Biology subjects

Soranzo, N.

Publications and source records attributed to Soranzo, N..

At least 19 recordsLinked to original sources

bioRxiv2026

EISCA and EISTA: Full-Spectrum Pipelines for Single-Cell and Spatial Transcriptomics Analysis

Single-cell and spatial transcriptomics are transforming our understanding of cellular heterogeneity and tissue organization, yet their analytical complexity remains a major bottleneck. Here, we present EISCA and EISTA, two standardized, end-to-end pipelines for single-cell RNA-seq and imaging-based spatial transcriptomics analysis. Built on the Nextflow nf-core framework, both pipelines implement modular, scalable, and reproducible workflows spanning primary, secondary, and tertiary analyses, from raw data processing to advanced downstream analyses. EISCA supports droplet- and plate-based scRNA-seq technologies, while EISTA is tailored for high-resolution spatial platforms including Vizgen MERFISH and 10x Xenium. Together, they integrate state-of-the-art methods for quality control, normalization, clustering, integration, cell-type annotation, differential expression, and cell-cell communication, with EISTA further enabling spatial statistical analyses. A central design principle is to balance standardization with flexibility: workflows can be executed end-to-end or modularly, enabling iterative, exploratory analyses with minimal overhead. Both pipelines deliver rapid preliminary results alongside an out-of-the-box report, facilitating immediate data assessment and accelerating downstream discovery. Case studies in plant immunity and human sepsis demonstrate that EISTA and EISCA reproducibly can be used to recover biologically meaningful insights. Collectively, these pipelines provide efficient, flexible, and scalable solutions for comprehensive single-cell and spatial transcriptomics analyses.

bioinformatics↗

bioRxiv2026

A Pan-pangenome illuminates complex structural variation and selection in humans, chimpanzees, and bonobos

Complete, haplotype-resolved genome assemblies have provided unprecedented insight into the evolution of structurally complex, rapidly evolving regions of human genomes1-6; however, population-scale pangenome resources of our closest relatives, chimpanzees and bonobos (genus, Pan), are necessary to ascertain the origins and evolutionary context of these loci. Here, we sequence and assemble 58 haplotypes from four distinct Pan clades to high contiguity (median contig NG50=54 Mb), including eight near-T2T genomes. These genomes reveal previously intractable genetic variation increasing estimates of genome-wide genetic diversity 6-37% across populations compared to short-read estimates7. We identify recurrent structural polymorphisms across species impacting genes associated with immune response and host-pathogen interaction and find that structural variants (SVs) are 170- to 260-fold more likely than single nucleotide variants (SNVs) to exhibit high-impact effects across species. Contrasting SV patterns across primates we find that transposable element mutation rates differ by as much as threefold between species. We show that human disease-associated short tandem repeat (TR) loci have uniquely expanded in humans sensitizing our species to these TR-expansion disorders. Physically phased haplotypes enable reconstruction of genome-wide genealogical histories, uncovering ancient, functional genetic variation maintained by balancing selection, as well as signatures of recent adaptation in chimpanzee subspecies. Several malaria-associated loci exhibit ancient structural polymorphism, including the African great ape-specific glycophorin (GYP) gene expansion. We characterize the sequence, structure, and composition of diverse glycophorin haplotypes in humans and chimpanzees. We identify independent malaria-protective GYPA-B fusion events in humans and novel chimpanzee glycophorin genes resulting from both ancient and recent fusion events demonstrating parallel adaptations to pathogen resistance across hominins. Together, our resource highlights the critical importance of nonhuman primate population-scale pangenomics for understanding the evolution of complex genome structures and the biodiversity of our endangered closest living relatives.

evolutionary biology↗

bioRxiv2026

Population-scalable genotyping from low-coverage sequencing data using pangenome graphs

Pangenome-based genotyping of structurally complex loci remains challenging at low sequencing depths, particularly when samples are of low quality, such as in ancient DNA. We present COSIGT (COsine SImilarity-based GenoTyper), a method that infers structural genotypes by matching short-read coverage patterns to pangenome haplotypes using cosine similarity. COSIGT maintains robust accuracy at low coverage (1-2X), outperforming existing methods where depth-sensitive approaches degrade. We demonstrate scalability to thousands of modern and ancient genomes, enabling population-scale analyses of complex variation from low-coverage data.

bioinformatics↗

bioRxiv2025

Dissecting the Cellular Genetics of Cardiovascular Disease Through Endothelial and Immune Compartments Profiling

