Biology subjects

Giugno, R.

Publications and source records attributed to Giugno, R..

11 recordsLinked to original sources

bioRxiv2025

CRISPR-HAWK: Haplotype- and Variant-aware guide design toolkit for CRISPR-Cas

Current CRISPR guide RNA design tools rely on reference genomes, overlooking how genetic variation impacts editing outcomes. As genome editing advances toward clinical applications, incorporating population diversity becomes essential for ensuring therapeutic efficacy across diverse populations. We present CRISPR-HAWK, a framework integrating individual- and population-scale variants and haplotypes into gRNA design. Analyzing therapeutic targets across 79,648 genomes reveals that genetic variants substantially alter guide performance. For the clinically approved sickle cell disease therapeutic guide targeting BCL11A, we identify haplotypes that completely abolish predicted cutting activity. Across seven therapeutic loci, 82.5% of guides contain variants modifying on-target activity. Variants also create novel protospacer adjacent motif sites generating individual-specific guides invisible to reference-based design. These findings demonstrate that variant-aware selection is critical for equitable genome editing. CRISPR-HAWK is available at https://github.com/pinellolab/CRISPR-HAWK and https://github.com/InfOmics/CRISPR-HAWK

bioinformatics↗

bioRxiv2025

Improving DNA Modeling with WaveDNA: Enhancing Speed, Generalizability, and Interpretability through Wavelet Transformation

Transcription factors (TFs) regulate gene expression by binding to short, specific DNA sequences, known as transcription factor binding sites (TFBSs). Accurate identification of TFBSs is fundamental for understanding transcriptional regulation. By leveraging their ability to capture complex non-linear patterns and hierarchical dependencies underlying TF-DNA binding deep learning (DL) has emerged as the state-of-the-art approach for modeling and identifying TFBSs. However, current models often require extensive pretraining, involve large parameter sets, and offer limited interpretability. To address these limitations, we introduce WaveDNA, a lightweight and interpretable DL framework that encode DNA sequences into two-dimensional representations using wavelet transforms. This approach enables the use of convolutional neural networks pretrained on images, facilitating efficient transfer learning without requiring large-scale genomic data pretraining. Across diverse ENCODE ChIP-seq datasets spanning different TFs, WaveDNA achieves predictive accuracy comparable to state-of-the-art DL models while using approximately fivefold fewer parameters and substantially less computational resources. Moreover, representing DNA sequences as images allows the direct application of established computer vision interpretability techniques to visualize the learned binding patterns. Together, these results demonstrate that WaveDNA offers a scalable, computationally efficient, and interpretable alternative for modeling TF-DNA interactions.

bioinformatics↗

bioRxiv2025

Benchmarking PWM and SVM-based Models for Transcription Factor Binding Site Prediction: A Comparative Analysis on Synthetic and Biological Data

Transcription Factors (TFs) are essential regulatory proteins that control the cellular transcriptional states by binding to specific DNA sequences known as Transcription Factor Binding Sites (TFBSs) or motifs. Accurate TFBS identification is crucial for unraveling regulatory mechanisms driving cellular dynamics. Over the years, various computational approaches have been developed to model TFBSs, with Position Weight Matrices (PWMs) being one of the most widely adopted methods. PWMs provide a probabilistic framework by representing nucleotide frequencies at every position within the binding site. While effective and interpretable, PWMs face significant limitations, such as their inability to capture positional dependencies or model complex interactions. To address these, advanced methods, such as Support Vector Machine (SVM)-based models, have been introduced. Leveraging human ChIP-seq data from ENCODE, this study systematically benchmarks the predictive performance of PWM and SVM-based models across different scenarios. We evaluate the impact of key factors such as training dataset size, sequence length, and kernel functions (for SVMs) on models performance. Additionally, we explore the impact of synthetic versus real biological background data during model training. Our analysis highlights strengths and limitations of both PWM and SVM-based approaches under different conditions, providing practical guidance for selecting and tailoring models to specific biological datasets. To complement our analysis, we present a comprehensive database of pretrained SVM models for TFBS detection, trained on human ChIP-seq data from diverse cell lines and tissues. This resource aims to facilitate broader adoption of SVM-based methods in TFBS prediction and enhance their practical utility in regulatory genomics research.

