Biology subjects

Garcia-Ruiz, S.

Publications and source records attributed to Garcia-Ruiz, S..

12 recordsLinked to original sources

bioRxiv2026

IsoMatch enables population-scale transcript integration and reveals reference-dependent isoform novelty

Transcriptome analyses increasingly generate large collections of transcript models that must be integrated, compared and interpreted across samples and reference annotations. Transcript novelty depends on structural equivalence criteria and reference annotation. Here we present IsoMatch, a scalable framework that harmonizes transcript merging and reference-based classification through explicit, configurable structural criteria. IsoMatch preserves source-GTF and original-transcript traceability throughout the workflow, allowing every merged and classified model to be traced back to its contributing inputs. Benchmarking showed that IsoMatch preserves distinct transcript models during integration through configurable matching parameters for both splice junction chains and transcript ends. With its index-based architecture, IsoMatch merged 163 ENCODE transcript catalogs and classified 7.9 million transcript models with peak memory usage of 0.49 GB and 1.1 GB, respectively. Integrating GENCODE, RefSeq and CHESS revealed that 71.2% of transcript groups remained annotation-specific even without terminal constraints. For 4.84 million merged transcript models from 119 long-read transcriptomes from the 1000 Genomes Project, the integrated reference increased the full-splice match proportion from 12.0-13.6% with individual annotations to 18.0%. IsoMatch enables scalable transcript integration and classification with explicit structural criteria and preserved traceability, demonstrating how reference choice shapes estimates of transcript novelty.

bioinformatics↗

bioRxiv2026

SALRR: Scalable Analysis of Long-Read RNA-Seq Enables Comprehensive Transcriptome Profiling in Human Brain

Isoform-resolved transcriptomics is fundamental to decoding the molecular complexity of the human brain, yet population-scale long-read RNA sequencing has remained inaccessible due to labor-intensive library preparation, sensitivity to RNA degradation in postmortem tissue, and the absence of integrated, reproducible analysis pipelines. Here we present SALRR (Scalable Analysis of Long-Read RNA-seq), an integrated wet-lab and computational platform designed to overcome these barriers. Automated ONT long-read cDNA library preparation on the Hamilton Microlab NGS STAR platform reduces hands-on time by 67% and enables 24 libraries per operator per day while maintaining performance across RNA integrity values. A modular, Snakemake-based pipeline performs end-to-end processing from ONT signal data to isoform-level quantification, incorporating SIRV spike-in calibration, multi-stage quality control, and stringent isoform validation. Applied to 10 postmortem frontal cortex samples from the North American Brain Expression Consortium, SALRR identified 31,607 high-confidence isoforms from 10,075 genes, including 8,532 novel splice variants absent from GENCODE v49, and complex splicing events systematically missed by short-read sequencing at neurodegeneration-relevant loci, including GBA1, CCNF, CHCHD10, and TREM2. All protocols and code are openly available, providing a scalable, community-ready framework for isoform-resolved transcriptomics in neurodegeneration, aging, and complex brain disease.

genomics↗

bioRxiv2026

Population-scale interpretation of RNA isoform diversity enabled by Isopedia

Alternative splicing generates extensive transcriptomic complexity, yet "novelty" is often inflated because of incomplete reference annotations, with 20-70% of transcripts in RNA-Seq studies labeled as novel. Isopedia provides an expandable data structure for reference-agnostic isoform annotation, which we demonstrate here through a population-scale catalog of 1,007 long-read datasets spanning 37 diverse biological contexts. By transitioning from reference-dependent to evidence-weighted annotation, Isopedia provides the frequency-based context necessary to distinguish stochastic noise from biologically active isoforms. In HG002 benchmarks, Isopedia reduced apparent isoform novelty by up to 26-fold, achieving a >95% annotation rate even for low-abundance isoforms typically missed by standard catalogs. The framework further supports systematic exploration of challenging loci such as pseudogenes and gene fusions. Isopedia transforms isoform discovery into a systematic interpretation of the human transcriptome, providing a critical foundation for clinical and functional RNA research. Isopedia is open source and freely available: https://github.com/zhengxinchang/isopedia.