BackgroundNon-communicable diseases such as coronary artery disease, atrial fibrillation, type 2 diabetes, hypertension, and others share endothelial dysfunction as one of their underlying features. The endothelium, as the interface between blood and vasculature, shapes disease onset and progression through its response to environmental cues. However, while the genetic component of these diseases has been captured by genome wide association studies (GWAS), which also highlighted a shared immune component, it remains unclear which of these disease loci exerts their effects through endothelial cells. This study identifies, and quantifies, the genetic determinants of endothelial cells molecular traits and their overlap to the common genetic variation component of these diseases. MethodsWe generated genotype, RNA-sequencing, H3K27ac ChIP-sequencing, ATAC-sequencing, and endothelial cells barrier stimuli response measurements for 100 samples of human umbilical vein endothelial cells. These were used to identify quantitative trait loci (QTL) for gene expression, transcriptional isoform usage, splice junction usage, chromatin activity and barrier response. We applied statistical colocalisation to identify the overlap between data layers, and to explain molecular QTLs contribution to GWAS disease loci. ResultsWe used molecular QTLs to identify the regulatory features of 8,214 genes, representing 36% of all expressed genes in endothelial cells. We also identified the molecular mechanisms underlying 815 loci across 16 disease GWAS. These represent between 29% and 40% of all loci for each disease, compared to the previous average of 23%. This is due to the choice of a cell type often underrepresented in tissue level data, and the inclusion of isoform, splicing and chromatin activity datasets. Furthermore, we compared the endothelial cells molecular QTLs with similar datasets in monocytes, neutrophils and CD4 T lymphocytes to shed light on the interplay between the endothelial and the immune compartments in these diseases. We identified loci acting through both the endothelial and the immune compartment, mostly with the same directionality of effect, and endothelial specific ones. ConclusionsThis work expands the knowledge of the mechanisms and genes underlying the effect of common genetic variation on non-communicable diseases having endothelial dysfunction as a shared feature. It also illustrates the interplay between endothelial cells and immune cell types in these diseases, highlighting shared and unique pathways.

genomics↗

bioRxiv2025

SAFE-LD: A novel method for the estimation of linkage disequilibrium from summary statistics

Genome-wide association studies (GWAS) have greatly advanced our understanding of the genetic architecture of complex traits. Downstream analyses of GWAS summary statistics require accurate in-sample LD, the variant correlations in the same individuals used for the GWAS, as even small discrepancies can propagate into substantial error. In practice, privacy and consent restrictions prevent sharing of individual-level genotypes, forcing researchers either to rely on external reference panels, which reduce accuracy and power, or to store and distribute massive precomputed LD matrices that are inflexible and difficult to analyze. Here we introduce SAFE-LD (Shrinkage and Anonymisation Framework for LD Estimation), a novel method that produces pseudo-genotypes designed to reproduce the exact in-sample LD of a cohort, while discarding all individual-level genetic content. SAFELD surrogates can be stored in VCF/PGEN formats and used seamlessly with standard pipelines, providing LD estimates indistinguishable from the originals but free from privacy concerns. Using extensive simulations on UK Biobank data, we show that SAFE-LD is robust across genomic regions and population sizes. Notably, SAFE-LD achieves finemapping accuracy on par with internal LD, and significantly outperforms external LD even under best-case conditions with cohort-matched reference panels. We further extend this framework to existing GWAS summary statistics through SAFE-LDss, which exploits existing published summary statistics where numerous traits have been analyzed on the same samples. SAFE-LD offers a scalable, privacy-preserving, and highly accurate alternative to traditional LD estimation, enabling easy sharing and seamless utilization with standard tools. By storing compact pseudo-genotypes instead of massive precomputed LD matrices, it also provides a highly efficient solution in terms of disk space and data management, while safeguarding participant privacy and supporting precise fine-mapping.

genetics↗

bioRxiv2024

microGalaxy: A gateway to tools, workflows, and training for reproducible and FAIR analysis of microbial data

