these variants and disentangle their functional significance, it is important to be able to accurately classify cis-regulatory elements within these non-coding regions of the genome. Historically, the classification of cis-regulatory elements relied purely on the presence of characteristic histone marks, with recent advancements in their classification using more sophisticated Hidden Markov Model (HMM)-based approaches. The limitation of the HMM-based approaches is that the output of these models is an arbitrary chromatin state, which then requires the user to manually assign these states to a particular class of cis-regulatory elements. Here we present a new tool, REgulamentary, which enables de novo genome-wide annotation of cis-regulatory elements in a cell-type specific manner. We benchmarked REgulamentary against GenoSTAN, the most popular existing published chromatin annotation and regulatory element identification tool, to demonstrate the advancements REgulamentary can provide in assigning chromatin states. Finally, as an example of REgulamentarys utility in solving complex disease trait loci, we applied REgulamentary to published GWAS data to demonstrate how this tool can be used to prioritise likely causal variants.

bioinformatics↗

bioRxiv2023

Active regulatory elements recruit cohesin to establish cell-specific chromatin domains.

As the structure of the genome is analysed at ever increasing resolution it is becoming clear that there is considerable variation in the 3D chromatin architecture across different cell types. It has been proposed that this may, in part, be due to increased recruitment of cohesin to activated cis-elements (enhancers and promoters) leading to cell-type specific loop extrusion underlying the formation of new subTADs. Here we show that cohesin correlates well with the presence of active enhancers and this varies in an allele-specific manner with the presence or absence of polymorphic enhancers which vary from one individual to another. Using the alpha globin cluster as a model, we show that when all enhancers are removed, peaks of cohesin disappear from these regions and the erythroid specific subTAD is no longer formed. Re-insertion of the major alpha globin enhancer (R2) is associated with the appearance of a new peak of cohesin at the site of insertion. In complementary experiments insertion of R2 into a "neutral" region of the genome recruits cohesin, induces transcription and creates a new large (75kb) erythroid specific domain. Together these findings support the proposal that active enhancers recruit cohesin, stimulate loop extrusion and promote the formation of cell specific subTADs.

genomics↗

bioRxiv2021

SMGen: A generator of synthetic models of biochemical reaction networks

Several software tools for the simulation and analysis of biochemical reaction networks have been developed in the last decades; however, assessing and comparing their computational performance in executing the typical tasks of Computational Systems Biology can be limited by the lack of a standardized benchmarking approach. To overcome these limitations, we propose here a novel tool, named SMGen, designed to automatically generate synthetic models of reaction networks that, by construction, are characterized by both features (e.g., system connectivity, reaction discreteness) and non trivial emergent dynamics of real biochemical networks. The generation of synthetic models in SMGen is based on the definition of an undirected graph consisting of a single connected component, which generally results in a computationally demanding task. To avoid any burden in the execution time, SMGen exploits a Main-Worker paradigm to speed up the overall process. SMGen is also provided with a user-friendly Graphical User Interface that allows the user to easily set up all the parameters required to generate a set of synthetic models with any user-defined number of reactions and species. We analysed the computational performance of SMGen by generating batches of symmetric and asymmetric Reaction-based Models (RBMs) of increasing size, showing how a different number of reactions and/or species affects the generation time. Our results show that when the number of reactions is higher than the number of species, SMGen has to identify and correct high numbers of errors during the creation process of the RBMs, a circumstance that increases the overall running time. Still, SMGen can create synthetic models with 512 species and reactions in less than 7 seconds. The open-source code of SMGen is available on GitLab: https://gitlab.com/sgr34/smgen.

systems biology↗

bioRxiv2021

SMaSH: A scalable, general marker gene identification framework for single-cell RNA sequencing and Spatial Transcriptomics

Spatial transcriptomics is revolutionising the study of single-cell RNA and tissue-wide cell heterogeneity, but few robust methods connecting spatially resolved cells to so-called marker genes from single-cell RNA sequencing, which generate significant insight gleaned from spatial methods, exist. Here we present SMaSH, a general computational framework for extracting key marker genes from single-cell RNA sequencing data for spatial transcriptomics approaches. SMaSH extracts robust and biologically well-motivated marker genes, which characterise the given data-set better than existing and limited computational approaches for global marker gene calculation.

bioinformatics↗

bioRxiv2020

Integrative Single-cell RNA-Seq and ATAC-Seq Analysis of Human Foetal Liver and Bone Marrow Haematopoiesis

Regulation of haematopoiesis during human development remains poorly defined. Here, we applied single-cell (sc)RNA-Seq and scATAC-Seq analysis to over 8,000 human immunophenotypic blood cells from foetal liver and bone marrow. We inferred their differentiation trajectory and identified three highly proliferative oligopotent progenitor populations downstream from haematopoietic stem cell/multipotent progenitors (HSC/MPPs). Along this trajectory, we observed opposing patterns of chromatin accessibility and differentiation that coincided with dynamic changes in the activity of distinct lineage-specific transcription factors. Integrative analysis of chromatin accessibility and gene expression revealed extensive epigenetic but not transcriptional priming of HSC/MPPs prior to their lineage commitment. Finally, we refined and functionally validated the sorting strategy for the HSC/MPPs and achieved around 90% enrichment. Our study provides a useful framework for future investigation of human developmental haematopoiesis in the context of blood pathologies and regenerative medicine.

cell biology↗