bioinformatics↗

bioRxiv2023

EasyCircR:Detection and reconstruction of circular RNAs post-transcriptional regulatory interaction networks

Circular RNAs (circRNAs) are regulatory RNAs that play a crucial role in various biological activities and have been identified as potential biomarkers for the development of neurological disorders and cancer. CircRNAs have emerged as significant regulators of gene expression through different mechanisms, including regulation of transcription and splicing, modulation of translation, and post-translational modification. Additionally, some circRNAs function as microRNA (miRNA) sponges in the cytoplasm, enhancing the expression of target genes by suppressing miRNA activity. Although existing pipelines reconstruct circRNAs, identify miRNAs sponged by them, retrieve cascade-regulated mRNAs, and represent the regulatory interactions as complex circRNA-miRNA-mRNA networks, none of the state-of-the-art approaches can discriminate the biological level at which the mRNAs involved in the interactions are regulated. EasyCircR is a novel R package that combines circRNA detection and reconstruction with post-transcriptional gene expression analysis (exon-intron split analysis) and miRNA response element prediction. The package enables estimation and visualization of circRNA-miRNA-mRNA interactions through an intuitive Shiny application, taking advantage of the post-transcriptional regulatory nature of circRNAs and excluding unrealistic regulatory outcomes. EasyCircR source code, Docker container and user guide are available at: https://github.com/InfOmics/EasyCircR

bioinformatics↗

bioRxiv2022

Esearch3D: Propagating gene expression in chromatin networks to illuminate active enhancers

Most cell type-specific genes are regulated by the interaction of enhancers with their promoters. The identification of enhancers is not trivial as enhancers are diverse in their characteristics and dynamic in their interaction partners. Currently, enhancer-associated features such as histone modifications, co-activators or bi-directional transcription are used in lieu of any definitive and universal enhancer feature. We present Esearch3D, a new approach that leverages network theory approaches to identify active enhancers. Our work is based on the fact that enhancers act as a source of regulatory information to increase the rate of transcription of their target genes and that the flow of this information is mediated by the folding of chromatin in the three-dimensional (3D) nuclear space between the enhancer and the target gene promoter. Esearch3D reverse engineers this flow of information to calculate the likelihood of enhancer activity in intergenic regions by propagating the transcription levels of genes across 3D-genome networks. Regions predicted to have high enhancer activity are shown to be enriched in annotations indicative of enhancer activity. These include: enhancer-associated histone marks, bi-directional CAGE-seq, STARR-seq, P300 and RNA polymerase II ChIP-seq, and expression quantitative trait loci (eQTL). Esearch3D successfully leverages the relationship between chromatin architecture and global transcription and represents a novel approach to predict active enhancers and understand the complex underpinnings of regulatory networks. The method is available at: https://github.com/InfOmics/Esearch3D.

bioinformatics↗

bioRxiv2022

Stardust: improving spatial transcriptomics data analysis through space aware modularity optimization based clustering.

BackgroundSpatial transcriptomics (ST) combines stained tissue images with spatially resolved high-throughput RNA sequencing. The spatial transcriptomic analysis includes challenging tasks like clustering, where a partition among data points (spots) is defined by means of a similarity measure. Improving clustering results is a key factor as clustering affects subsequent downstream analysis. State-of-the-art approaches group data by taking into account transcriptional similarity and some by exploiting spatial information as well. However, it is not yet clear how much the spatial information combined with transcriptomics improves the clustering result. ResultsWe propose a new clustering method, Stardust, that easily exploits the combination of space and transcriptomic information in the clustering procedure through a manual or fully automatic tuning of algorithm parameters. Moreover, a parameter-free version of the method is also provided where the spatial contribution depends dynamically on the expression distances distribution in the space. We evaluated the proposed methods results by analysing ST datasets available on the 10x Genomics website and comparing clustering performances with state-of-the-art approaches by measuring the spots stability in the clusters and their biological coherence. Stability is defined by the tendency of each point to remain clustered with the same neighbours when perturbations are applied. ConclusionsStardust is an easy-to-use methodology allowing to define how much spatial information should influence clustering on different tissues and achieving more stable results than state-of-the-art approaches.