The explosion of microbial omics data has outpaced the ability of many researchers to analyze it, with complex tools and limited computational resources creating barriers to discovery. To address this gap, we present the Microbiology Galaxy Lab: a free, globally accessible, community-supported platform that combines state-of-the-art analytical power with user-friendly accessibility. Supported by the Galaxy and global microbiology communities, this platform integrates over 315 tool suites and 115 curated workflows, enabling comprehensive metabarcoding, (meta)genomic, (meta)transcriptomic, and (meta)proteomic data analysis within a FAIR-aligned environment. It also supports research in the health and infectious disease sectors, as well as in environmental microbiology. The platforms utility is exemplified through various use cases, including antimicrobial resistance tracking, biomarker prediction, microbiome classification, and functional annotation of key microbes. Built on reproducibility and community engagement, it supports creation, sharing, and updating of best-practice workflows. Over 35 tutorials and learning paths empower scientists, fostering an ecosystem that keeps resources at the forefront of microbial science. The Microbiology Galaxy Lab enables collective analysis, democratising research, thereby accelerating discovery across the global microbiology community (microbiology.usegalaxy.org, microbiology.usegalaxy.eu, microbiology.usegalaxy.org.au, microbiology.usegalaxy.fr).

bioinformatics↗

bioRxiv2024

Variant-to-function dissection of rare non-coding GWAS loci with high impact on blood traits

Understanding the function of genetic variants associated with human traits and diseases remains a significant challenge. Here we combined analyses based on natural genetic variation and genetic engineering to dissect the function of 94 non-coding variants associated with haematological traits. We describe 22 genetic variants impacting haematological variation through gene expression. Further, through in-depth functional analysis, we illustrate how a rare, non-coding variant near the CUX1 transcription factor impacts megakaryopoiesis through modulation of the CUX1 transcriptional cascade. Collectively, our findings enhance the functional interpretation of genetic association studies and advance understanding of how non-coding variants contribute to blood and immune system variation.

genetics↗

bioRxiv2024

Global diversity, recurrent evolution, and recent selection on amylase structural haplotypes in humans

The adoption of agriculture, first documented [~]12,000 years ago in the Fertile Crescent, triggered a rapid shift toward starch-rich diets in human populations. Amylase genes facilitate starch digestion and increased salivary amylase copy number has been observed in some modern human populations with high starch intake, though evidence of recent selection is lacking. Here, using 52 long-read diploid assemblies and short read data from [~]5,600 contemporary and ancient humans, we resolve the diversity, evolutionary history, and selective impact of structural variation at the amylase locus. We find that amylase genes have higher copy numbers in populations with agricultural subsistence compared to fishing, hunting, and pastoral groups. We identify 28 distinct amylase structural architectures and demonstrate that nearly identical structures have arisen recurrently on different haplotype backgrounds throughout recent human history. AMY1 and AMY2A genes each exhibit multiple duplications/deletions with mutation rates >10,000-fold the SNP mutation rate, whereas AMY2B gene duplications share a single origin. Using a pangenome graph-based approach to infer structural haplotypes across thousands of humans, we identify extensively duplicated haplotypes present at higher frequencies in modern day populations with traditionally agricultural diets. Leveraging 533 ancient human genomes we find that duplication-containing haplotypes (i.e. haplotypes with more amylase gene copies than the ancestral haplotype) have increased in frequency more than seven-fold over the last 12,000 years providing evidence for recent selection in West Eurasians. Together, our study highlights the potential impacts of the agricultural revolution on human genomes and the importance of long-read sequencing in identifying signatures of selection at structurally complex loci.

evolutionary biology↗

bioRxiv2023

Misexpression of inactive genes in whole blood is associated with nearby rare structural variants

Gene misexpression is the aberrant transcription of a gene in a context where it is usually inactive. Despite its known pathological consequences in specific rare diseases, we have a limited understanding of its wider prevalence and mechanisms in humans. To address this, we analyzed gene misexpression in 4,568 whole blood bulk RNA sequencing samples from INTERVAL study blood donors. We found that while individual misexpression events occur rarely, in aggregate they were found in almost all samples and over half of inactive genes. Using 2,821 paired whole genome and RNA sequencing samples, we identified that misexpression events are enriched in cis for rare structural variants. We established putative mechanisms through which a subset of SVs lead to gene misexpression, including transcriptional readthrough, transcript fusions and gene inversion. Overall, we develop misexpression as a novel type of transcriptomic outlier analysis and extend our understanding of the variety of mechanisms by which genetic variants can influence gene expression.

genetics↗

bioRxiv2023

eQTLs identify regulatory networks and drivers of variation in the individual response to sepsis

