Biology subjects

Morrison, J.

Publications and source records attributed to Morrison, J..

18 recordsLinked to original sources

bioRxiv2026

TIM3+ Tumor Associated M2 Macrophages Impair Antitumor T Cell Immunity and Promote Gastric Cancer Progression and Peritoneal Metastasis

Peritoneal metastases (PM) are the leading cause of cancer-related death in gastric cancer (GC) patients with survival typically < 9 months. Here, we demonstrate that TIM3 and its ligands are increased along the GC continuum and associated with poor survival. Integrated omics analyses and functional studies revealed highly enriched TIM3 in CD163+ tumor associated M2 immunosuppressive macrophages significantly promote tumor cell invasion and tumor growth in vivo, while TIM3 depletion in macrophages reduced tumor cell malignant attributes and increased T cell immunity from PBMCs or CD45+ immune cells of malignant ascites in co-culture system. By cytokine and kinase arrays, we discovered that depletion of TIM3 in macrophages reduced the production of notable secretome of cytokines/chemokines from M2 macrophages; and the protumor function of TIM3+ macrophages rely on the p90RSK1/2/CCL20 axis. Finally, we reveal that TIM3 blockage or genetic KO had superior antitumor activity in combination with anti-PD1 immunotherapy and mitomycin C (MMC) chemotherapy. Together, this study uncovers an important role for TIM3 in tumor associated M2 macrophages and underscores the potential of TIM3 blockage in GC patients with PM. Statement of significanceIn this study, we show TIM3 increases along GC continuum, and highly enriched on tumor associated M2 macrophages that fuel tumor growth; and suppress T cell function via p90RSK1/2/CCL20 axis. TIM3 depletion restores T-cell immunity and curbs tumor growth. TIM3 blockade combined with anti-PD1 and mitomycin C provide a novel therapeutic strategy for GC patients with PM.

immunology↗

bioRxiv2025

Tranquillyzer: A Flexible Neural Network Framework for Structural Annotation and Demultiplexing of Long-Read Transcriptomes

Long-read single-cell RNA sequencing using platforms such as Oxford Nanopore Technologies (ONT) enables full-length transcriptome profiling at single-cell resolution. However, high sequencing error rates, diverse library architectures, and increasing dataset scale introduce major challenges for accurately identifying cell barcodes (CBCs) and unique molecular identifiers (UMIs) - key prerequisites for reliable demultiplexing and deduplication, respectively. Existing pipelines rely on hard-coded heuristics or local transition rules that cannot fully capture this broader structural context and often fail to robustly interpret reads with indel-induced shifts, truncated segments, or non-canonical element ordering. We introduce Tranquillyzer (TRANscript QUantification In Long reads-anaLYZER), a flexible, architecture-aware deep learning framework for processing long-read single-cell RNA-seq data. Tranquillyzer employs a hybrid neural network architecture and a global, context-aware design, and enables precise identification of structural elements - even when elements are shifted, partially degraded, or repeated due to sequencing noise or library construction variability. In addition to supporting established single-cell protocols, Tranquillyzer accommodates custom library formats through rapid, one-time model training on user-defined label schemas, typically completed within a few hours on standard GPUs. Additional features such as scalability across large datasets and comprehensive visualization capabilities further position Tranquillyzer as a flexible and scalable framework solution for processing long-read single-cell transcriptomic datasets.

bioinformatics↗

bioRxiv2025

Synthbar: A Lightweight Tool for Adding Synthetic Barcodes to Sequencing Reads

Preparation of single-cell sequencing libraries includes adding nucleotide barcodes to assist with pooling samples or cells together for sequencing. The popularity of droplet-based single-cell protocols has spurred the development of computational tools that expect the read structure of the assay to include a cell barcode (CB). Microwell plate-based protocols, such as the Switching Mechanism At the 5 end of the RNA Transcript (SMART) single-cell RNA sequencing (scRNA-seq) family of methods, typically do not add a CB as part of the library preparation method as there is typically one cell per well and standard unique dual indices are sufficient for multiplexing. While several tools exist to manipulate and parse varying single-cell read structures, no tool is currently available to easily add synthetic CBs to enable use of computational tooling that expects the presence of a CB, such as STARsolo, zUMIs, and Alevin. Synthbar fills this gap as a lightweight tool that is assay agnostic, can add user-defined CBs, and modify read structures. Availability and ImplementationSource code and binaries are freely available at https://github.com/jamorrison/synthbar under the MIT license. Synthbar is implemented in C and is supported on macOS and Linux. Supplementary informationSupplementary data are available at the end of the document.

bioinformatics↗

bioRxiv2025

Device encapsulated MSCs for adaptive secretome therapy to effectively target ischaemic heart injury