bioinformatics↗

bioRxiv2025

Transcriptomic analysis of repeat expansion-ataxias uncovers distinct non-neuronal cell type-specific signatures of disease across the human brain

Hereditary ataxias are a heterogeneous group of neurogenetic conditions characterised by the clinical syndrome of progressive loss of coordination from neurodegeneration of the cerebellum. A commonality across the most prevalent ataxias is the underlying disease mechanism secondary to expansions of short tandem DNA repeats. There is currently an incomplete understanding of the pathogenic mechanisms of these repeat expansion disorders, a core feature of which revolves around RNA-dysregulation. In this study, we used both bulk and single nuclear RNA-sequencing to study post-mortem brain tissue of human donors with a range of repeat-expansion ataxias to reveal further mechanistic insights. We compared post-mortem paired cerebellar and frontal cortex tissue bulk RNA-sequencing data from 23 ataxia patients and 22 sex-, age-matched controls from two brain banks (spinocerebellar ataxia (SCA)1, SCA2, SCA6, SCA7, SCA17, Friedreichs ataxia (FRDA), and 7 cases with unknown molecular diagnoses). We analysed bulk RNA-sequencing data for transcript usage, differential and cell-type-specific expression to transcriptomically profile these diseases. We also generated single nuclear RNA-sequencing data of the cerebellum from donors with SCA1, SCA2, SCA6 and FRDA to decipher changes in cell type proportions in the disease state. Using this approach, we found that: (i) despite the commonalities in the genetics of ataxia, there were components of their transcriptional signatures which were distinct; (ii) there were extensive transcriptional changes evident not only in the cerebellum but also the frontal cortex in ataxia cases; (iii) activation of immune and inflammatory pathways, as well as involvement of non-neuronal cell types was a feature of all ataxias to a lesser or greater extent. This study provides a novel resource to understand the mechanisms of disease in ataxia. Furthermore, taken together, these results highlight immune pathways and the role of non-neuronal cell types as early and potentially important therapeutic targets. These findings provide a map of transcriptomic changes in ataxia to further understanding of the underlying pathogenesis.

neuroscience↗

bioRxiv2024

Network nature of ligand-receptor interactions underlies disease comorbidity in the brain

Neurodegenerative disorders have overlapping symptoms and have high comorbidity rates, but this is not reflected in overlaps of risk genes. We have investigated whether ligand-receptor interactions (LRIs) are a mechanism by which distinct genes associated with disease risk can impact overlapping outcomes. We found that LRIs are likely disrupted in neurological disease and that the ligand-receptor networks associated with neurological diseases have substantial overlaps. Specifically, 96.8% of LRIs associated with disease risk are interconnected in a single LR network. These ligands and receptors are enriched for roles in inflammatory pathways and highlight the role of glia in cross-disease risk. Disruption to this LR network due to disease-associated processes (e.g. differential transcript use, protein misfolding) is likely to contribute to disease progression and risk of comorbidity. Our findings have implications for drug development, as they highlight the potential benefits and risks of pursuing cross-disease drug targets.

neuroscience↗

bioRxiv2024

Alpha-synuclein aggregates trigger anti-viral immune pathways and RNA editing in human astrocytes

RNA editing is a post transcriptional mechanism that targets changes in RNA transcripts to modulate innate immune responses. We report the role of astrocyte specific, ADAR1 mediated RNA editing in neuroinflammation in Parkinsons disease. We generated hiPSC-derived astrocytes, neurons and co-cultures and exposed them to small soluble alpha-synuclein aggregates. Oligomeric alpha-synuclein triggered an inflammatory glial state associated with TLR activation, viral responses, and cytokine secretion. This reactive state resulted in loss of neurosupportive functions, and the induction of neuronal toxicity. Notably, interferon response pathways were activated leading to upregulation, and isoform switching of the RNA deaminase enzyme, ADAR1. ADAR1 mediates A-to-I RNA editing, and increases in RNA editing were observed in inflammatory pathways in cells, as well as in post-mortem human PD brain. Aberrant, or dysregulated, ADAR1 responses and RNA editing may lead to sustained inflammatory reactive states in astrocytes triggered by alpha-synuclein aggregation, and this may drive the neuroinflammatory cascade in Parkinsons.

