Biology subjects

Knowles, D. A.

Publications and source records attributed to Knowles, D. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Bayesian Factor Analysis for Binary and Ordinal Phenotypes with Missingness

Binary and ordinal phenotypes are common in clinical screening and self-reported questionnaires, but many factor analysis and matrix factorization methods are only applicable for quantitative phenotypes with real-valued and/or continuous data distributions. To address this, we propose FABOr (Factor Analysis of Binary and Ordinal data), a Bayesian framework for matrix factorization in which the low-rank matrices are latent variables with continuous priors while the phenotypes are observed variables modeled with appropriate binary/ordinal likelihoods. We also develop missing not at random (MNAR) extensions of FABOr for analyzing data with structured missingness. In experiments with simulated phenotypes, we found that FABOr performs similarly to the best-performing benchmark methods on binary data at imputation and exceeds the performance of all tested benchmark methods on ordinal data. We then applied FABOr to analyze real-world binary and ordinal phenotypes from the Simons Foundation SPARK dataset on autism spectrum disorder (ASD) and found that it improved imputation accuracy by up to 5% on binary data and up to 23% on ordinal data relative to the benchmark methods.

bioinformatics↗

bioRxiv2026

LRP2: A proteogenomics pipeline for long-read informed protein isoform analysis and discovery

Most human genes produce multiple RNA isoforms, yet it remains unclear which isoforms are translated into stable, functional proteins. Long-read RNA sequencing resolves full-length transcript structures and, when paired with mass spectrometry, can provide empirical evidence of isoform translation. Despite this opportunity, comprehensive workflows integrating isoform discovery, open reading frame prediction, peptide identification, and protein inference remain limited, leaving users to handle these steps piecemeal. Here, we present LRP2, a modular, end-to-end long-read proteogenomics pipeline built in Nextflow. LRP2 scales transcript discovery to hundreds of samples via PacBio's latest Isocall tool, removes technical artifacts with SQANTI QC, generates and classifies predicted proteomes via CPAT and SQANTI Protein, performs multi-group differential expression and usage analysis via edgeR, DRIMSeq, and a long-read adaptation of LeafCutter, and integrates protein-level evidence from DDA and DIA MS data through FragPipe. For cross-dataset comparison of novel isoforms, LRP2 employs deterministic splice-junction, coordinate-based isoform identifiers. Used as an integrated pipeline, LRP2 enables the detection of novel peptides and improves the protein isoform inference to confirm protein isoform translation.

bioinformatics↗

bioRxiv2026

CLADES - Contrastive Learning Augmented DifferEntial Splicing with Orthologous Positive Pairs

Alternative splicing (AS) reshapes transcript and protein repertoires across biological, e.g. cellular, contexts. However, learning sequence [->] content-specific splicing mappings is challenging due to limited labels across tissues and cell types and variability introduced by experimental protocols. We propose a contrastive representation learning pre-training approach grounded in evolutionary conservation. Orthologous exon-intron junction sequences are treated as semantically consistent views of the same regulatory program: evolutionary orthologs are positive pairs, non-homologous junctions are negatives. This discriminative objective aligns embeddings of regulatory equivalents while separating functionally unrelated sequences, inducing invariances to unconstrained sequence and emphasizing conserved motif/RBP and positional signals. We show that this pre-training strategy provides representations that help predict{Delta}{psi} , the change in exon inclusion between conditions, which encodes both direction and magnitude of splicing shifts. Specifically, we finetune a lightweight supervised head on available labels to predict{Delta}{psi} . To make these predictions biologically meaningful, we further introduce an interpretable, splice-motif-aware classification framework grounded in known regulatory signals. On benchmarks spanning tissue- and cell-type differential splicing, the learned representations yield strong{Delta}{psi} classification performance (AUPRC/AUROC for increased/decreased inclusion) and competitive results for regression (RMSE, Spearman). These findings indicate that evolution-as-augmentation, instantiated via contrastive learning, is an effective and biologically principled route to context-resolved splicing prediction.

genomics↗

bioRxiv2025

Aging-associated alternative splicing programs conserved between human and mouse tissues