Effective long-term strategies to protect the ischaemic heart remain a significant challenge. Mesenchymal stem cells (MSCs) offer therapeutic potential primarily through their secretome, a bioactive factor-rich milieu with broad beneficial effects. However, existing delivery methods have not been shown to provide sustained benefits. Herein, we introduce an innovative approach for sustained MSC-secretome delivery for long-term cardioprotection. In a rat model of chronic myocardial infarction, Cymerus MSCs, derived from human induced pluripotent stem cells (iPSCs), were encapsulated in a Procyon immunoisolation device and implanted subcutaneously. A human-iPSC-derived engineered cardiac microtissue model was used to simulate ischaemia-reperfusion injury and assess cardioprotective effects in a human context. The MSC-loaded Procyon device significantly improved cardiac function and reduced adverse left ventricular remodelling over a 12-week period. These positive effects were consistent across both young and middle-aged, male and female rats, indicating broad applicability. The encapsulated MSCs remained viable and continuously released therapeutic secretome for 12 weeks. In vitro, the MSC secretome protected human engineered cardiac microtissues from simulated ischaemia-reperfusion injury, restoring contractile function, improving cell viability, and reducing oxidative stress. Proteomic analysis of MSCs revealed 179 unique cellular proteins post-implantation, linked to adaptive immune and inflammatory responses as well as wound healing. MSC secretome profiling revealed increased protein diversity associated with tissue repair and immune regulation, suggesting MSCs undergo an adaptive response to ischaemic conditions, enhancing their therapeutic potential. This translational study highlights a clinically viable, minimally invasive method for sustained cardioprotection, harnessing the MSC secretome to address a pivotal gap in current treatments for ischaemic heart disease.

pathology↗

bioRxiv2025

Multivariable Mendelian Randomization adjusting for heritable confounding analyzes the causal effects of C-reactive protein on multiple diseases

BackgroundC-reactive protein (CRP) is a marker of inflammation associated with autoimmune, cardiovascular, and neuropsychiatric disorders. However, it remains unclear whether CRP causally affects these traits or if observed associations result from reverse causation or confounding. Mendelian randomization (MR) uses genetic variants as instrumental variables to estimate causal effects and avoid the biases present in observational studies. Prior MR studies have suggested causal effects of CRP on several traits, including schizophrenia, bipolar disorder, and colorectal cancer. However, MR may produce biased results if factors that confound the exposure and outcome are heritable, resulting in horizontal pleiotropy. This is a major concern for studies of CRP, because CRP levels may increase in response to inflammation caused by a wide range of heritable conditions. MethodsMultivariable Mendelian randomization (MVMR) can be used to eliminate bias from heritable confounding when GWAS summary data are available for confounders. In this study, we use MVMR to estimate the causal effects of CRP on 12 outcomes with prior evidence of a causal or associational link to CRP. We use a novel computational pipeline to identify a broad set of potential heritable confounders between CRP and each outcome trait from studies in the MRC-IEU OpenGWAS database. We compare MVMR results with computationally selected confounders to univariable MR results and MVMR using a narrower, literature derived set of confounders. ResultsWe find that univariable MR suggests evidence of a potential risk-increasing effect of CRP on coronary artery disease, knee osteoarthritis, and rheumatoid arthritis, and a protective effect on schizophrenia. However, after adjusting for computationally selected heritable confounders, only the causal effects on rheumatoid arthritis (OR 1.18, 95% CI [1.07,1.31], p=0.0010 by GRAPPLE) and schizophrenia (OR 0.87, 95% CI [0.79,0.96], p=0.0038 by GRAPPLE) remain significant. Additionally, after adjusting for confounders we find evidence of a potential protective effect of CRP on bipolar disorder at the nominal significance level, which is not observed in the univariable analysis. ConclusionThese results suggest that univariable MR analyses of CRP may be biased by high levels of heritable confounding, though CRP may indeed play a causal role in development of some diseases, potentially mediated by its role in innate immunity. These results also high-light the potential for automatic confounder selection to improve the robustness of Mendelian randomization analyses. Key MessagesO_LIWe used multivariable Mendelian randomization to estimate the causal effect of CRP levels on various diseases after adjusting for heritable confounders. C_LIO_LIWe proposed a novel computational pipeline for phenome-wide heritable confounder selection using traits from the MRC-IEU OpenGWAS database. C_LIO_LIOur study did not find evidence of a causal effect of CRP on multiple diseases, except rheuma-toid arthritis and schizophrenia. C_LIO_LIOur study highlights the role of CRP as an indicator of inflammation rather than a causal factor in disease risk, suggesting that previous MR analyses may have been biased by heritable confounding. C_LI

genetics↗

bioRxiv2024

Pathway Polygenic Risk Scores (pPRS) for the Analysis of Gene-environment Interaction