neuroscience↗

bioRxiv2023

Splicing accuracy varies across human introns, tissues and age

Alternative splicing impacts most multi-exonic human genes. Inaccuracies during this process may have an important role in ageing and disease. Here, we investigated mis-splicing using RNA-sequencing data from ~14K control samples and 42 human body sites, focusing on split reads partially mapping to known transcripts in annotation. We show that mis-splicing occurs at different rates across introns and tissues and that these splicing inaccuracies are primarily affected by the abundance of core components of the spliceosome assembly and its regulators. Using publicly available data on short-hairpin RNA-knockdowns of numerous spliceosomal components and related regulators, we found support for the importance of RNA-binding proteins in mis-splicing. We also demonstrated that age is positively correlated with mis-splicing, and it affects genes implicated in neurodegenerative diseases. This in-depth characterisation of mis-splicing can have important implications for our understanding of the role of splicing inaccuracies in human disease and the interpretation of long-read RNA-sequencing data.

genetics↗

bioRxiv2022

ggtranscript: an R package for the visualization and interpretation of transcript isoforms using ggplot2

MotivationThe advent of long-read sequencing technologies has increased demand for the visualisation and interpretation of transcripts. However, tools that perform such visualizations remain inflexible and lack the ability to easily identify differences between transcript structures. Here, we introduce ggtranscript, an R package that provides a fast and flexible method to visualize and compare transcripts. As a ggplot2 extension, ggtranscript inherits the functionality and familiarity of ggplot2 making it easy to use. Availability and implementationggtranscript is available at https://github.com/dzhang32/ggtranscript, DOI: https://doi.org/10.5281/zenodo.6374061

bioinformatics↗

bioRxiv2021

Detection of pathogenic splicing events from RNA-sequencing data using dasper

Although next-generation sequencing technologies have accelerated the discovery of novel gene-to-disease associations, many patients with suspected Mendelian diseases still leave the clinic without a genetic diagnosis. An estimated one third of these patients will have disorders caused by mutations impacting splicing. RNA-sequencing has been shown to be a promising diagnostic tool, however few methods have been developed to integrate RNA-sequencing data into the diagnostic pipeline. Here, we introduce dasper, an R/Bioconductor package that improves upon existing tools for detecting aberrant splicing by using machine learning to incorporate disruptions in exon-exon junction counts as well as coverage. dasper is designed for diagnostics, providing a rank-based report of how aberrant each splicing event looks, as well as including visualization functionality to facilitate interpretation. We validate dasper using 16 patient-derived fibroblast cell lines harbouring pathogenic variants known to impact splicing. We find that dasper is able to detect pathogenic splicing events with greater accuracy than existing LeafCutterMD or z-score approaches. Furthermore, by only applying a broad OMIM gene filter (without any variant-level filters), dasper is able to detect pathogenic splicing events within the top 10 most aberrant identified for each patient. Since using publicly available control data minimises costs associated with incorporating RNA-sequencing into diagnostic pipelines, we also investigate the use of 504 GTEx fibroblast samples as controls. We find that dasper leverages publicly available data effectively, ranking pathogenic splicing events in the top 25. Thus, we believe dasper can increase diagnostic yield for a pathogenic splicing variants and enable the efficient implementation of RNA-sequencing for diagnostics in clinical laboratories.

bioinformatics↗

bioRxiv2021

Leveraging omic features with F3UTER enables identification of unannotated 3'UTRs for synaptic genes