Alternative splicing is a key gene regulatory process that diversifies the proteome and controls gene dosage. Previous studies have detected aging-associated splicing changes across various tissues. However, their use of bulk RNA-seq obfuscates the impacted cell-types and may confound cell-type proportion changes with cell-intrinsic ones. We present a framework that first assembles and maps alternative splicing events in appropriate single cell RNA-seq data (scRNA-seq), then applies LeafletFA, a probabilistic model that discovers coordinated splicing programs (SPs) without requiring prior knowledge of cell types or clinical information, such as age. Applying this framework to over 200,000 cells from mouse and human Smart-seq2 multi-tissue atlases, we discovered global and cell type specific aging-associated SPs. In mice, integrating SPs with gene expression significantly improved age prediction in 46 of 76 tissue-cell types tested. We identified the RNA-binding protein Snrnp70 as a key upstream mediator driving the shift from youthful to aged SPs. To test evolutionary conservation of these patterns, we employed transfer learning to map the murine splicing dictionary onto human transcriptomes. This revealed a conserved "youth" program (SP4) that is maintained in quiescent endothelial tissues but lost in high-turnover organs. We identified a conserved, age-dependent alternative 5 splice site usage in the splicing factor SRSF5 as a molecular marker of this program in both species. Our work establishes alternative splicing as a coordinated, evolutionarily conserved dimension of cellular aging and provides LeafletFA as an open-source tool for single-cell splicing analysis.

systems biology↗

bioRxiv2025

Robust Integration of Sparse Single-Cell Alternative Splicing and Gene Expression Data with SpliceVI

Alternative splicing (AS) and gene expression (GE) are tightly related regulatory processes, critical for defining cell types and states, yet are rarely modeled together in single-cell analyses. This hinders a comprehensive understanding of cellular identity. We address this by introducing SpliceVI, adapted from MultiVI (Multi-modal Variational Inference) to specifically handle AS. Applied to a large multisample mouse Smart-seq2 dataset (n = 142, 315 cells/nuclei), SpliceVI jointly learns from both AS and GE using a partial variational autoencoder that effectively handles the sparsity and missingness of splicing data. We show that SpliceVIs joint embeddings are more expressive and informative of biological correlates like age than a GE-only approach (scVI). SpliceVI also uncovers splicingbased differences between neuronal subclusters. This approach reveals the distinct yet synergistic relationship between AS and GE in shaping cellular diversity in mouse.

systems biology↗

bioRxiv2025

Mapping transcriptional responses to cellular perturbation dictionaries with RNA fingerprinting

Single-cell perturbation dictionaries provide systematic measurements of how cells respond to genetic and chemical perturbations, and create the opportunity to assign causal interpretations to observational data. Here, we introduce RNA fingerprinting, a statistical framework that maps transcriptional responses from new experiments onto reference perturbation dictionaries. RNA fingerprinting learns denoised perturbation "fingerprints" from single-cell data, then probabilisti-cally assigns query cells to one or more candidate perturbations while accounting for uncertainty. We benchmark our method across ground-truth datasets, demonstrating accurate assignments at single-cell resolution, scalability to genome-wide screens, and the ability to resolve combinatorial perturbations. We demonstrate its broad utility across diverse biological settings: identifying context-specific regulators of p53 under ribosomal stress, characterizing drug mechanisms of action and dose-dependent off-target effects, and uncovering cytokine-driven B cell heterogeneity during secondary influenza infection in vivo. Together, these results establish RNA fingerprinting as a versatile framework for interpreting single-cell datasets by linking cellular states to the underlying perturbations which generated them.

genomics↗

bioRxiv2025

Perplexity as a Metric for Isoform Diversity in the Human Transcriptome

Characterizing the extensive isoform diversity revealed by long-read RNA-sequencing remains challenging. After removal of technical artifacts, existing pipelines apply arbitrary expression thresholds that filter out bona fide transcript structures, misrepresenting diversity and hindering reproducibility. Instead of discarding isoforms, we propose a fundamentally distinct approach to quantifying isoform diversity using perplexity-the effective number of isoforms for a gene, derived from Shannon entropy-wherein every isoform, including low-abundance ones, contributes proportionally to a genes diversity. Analyzing 124 ENCODE4 PacBio datasets spanning 55 human cell types, we show that perplexity provides interpretable and reproducible isoform diversity measurements across genes, regulatory levels, and tissues.

genomics↗

bioRxiv2025

A computational framework for mapping isoform landscape and regulatory mechanisms from spatial transcriptomics data