A polygenic risk score (PRS) is used to quantify the combined disease risk of many genetic variants. For complex human traits there is interest in determining whether the PRS modifies, i.e. interacts with, important environmental (E) risk factors. Detection of a PRS by environment (PRS x E) interaction may provide clues to underlying biology and can be useful in developing targeted prevention strategies for modifiable risk factors. The standard PRS may include a subset of variants that interact with E but a much larger subset of variants that affect disease without regard to E. This latter subset will water down the underlying signal in former subset, leading to reduced power to detect PRS x E interaction. We explore the use of pathway-defined PRS (pPRS) scores, using state of the art tools to annotate subsets of variants to genomic pathways. We demonstrate via simulation that testing targeted pPRS x E interaction can yield substantially greater power than testing overall PRS x E interaction. We also analyze a large study (N=78,253) of colorectal cancer (CRC) where E = non-steroidal anti-inflammatory drugs (NSAIDs), a well-established protective exposure. While no evidence of overall PRS x NSAIDs interaction (p=0.41) is observed, a significant pPRS x NSAIDs interaction (p=0.0003) is identified based on SNPs within the TGF-{beta} / gonadotropin releasing hormone receptor (GRHR) pathway. NSAIDS is protective (OR=0.84) for those at the 5th percentile of the TGF-{beta}/GRHR pPRS (low genetic risk, OR), but significantly more protective (OR=0.70) for those at the 95th percentile (high genetic risk). From a biological perspective, this suggests that NSAIDs may act to reduce CRC risk specifically through genes in these pathways. From a population health perspective, our result suggests that focusing on genes within these pathways may be effective at identifying those for whom NSAIDs-based CRC-prevention efforts may be most effective. Author SummaryThe identification of polygenic risk score (PRS) by environment (PRSxE) interactions may provide clues to underlying biology and facilitate targeted disease prevention strategies. The standard approach to computing a PRS likely includes many variants that affect disease without regard to E, reducing power to detect PRS x E interactions. We utilize gene annotation tools to develop pathway-based PRS (pPRS) scores and show by simulation studies that testing pPRS x E interaction can yield substantially greater power than testing PRS x E, while also integrating biological knowledge into the analysis. We apply our method to a large study of colorectal cancer to identify a significant pPRS x NSAIDs interaction (p=0.0003) based on SNPs within the TGF-{beta} / gonadotropin releasing hormone receptor (GRHR) pathway. Our findings suggest that focusing on genetic susceptibility within biologically informed pathways may be more sensitive for identifying exposures that can be considered as part of a precision prevention approach.

genetics↗

bioRxiv2024

Probabilistic Fine-mapping of Putative Causal Genes

Integrative genetic analysis of molecular and complex trait data, including colocalization analysis and transcriptome-wide association studies (TWAS), has shown promise in linking GWAS findings to putative causal genes (PCGs) underlying complex diseases. However, existing methods have notable limitations: TWAS tend to produce an excess of false-positive PCGs, while colocalization analysis often lacks sufficient statistical power, resulting in many false negatives. This paper introduces a probabilistic fine-mapping method, INTERFACE, which is designed to identify putative causal genes while accounting for direct variant-to-trait effects within genomic regions harboring multiple gene candidates. INTERFACE lever-ages interpretable, data-informed priors that incorporate both colocalization and TWAS evidence, enhancing the sensitivity and specificity of PCG inference and setting it apart from existing methods. Additionally, INTERFACE implements analytical measures to improve the accuracy of gene-to-trait effect estimation. We apply INTERFACE to METSIM plasma metabolite GWASs and UK Biobank pQTL data to identify causal genes regulating blood metabolite levels and demonstrate the unique biological insights INTERFACE provides.

genetics↗

bioRxiv2024

High-coverage allele-resolved single-cell DNA methylation profiling by scDEEP-mC reveals cell lineage, X-inactivation state, and replication dynamics

DNA methylation is a relatively stable epigenetic mark with important roles in development and disease.1 Since cell-to-cell variation in epigenetic programming can reflect important differences in cell state and fate, it is clear that single-cell methods are essential to understanding this key epigenetic mark in heterogeneous tissues. Existing single-cell whole-genome bisulfite sequencing (scWGBS) methods2 have significant shortcomings, including very low CpG coverage3-7 or inefficient library generation requiring extremely deep sequencing.8 These methods offer limited insight into focal regulatory regions and generally preclude direct cell-to-cell comparisons. To address these shortcomings, we have developed an improved method, scDEEP-mC (single-cell Deep and Efficient Epigenomic Profiling of methyl-C). We show that high-coverage promoter methylation profiling by scDEEP-mC can identify multiple cell types, while allele-resolved methylation calls allow assessment of X-inactivation state in single cells and identification of transcription factor binding sites (TFBS) enriched for hemi-methylation. We also use scDEEP-mC to profile single-cell copy number alterations, identify actively replicating cells, and track DNA methylation dynamics during and after DNA replication. The high coverage of scDEEP-mC creates an exceptional opportunity to explore DNA methylation biology in individual cells.