Sepsis is a clinical syndrome of life-threatening organ dysfunction caused by a dysregulated response to infection, for which disease heterogeneity is a major obstacle to developing targeted treatments. We have previously identified gene expression-based patient subgroups (Sepsis Response Signatures: SRS) informative for outcome and underlying pathophysiology. Here we aimed to investigate the role of genetic variation in determining the host transcriptomic response and to delineate regulatory networks underlying SRS. Using genotyping and RNA-seq data on 638 adult sepsis patients, we report 16,049 independent expression (eQTLs) and 32 co-expression module (modQTLs) quantitative trait loci in this disease context. We identified significant interactions between SRS and genotype for 1,578 SNP-gene pairs, and combined transcription factor (TF) binding site information (SNP2TFBS) and predicted regulon activity (DoRothEA) to identify candidate upstream regulators. These included HIF1A and CEBPB, which were associated with progenitor and immature neutrophil subsets respectively, further implicating glycolysis and emergency granulopoiesis in SRS1. Overall, these approaches identified putative mechanistic links between host genetic variation, cell subtypes, and the individual transcriptomic response to infection. Understanding the regulatory networks underlying patient heterogeneity provides additional information for developing immunomodulatory treatments and a personalised medicine approach to treating sepsis.

genomics↗

bioRxiv2023

Building pangenome graphs

Pangenome graphs can represent all variation between multiple reference genomes, but current approaches to build them exclude complex sequences or are based upon a single reference. In response, we developed the PanGenome Graph Builder (PGGB), a pipeline for constructing pangenome graphs without bias or exclusion. PGGB uses all-to-all alignments to build a variation graph in which we can identify variation, measure conservation, detect recombination events, and infer phylogenetic relationships.

bioinformatics↗

bioRxiv2022

Galaxy Training: A Powerful Framework for Teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis and stewardship are still rarely taught in life science educational programs [1], resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org); an open access, community-driven framework for the collection of FAIR training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform [2]. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.

bioinformatics↗

bioRxiv2022

An atlas of genetic scores to predict multi-omic traits

Genetically predicted levels of multi-omic traits can uncover the molecular underpinnings of common phenotypes in a highly efficient manner. Here, we utilised a large cohort (INTERVAL; N=50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, N=3,175; Olink, N=4,822), plasma metabolomics (Metabolon HD4, N=8,153), serum metabolomics (Nightingale, N=37,359), and whole blood Illumina RNA sequencing (N=4,136). We used machine learning to train genetic scores for 17,227 molecular traits, including 10,521 which reached Bonferroni-adjusted significance. We evaluated genetic score performances in external validation across European, Asian and African American ancestries, and assessed their longitudinal stability within diverse individuals. We demonstrated the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of UK Biobank to identify disease associations using a phenome-wide scan. Finally, we developed a portal (OmicsPred.org) to facilitate public access to all genetic scores and validation results as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

genomics↗

bioRxiv2022

Planemo: a command-line toolkit for developing, deploying, and executing scientific data analyses

There are thousands of well-maintained high-quality open-source software utilities for all aspects of scientific data analysis. For over a decade, the Galaxy Project has been providing computational infrastructure and a unified user interface for these tools to make them accessible to a wide range of researchers. In order to streamline the process of integrating tools and constructing workflows as much as possible, we have developed Planemo, a software development kit for tool and workflow developers and Galaxy power users. Here we outline Planemos implementation and describe its broad range of functionality for designing, testing and executing Galaxy tools, workflows and training material. In addition, we discuss the philosophy underlying Galaxy tool and workflow development, and how Planemo encourages the use of development best practices, such as test-driven development, by its users, including those who are not professional software developers. Planemo is a mature project widely used within the Galaxy community which has been downloaded over 80,000 times.

bioinformatics↗

bioRxiv2021

LotuS2: An ultrafast and highly accurate tool for amplicon sequencing analysis

BackgroundAmplicon sequencing is an established and cost-efficient method for profiling microbiomes. However, many available tools to process this data require both bioinformatics skills and high computational power to process big datasets. Furthermore, there are only few tools that allow for long read amplicon data analysis. To bridge this gap, we developed the LotuS2 (Less OTU Scripts 2) pipeline, enabling user-friendly, resource friendly, and versatile analysis of raw amplicon sequences. ResultsIn LotuS2, six different sequence clustering algorithms as well as extensive pre- and post-processing options allow for flexible data analysis by both experts, where parameters can be fully adjusted, and novices, where defaults are provided for different scenarios. We benchmarked three independent gut and soil datasets, where LotuS2 was on average 29 times faster compared to other pipelines - yet could better reproduce the alpha- and beta-diversity of technical replicate samples. Further benchmarking a mock community with known taxa composition showed that, compared to the other pipelines, LotuS2 recovered a higher fraction of correctly identified genera and species (98% and 57%, respectively). At ASV/OTU level, precision and F-score were highest for LotuS2, as was the fraction of correctly reconstructed 16S sequences. ConclusionLotuS2 is a lightweight and user-friendly pipeline that is fast, precise and streamlined. High data usage rates and reliability enable high-throughput microbiome analysis in minutes. AvailabilityLotuS2 is available from GitHub, conda or via a Galaxy web interface, documented at http://lotus2.earlham.ac.uk/.

bioinformatics↗

bioRxiv2021

Transcriptome-wide association study in UK Biobank Europeans identifies associations with blood cell traits.