Transcript diversity including splicing and alternative 3 end usage is crucial for cellular identity and adaptation, yet its spatial coordination remains poorly understood. Here, we present SPLISOSM (SpatiaL ISOform Statistical Modeling), a computational framework for detecting isoform-resolution patterns from spatial transcriptomics data. SPLISOSM leverages multivariate testing to account for spot- and isoform-level dependencies, demonstrating robust and theoretically grounded performance on sparse data. In the mouse brain, we identify over 1,000 spatially variable transcript diversity events, primarily in synaptic signaling pathways linked to neuropsychiatric disorders, and uncover both known and novel regulatory relationships with region-specific RNA binding proteins. We further show that these patterns are evolutionarily conserved between mouse and human prefrontal cortex. Analysis of human glioblastoma highlights pervasive transcript diversity in antigen presentation and adhesion genes associated with specific microenvironmental conditions. Together, we present a comprehensive spatial splicing analysis in the brain under normal and neoplastic conditions.

bioinformatics↗

bioRxiv2025

Oncogenic mutations disrupt lineage differentiation via epigenetic perturbations

Understanding lineage differentiation is an ongoing challenge in the field of stem cell research. We acquired eight gain-of-function (GOF) oncogenic mutations associated with DNA methylation alteration from the TCGA datasets. After introducing each oncogene into wild-type stem cells separately, we found that the mutant stem cells are difficult to reprogram to iPSCs and terminally differentiate to induced neuronal cells, and retain proliferation capacity in the process of differentiation. This findings suggests that oncogenic mutations impair the lineage differentiation. After analyzing the resulting DNA methylation profiles, we identified many novel lineage differentiation genes in mutant cells showing DNA methylation alteration compared with wild-type cells, which is consistent with their methylation states in low-grade gliomas (LGGs) compared with control brain. Collectively, our results indicate that oncogenic mutations in stem cells are associated with DNA methylation changes for genes related to multipotency maintenance, differentiation resistance, and tumorigenesis, therefore, displaying impairment in lineage commitment.

cell biology↗

bioRxiv2025

Genome-wide mapping of mesoscale neuronal RNA organization and condensation

Subcellular RNA organization can affect critical cellular functions. However, our understanding of RNA microenvironments, particularly biomolecular condensates, remains limited, largely due to a lack of technologies to comprehensively interrogate mesoscale RNA organization. Here, we adapt Split-Pool Recognition of Interactions by Tag Extension to map micron-scale RNA-RNA spatial proximity genome-wide across cell regions (RNA-SPRITE). Deploying RNA-SPRITE, we find extensive, conserved organization of mature mRNAs, with increased colocalization between mRNAs that share RNA-binding protein (RBP) motifs or encode functionally related proteins. Both effects are especially strong in dendrites and axons, suggesting prevalent mRNA co-regulation. Moreover, mRNAs with less compact folding, lower translation efficiency, and specific RBP motifs are more likely to be in RNA-rich condensates. However, perturbations that broadly dissolve or enhance condensation reveal that RBP motif and encoded protein-mediated colocalizations largely remain intact, independent of condensation. These results demonstrate the power of RNA-SPRITE in revealing critical aspects of RNAs functional organization. In BriefUnbiased, genome-wide maps of RNA-RNA mesoscale spatial proximity uncover extensive subcellular organization and its governing principles. HighlightsO_LIRNA-SPRITE reveals micron-scale RNA colocalization genome-wide across cell regions C_LIO_LImRNA colocalization specificity is driven by shared motifs and encoded protein function C_LIO_LImRNAs with less compact folding, lower translation efficiency, and distinct protein-binding motifs are more likely to be in condensates C_LIO_LINeurites have a particularly high degree of sequence and function-dependent mRNA organization C_LI