bioinformatics↗

bioRxiv2022

Microglial amyloid beta clearance is driven by PIEZO1 channels

BackgroundMicroglia are the endogenous immune cells of the brain and act as sensors of pathology to maintain brain homeostasis and eliminate potential threats. In Alzheimers disease (AD), toxic amyloid beta (A{beta}) accumulates in the brain and forms stiff plaques. In late-onset AD accounting for 95% of all cases, this is thought to be due to reduced clearance of A{beta}. Human genome-wide association studies and animal models suggest that reduced clearance results from aberrant function of microglia. While the impact of neurochemical pathways on microglia have been broadly studied, mechanical receptors regulating microglial functions remain largely unexplored. MethodsHere we showed that a mechanotransduction ion channel, PIEZO1, is expressed and functional in human and mouse microglia. We used a small molecule agonist, Yoda1, to study how activation of PIEZO1 affects AD-related functions in human induced pluripotent stem cell (iPSC) -derived microglia-like cells (iMGL) under controlled laboratory experiments. Cell survival, metabolism, phagocytosis and lysosomal activity were assessed using real-time functional assays. To evaluate the effect of activation of PIEZO1 in vivo, 5-month-old 5xFAD male mice were infused daily with Yoda1 for two weeks through intracranial cannulas. Microglial Iba1 expression and A{beta} pathology were quantified with immunohistochemistry and confocal microscopy. Published human and mouse AD datasets were used for in-depth analysis of PIEZO1 gene expression and related pathways in microglial subpopulations. ResultsWe show that PIEZO1 orchestrates A{beta} clearance by enhancing microglial survival, phagocytosis, and lysosomal activity. A{beta} inhibited PIEZO1-mediated calcium transients, whereas activation of PIEZO1 with a selective agonist, Yoda1, improved microglial phagocytosis resulting in A{beta} clearance both in human and mouse models of AD. Moreover, PIEZO1 expression was associated with a unique microglial transcriptional phenotype in AD as indicated by assessment of cellular metabolism, and human and mouse single cell datasets. ConclusionThese results indicate that the compromised function of microglia in AD could be improved by controlled activation of PIEZO1 channels resulting in alleviated A{beta} burden. Pharmacological regulation of these mechanoreceptors in microglia could represent a novel therapeutic paradigm for AD. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=176 HEIGHT=200 SRC="FIGDIR/small/484831v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@832eadorg.highwire.dtl.DTLVardef@6d8719org.highwire.dtl.DTLVardef@c09740org.highwire.dtl.DTLVardef@9fa85_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2021

Multi view based imaging genetics analysis on Parkinson disease

Longitudinal studies integrating imaging and genetic data have recently become widespread among bioinformatics researchers. Combining such heterogeneous data allows a better understanding of complex diseases origins and causes. Through a multi-view based workflow proposal, we show the common steps and tools used in imaging genetics analysis, interpolating genotyping, neuroimaging and transcriptomic data. We describe the advantages of existing methods to analyze heterogeneous datasets, using Parkinsons Disease (PD) as a case study. Parkinsons disease is associated with both genetic and neuroimaging factors, however such imaging genetics associations are at an early investigation stage. Therefore it is desirable to have a free and open source workflow that integrates different analysis flows in order to recover potential genetic biomarkers in PD, as in other complex diseases.