Previous genome-wide association studies (GWAS) of hematological traits have identified over 10,000 distinct trait-specific risk loci, but the underlying causal mechanisms at these loci remain incompletely characterized. We performed a transcriptome-wide association study (TWAS) of 29 hematological traits in 399,835 UK Biobank (UKB) participants of European ancestry using gene expression prediction models trained from whole blood RNA-seq data in 922 individuals. We discovered 557 TWAS signals associated with hematological traits distinct from previously discovered GWAS variants, including 10 completely novel gene-trait pairs corresponding to 9 unique genes. Among the 557 associations, 301 were available for replication in a cohort of 141,286 participants of European ancestry from the Million Veteran Program (MVP). Of these 301 associations, 199 replicated at a nominal threshold ( = 0.05) and 108 replicated at a strict Bonferroni adjusted threshold ( = 0.05/301). Using our TWAS results, we systematically assigned 4,261 out of 16,900 previously identified hematological trait GWAS variants to putative target genes. Compared to coloc, our TWAS results show reduced specificity and increased sensitivity to assign variants to target genes.

genetics↗

bioRxiv2020

Expanding the Galaxy's reference data

SummaryProperly and effectively managing reference datasets is an important task for many bioinformatics analyses. Refgenie is a reference asset management system that allows to easily organize, retrieve, and share such datasets. Here, we describe the integration of refgenie into the Galaxy platform. Server administrators are able to configure Galaxy to make use of reference datasets made available on a refgenie instance. Additionally, a Galaxy Data Manager tool has been developed to provide a graphical interface to refgenies remote reference retrieval functionality. A large collection of reference datasets has also been made available using the CVMFS repository from GalaxyProject.org, with mirrors across the United States, Canada, Europe, and Australia, enabling easy use outside of Galaxy. Availability and implementationThe ability of Galaxy to use refgenie assets was added to the core Galaxy framework in version 20.05, which is available from https://github.com/galaxyproject/galaxy under the Academic Free License version 3.0. The refgenie Data Manager tool can be installed via the Galaxy ToolShed, with source code managed at https://github.com/BlankenbergLab/galaxy-tools-blankenberg/tree/main/data_managers/data_manager_refgenie_pull and released using an MIT license.

bioinformatics↗

bioRxiv2020

A single-cell RNA-seq Training and Analysis Suite using the Galaxy Framework

BackgroundThe vast ecosystem of single-cell RNA-seq tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different software suites. The uptake of 10x Genomics datasets has begun to calm this diversity, and the bioinformatics community leans once more towards the large computing requirements and the statistically-driven methods needed to process and understand these ever-growing datasets. ResultsHere we outline several Galaxy workflows and learning resources for scRNA-seq, with the aim of providing a comprehensive analysis environment paired with a thorough user learning experience that bridges the knowledge gap between the computational methods and the underlying cell biology. The Galaxy reproducible bioinformatics framework provides tools, workflows and trainings that not only enable users to perform one-click 10x preprocessing, but also empowers them to demultiplex raw sequencing from custom tagged and full-length sequencing protocols. The downstream analysis supports a wide range of high-quality interoperable suites separated into common stages of analysis: inspection, filtering, normalization, confounder removal and clustering. The teaching resources cover an assortment of different concepts from computer science to cell biology. Access to all resources is provided at the singlecell.usegalaxy.eu portal. ConclusionsThe reproducible and training-oriented Galaxy framework provides a sustainable HPC environment for users to run flexible analyses on both 10x and alternative platforms. The tutorials from the Galaxy Training Network along with the frequent training workshops hosted by the Galaxy Community provide a means for users to learn, publish and teach scRNA-seq analysis. Key PointsO_LISingle-cell RNA-seq has stabilised towards 10x Genomics datasets. C_LIO_LIGalaxy provides rich and reproducible scRNA-seq workflows with a wide range of robust tools. C_LIO_LIThe Galaxy Training Network provides tutorials for the processing of both 10x and non-10x datasets. C_LI

bioinformatics↗