molecular biology↗

bioRxiv2025

Inferring fungal cis-regulatory networks from genome sequences

Gene expression patterns are determined to a large extent by transcription factor binding to non-coding regulatory regions in the genome. However, gene expression cannot yet be systematically predicted from genome sequences, in part because non-functional matches to the sequence patterns (motifs) recognized by transcription factors (TFs) occur frequently throughout the genome. Large-scale functional genomics data for many TFs has enabled characterization of regulatory networks in experimentally accessible cells such as budding yeast. Beyond yeast, fungi are important industrial organisms and pathogens, but large-scale functional data is only sporadically available. Uncharacterized regulatory networks control key pathways and gene expression programs associated with fungal phenotypes. Here we explore a sequence-only approach to inferring regulatory networks by leveraging the 100s of genomes now available for many clades of fungi. We use gene orthology as the learning signal to infer interpretable, TF motif-based representations of non-coding regulatory regions. Using these representations to identify conserved signals for motifs, comparative genomics can be scaled to evolutionary comparisons where sequence similarity cannot be detected. We show that similarity of these conserved motif signals predicts gene expression and regulation better than using experimental data, and that we can infer known and novel regulatory connections in diverse fungi. Our new predictions include a pathway for recombination in C. albicans and pathways for mating and an RNAi immune response in Neurospora. Taken together, our results indicate that specific hypotheses about transcriptional regulation in fungi can be obtained for many genes from genome sequence analysis alone.

systems biology↗

bioRxiv2025

Multi-sample, multi-platform isoform quantification using empirical Bayes

Accurate quantification of RNA isoform abundance is crucial for understanding gene regulation, cellular behavior, and disease mechanisms. While short-read (SR) sequencing provides high-throughput and cost-effective transcript quantification, it suffers from read-to-transcript ambiguity. Long-read (LR) sequencing reduces this ambiguity but faces challenges such as high error rates, biases, and lower throughput. Existing methods rely on either SR or LR data and operate on single or merged samples, failing to leverage the variability across multiple samples and the complementary strengths of both technologies. As a result, they struggle to accurately quantify low-abundance and moderate-expressed isoforms and often require complex models for sample-specific bias correction. To address these limitations, we introduce JOLI, a hierarchical model that leverages multi-sample learning to enhance transcript quantification by jointly integrating SR and LR sequencing data. By incorporating multi-sample learning, JOLI captures shared transcript structures, corrects for systematic biases, and enhances statistical power, particularly for low- and moderate-abundance isoforms. Our model applies an empirical Bayes framework, learning a shared prior across samples to improve inference consistency. By jointly modeling SR and LR data, it integrates the strengths of both technologies, achieving higher accuracy and reproducibility in transcript quantification. Through benchmarking on simulated and real RNA-seq datasets, we show that JOLI consistently outperforms single-sample EM method by improving ranking consistency, proportional agreement, and estimation accuracy while enhancing reproducibility. Specifically, in simulations, JOLI multi-sample improves Spearman correlation by 9.8% for LR and 7.7% for SR data compared to single-sample method, while for real data, the improvements are 2.56% (LR) and 1.28% (SR), respectively. Multi-sample learning further improves the quantification of isoforms with low to moderate expression levels. Furthermore, JOLI performs competitively with state-of-the-art methods, highlighting its robustness in transcript quantification.

bioinformatics↗

bioRxiv2024

Alternating hemiplegia of childhood associated mutations in Atp1a3 reveal diverse neurological alterations in mice.

Pathogenic variants in the neuronal Na+/K+ ATPase transmembrane ion transporter (ATP1A3) cause a spectrum of neurological disorders including alternating hemiplegia of childhood (AHC). The most common de novo pathogenic variants in AHC are p.D801N ([~]40% of patients) and p.E815K ([~]25% of patients), which lead to early mortality by spontaneous death in mice. Nevertheless, knowledge of the development of clinically relevant neurological phenotypes without the obstacle of premature death, is critical for the identification of pathophysiological mechanisms and ultimately, for the testing of therapeutic strategies in disease models. Here, we used hybrid vigor attempting to mitigate the fragility of AHC mice and then, performed behavioral, electrophysiological, biochemical, and molecular testing to comparatively analyze mice that carry either of two most common AHC patient observed variants in the Atp1a3 gene. Collectively, our data reveal the presence but also the differential impact of the p.D801N and p.E815K variants on disease relevant alterations such as spontaneous and stress-induced paroxysmal episodes, motor function, behavioral and neurophysiological activity, and neuroinflammation. Our alternate AHC mouse models with their phenotypic deficits open novel avenues for the investigation of disease biology and therapeutic testing for ATP1A3 research.