There is growing evidence for the importance of 3 untranslated region (3UTR) dependent regulatory processes. However, our current human 3UTR catalogue is incomplete. Here, we developed a machine learning-based framework, leveraging both genomic and tissue-specific transcriptomic features to predict previously unannotated 3UTRs. We identify unannotated 3UTRs associated with 1,513 genes across 39 human tissues, with the greatest abundance found in brain. These unannotated 3UTRs were significantly enriched for RNA binding protein (RBP) motifs and exhibited high human lineage-specificity. We found that brain-specific unannotated 3UTRs were enriched for the binding motifs of important neuronal RBPs such as TARDBP and RBFOX1, and their associated genes were involved in synaptic function and brain- related disorders. Our data is shared through an online resource F3UTER (https://astx.shinyapps.io/F3UTER/). Overall, our data improves 3UTR annotation and provides novel insights into the mRNA-RBP interactome in the human brain, with implications for our understanding of neurological and neurodevelopmental diseases.

bioinformatics↗

bioRxiv2021

Human brain mitochondrial-nuclear cross-talk is cell-type specific and is perturbed by neurodegeneration

Mitochondrial dysfunction contributes to the pathogenesis of many neurodegenerative diseases as mitochondria are essential to neuronal function. The mitochondrial genome encodes a small number of core respiratory chain proteins, whereas the vast majority of mitochondrial proteins are encoded by the nuclear genome. Here we focus on establishing a profile of nuclear-mitochondrial transcriptional relationships in healthy human central nervous system tissue data, before examining perturbations of these processes in Alzheimer&#8217s disease using transcriptomic data originating from affected human brain tissue. Through cross-central nervous system analysis of mitochondrial-nuclear gene pair relationships, we find that the cell type composition underlies regional variation, and variation is driven at the subcellular level by heterogeneity of nuclear-mitochondrial coordination in post-synaptic regions. We show that nuclear genes causally implicated in sporadic Parkinson&#8217s disease and Alzheimer&#8217s disease show much stronger relationships with the mitochondrial genome than expected by chance, and that nuclear-mitochondrial relationships are significantly perturbed in Alzheimer&#8217s disease cases, particularly amongst genes involved in synaptic and lysosomal pathways. Finally, we present MitoNuclearCOEXPlorer, a web tool designed to allow users to interrogate and visualise key mitochondrial-nuclear relationships in multi-dimensional brain data. We conclude that mitochondrial-nuclear relationships differ significantly across regions of the healthy brain, which appears to be driven by the functional specialisation of different cell types. We also find that mitochondrial-nuclear co-expression in critical pathways is disrupted in Alzheimer&#8217s disease, potentially implicating the regulation of energy balance and removal of dysfunctional mitochondria in the etiology or progression of the disease and making the case for the relevance of bi-genomic co-ordination in the pathogenesis of neurodegenerative diseases.

genomics↗

bioRxiv2020

Modeling multifunctionality of genes with secondary gene co-expression networks in human brain provides novel disease insights

MotivationCo-expression networks are a powerful gene expression analysis method to study how genes co-express together in clusters with functional coherence that usually resemble specific cell type behaviour for the genes involved. They can be applied to bulk-tissue gene expression profiling and assign function, and usually cell type specificity, to a high percentage of the gene pool used to construct the network. One of the limitations of this method is that each gene is predicted to play a role in a specific set of coherent functions in a single cell type (i.e. at most we get a single for each gene). We present here GMSCA (Gene Multifunctionality Secondary Co-expression Analysis), a software tool that exploits the co-expression paradigm to increase the number of functions and cell types ascribed to a gene in bulk-tissue co-expression networks. ResultsWe applied GMSCA to 27 co-expression networks derived from bulk-tissue gene expression profiling of a variety of brain tissues. Neurons and glial cells (microglia, astrocytes and oligodendrocytes) were considered the main cell types. Applying this approach, we increase the overall number of predicted triplets by 46.73%. Moreover, GMSCA predicts that the SNCA gene, traditionally associated to work mainly in neurons, also plays a relevant function in oligodendrocytes. AvailabilityThe tool is available at GitHub,https://github.com/drlaguna/GMSCA as open source software. ImplementationGSMCA is implemented in R.

bioinformatics↗