bioinformatics↗

bioRxiv2021

Human genetic diversity modifies therapeutic gene editing off-target potential

CRISPR gene editing holds great promise to modify somatic genomes to ameliorate disease. In silico prediction of homologous sites coupled with biochemical evaluation of possible genomic off-targets may predict genotoxicity risk of individual gene editing reagents. However, standard computational and biochemical methods focus on reference genomes and do not consider the impact of genetic diversity on off-target potential. Here we developed a web application called CRISPRme that explicitly and efficiently integrates human genetic variant datasets with orthogonal genomic annotations to nominate and prioritize off-target sites at scale. The method considers both single-nucleotide variants (SNVs) and indels, accounts for bona fide haplotypes, accepts spacer:protospacer mismatches and bulges, and is suitable for personal genome analyses. We tested the tool with a guide RNA (gRNA) targeting the BCL11A erythroid enhancer that has shown therapeutic promise in clinical trials for sickle cell disease (SCD) and {beta}-thalassemia1. We find that the top candidate off-target site is produced by a non-reference allele common in African-ancestry populations (rs114518452, minor allele frequency (MAF)=4.5%) that introduces a protospacer adjacent motif (PAM) for SpCas9. We validate that SpCas9 generates indels ([~]9.6% frequency) and chr2 pericentric inversions in a strictly allele-specific manner in edited CD34+ hematopoietic stem/progenitor cells (HSPCs), although a high-fidelity Cas9 variant mitigates this off-target. The CRISPRme tool highlights alternative allele-specific off-target editing as a prevalent risk of gRNAs considered for therapeutic gene editing. Our report illustrates how population and private genetic variants should be considered as modifiers of genome editing outcomes. We suggest that variant-aware off-target assessment should be considered in therapeutic genome editing efforts going forward and provide a powerful approach for comprehensive off-target nomination.

genetics↗

bioRxiv2021

GRAFIMO: variant and haplotype aware motif scanning on pangenome graphs

Transcription factors (TFs) are proteins that promote or reduce the expression of genes by binding short genomic DNA sequences known as transcription factor binding sites (TFBS). While several tools have been developed to scan for potential occurrences of TFBS in linear DNA sequences or reference genomes, no tool exists to find them in pangenome variation graphs (VGs). VGs are sequence-labelled graphs that can efficiently encode collections of genomes and their variants in a single, compact data structure. Because VGs can losslessly compress large pangenomes, TFBS scanning in VGs can efficiently capture how genomic variation affects the potential binding landscape of TFs in a population of individuals. Here we present GRAFIMO (GRAph-based Finding of Individual Motif Occurrences), a command-line tool for the scanning of known TF DNA motifs represented as Position Weight Matrices (PWMs) in VGs. GRAFIMO extends the standard PWM scanning procedure by considering variations and alternative haplotypes encoded in a VG. Using GRAFIMO on a VG based on individuals from the 1000 Genomes project we recover several potential binding sites that are enhanced, weakened or missed when scanning only the reference genome, and which could constitute individual-specific binding events. GRAFIMO is available as an open-source tool, under the MIT license, at https://github.com/pinellolab/GRAFIMO and https://github.com/InfOmics/GRAFIMO. Author summaryTranscription factors (TFs) are key regulatory proteins and mutations occurring in their binding sites can alter the normal transcriptional landscape of a cell and lead to disease states. Pangenome variation graphs (VGs) efficiently encode genomes from a population of individuals and their genetic variations. GRAFIMO is an open-source tool that extends the traditional PWM scanning procedure to VGs. By scanning for potential TBFS in VGs, GRAFIMO can simultaneously search thousands of genomes while accounting for SNPs, indels, and structural variants. GRAFIMO reports motif occurrences, their statistical significance, frequency, and location within the reference or alternative haplotypes in a given VG. GRAFIMO makes it possible to study how genetic variation affects the binding landscape of known TFs within a population of individuals.

bioinformatics↗

bioRxiv2020

Disease specific alterations in the olfactory mucosa of patients with Alzheimer's disease

Olfactory dysfunction manifests early in several neurodegenerative disorders. Olfaction is orchestrated by olfactory mucosal cells located in the upper nasal cavity. However, it is unclear how this tissue reflects key neurodegenerative features in Alzheimers disease. Here we report that Alzheimers disease olfactory mucosal cells obtained from live individuals secrete toxic amyloid-beta. We detail cell-type-specific gene expression patterns, unveiling 147 differentially expressed disease-associated genes compared to the cognitively healthy controls, and 5 distinct populations in globose basal cell -, myofibroblast-, and fibroblast/ stromal - like cells in vitro. Overall, coordinated alteration of RNA and protein metabolism, inflammatory processes and signal transduction were observed in multiple cell populations, suggesting a key role in pathophysiology. Our results demonstrate the potential of olfactory cell cultures in modelling Alzheimers disease advocate their use for diagnostic purposes. Moreover, for the first time we provide single cell data on olfactory mucosa in Alzheimers disease for investigating molecular and cellular mechanisms associated with the disease.

neuroscience↗