neuroscience↗

bioRxiv2023

DIISCO: A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data

Characterizing cell-cell communication and tracking its variability over time is essential for understanding the coordination of biological processes mediating normal development, progression of disease, or responses to perturbations such as therapies. Existing tools lack the ability to capture time-dependent intercellular interactions, such as those influenced by therapy, and primarily rely on existing databases compiled from limited contexts. We present DIISCO, a Bayesian framework for characterizing the temporal dynamics of cellular interactions using single-cell RNA-sequencing data from multiple time points. Our method uses structured Gaussian process regression to unveil time-resolved interactions among diverse cell types according to their co-evolution and incorporates prior knowledge of receptor-ligand complexes. We show the interpretability of DIISCO in simulated data and new data collected from CAR-T cells co-cultured with lymphoma cells, demonstrating its potential to uncover dynamic cell-cell crosstalk. AvailabilityDIISCO is publicly accessible at https://github.com/azizilab/DIISCO_public. All data will be deposited to GEO upon publication.

bioinformatics↗

bioRxiv2023

Investigating RNA splicing as a source of cellular diversity using a binomial mixture model

Alternative splicing (AS) contributes significantly to RNA and protein variability yet its role in defining cellular diversity is not fully understood. While Smart-seq2 offers enhanced coverage across transcripts compared to 10X single cell RNA-sequencing (scRNA-seq), current computational methods often miss the full complexity of AS. Most approaches for single cell based differential splicing analysis focus on simple AS events such as exon skipping, and rely on predefined cell type labels or low-dimensional gene expression representations. This limits their ability to detect more complex AS events and makes them dependent on prior knowledge of cell classifications. Here, we present Leaflet, a splice junction centric approach inspired by Leafcutter, our tool for quantifying RNA splicing variation with bulk RNA-seq. Leaflet is a probabilistic mixture model designed to infer AS-driven cell states without the need for cell type labels. We detail Leaflets generative model, inference methodology, and its efficiency in detecting differentially spliced junctions. By applying Leaflet to the Tabula Muris brain cell dataset, we highlight cell-state specific splicing patterns, offering a deeper insight into cellular diversity beyond that captured by gene expression alone.

systems biology↗

bioRxiv2023

Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits

Inference of directed biological networks is an important but notoriously challenging problem. We introduce inverse sparse regression (inspre), an approach to learning causal networks that leverages large-scale intervention-response data. Applied to 788 genes from the genome-wide perturb-seq dataset, inspre helps elucidate the network architecture of blood traits.

genetics↗

bioRxiv2023

Cas13d-mediated isoform-specific RNA knockdown with a unified computational and experimental toolbox

Alternative splicing is an essential mechanism for diversifying proteins, in which mature RNA isoforms produce proteins with potentially distinct functions. Two major challenges in characterizing the cellular function of isoforms are the lack of experimental methods to specifically and efficiently modulate isoform expression and computational tools for complex experimental design. To address these gaps, we developed and methodically tested a strategy which pairs the RNA-targeting CRISPR/Cas13d system with guide RNAs that span exon-exon junctions in the mature RNA. We performed a high-throughput essentiality screen, quantitative RT-PCR assays, and PacBio long read sequencing to affirm our ability to specifically target and robustly knockdown individual RNA isoforms. In parallel, we provide computational tools for experimental design and screen analysis. Considering all possible splice junctions annotated in GENCODE for multi-isoform genes and our gRNA efficacy predictions, we estimate that our junction-centric strategy can uniquely target up to 89% of human RNA isoforms, including 50,066 protein-coding and 11,415 lncRNA isoforms. Importantly, this specificity spans all splicing and transcriptional events, including exon skipping and inclusion, alternative 5 and 3 splice sites, and alternative starts and ends.

genomics↗

bioRxiv2022

A Unified Modular Framework to Incorporate Structural Dependency in Spatial Omics Data

Spatial omics technologies can help identify spatially organized biological processes, but existing computational approaches often overlook structural dependencies in the data. Here, we introduce Smoother, a unified framework that integrates positional information into non-spatial models via modular priors and losses. In simulated and real datasets, Smoother enables accurate data imputation, cell-type deconvolution, and dimensionality reduction with remarkable efficiency. In colorectal cancer, Smoother-guided deconvolution revealed plasma cell and fibroblast subtype localizations linked to tumor microenvironment restructuring. Additionally, joint modeling of spatial and single-cell human prostate data with Smoother allowed for spatial mapping of reference populations with significantly reduced ambiguity.

bioinformatics↗