genomics↗

bioRxiv2024

GWASBrewer: An R Package for Simulating Realistic GWAS Summary Statistics

1Many statistical genetics analysis methods make use of GWAS summary statistics. Best statistical practice requires evaluating these methods in simulations against a known truth. Ideally, these simulations should be as realistic as possible. However, simulating summary statistics by first simulating individual genotype and phenotype data is extremely computationally demanding, especially when large sample sizes or many traits are required. We present GWASBrewer, an open source R package for direct simulation of GWAS summary statistics. We show that statistics simulated by GWASBrewer have the same distribution as statistics generated from individual level data, and can be produced at a fraction of the computational expense. Additionally, GWASBrewer can simulate standard error estimates, something that is typically not done when sampling summary statistics directly. GWASBrewer is highly flexible, allowing the user to simulate data for multiple traits connected by causal effects and with complex distributions of effect sizes. We demonstrate example uses of GWASBrewer for evaluating Mendelian randomization, polygenic risk score, and heritability estimation methods.

genetics↗

bioRxiv2024

Integrative analysis of the genome, transcriptome, and proteome identifies causal mechanisms of complex traits.

We present multi-integration of transcriptome-wide association studies and colocalization (Multi-INTACT), an algorithm that models multiple gene products (e.g. encoded RNA transcript and protein levels) to implicate causal genes and relevant gene products. In simulations, Multi-INTACT achieves higher power than existing methods, maintains calibrated false discovery rates, and detects the true causal gene product(s). We apply Multi-INTACT to GWAS on 1,408 metabolites, integrating the GTEx expression and UK Biobank protein QTL datasets. Multi-INTACT infers 52% to 109% more metabolite causal genes than protein-alone or expression-alone analyses and indicates both gene products are relevant for most gene nominations.

bioinformatics↗

bioRxiv2023

Body weight, gonadectomy, and other risk factors for diagnosis of osteoarthritis in companion dogs

OBJECTIVETo evaluate age, sex, body weight, breed, neuter status, and age at neutering as risk factors for diagnosis of osteoarthritis in companion dogs ANIMALSDogs seen as patients at Banfield Pet Hospital in the United States from 1998-2019 with a date of death in 2019. The final cohort consisted of 131,140 dogs. METHODSIn this retrospective cohort study, Cox proportional hazard models were used to test for associations between osteoarthritis incidence and age at baseline, sex, maximum body weight, maximum body condition score, neuter status, and age at neutering. The same model was used to test these associations in 12 representative breeds, chosen based on breed weight and sample size. RESULTSOlder age, higher adult body weight, gonadectomy, and younger age at gonadectomy were significantly associated with higher risks of osteoarthritis in the total cohort and in all 12 breeds evaluated. Higher body condition score and sex were also significantly associated with osteoarthritis but with minimal effect sizes in the overall cohort, and these risk factors were not consistently significant in all breeds tested. CLINICAL RELEVANCEThese results will assist veterinarians in identifying dogs at higher risk for osteoarthritis and applying appropriate diagnostic, preventative, and treatment interventions. An understanding of potentially modifiable risk factors, such as body condition, and neutering, will support evidence-based discussions with dog owners about risk management in individual patients.

systems biology↗

bioRxiv2023

Imperfect gold standard gene sets yield inaccurate evaluation of causal gene identification methods

Causal gene discovery methods are often evaluated using gold-standard (GS) sets of causal genes. However, GS gene sets are always incomplete, leading to mis-estimation of sensitivity, specificity, AUC. Labeling biases in GS gene sets can also lead to inaccurate ordering of discovery methods. We argue that evaluation of these methods should rely on statistical techniques like those used for variant discovery, rather than on comparison with GS gene sets.

genetics↗

bioRxiv2022

Probabilistic integration of transcriptome-wide association studies and colocalization analysis prioritizes molecular pathways of complex traits

Transcriptome-wide association studies (TWAS) and colocalization analysis are complementary integrative genetic association approaches routinely used to identify functional units underlying complex traits in post-genome-wide association study (post-GWAS) analyses. Recent studies suggest that both approaches are individually imperfect, but joint usage can yield robust and powerful inference results. This paper introduces a new statistical framework, INTACT, to perform probabilistic integration of TWAS and colocalization evidence for implicating putative causal genes. This procedure is flexible and can work with a wide range of existing TWAS and colocalization approaches. It has the unique ability to quantify the uncertainty of implicated genes, enabling rigorous control of false-positive discoveries. Taking advantage of this highly-desirable feature, we describe an efficient algorithm, INTACT-GSE, for gene set enrichment analysis based on the integrated TWAS and colocalization analysis results. We examine the proposed computational methods and illustrate their improved performance over the existing approaches through simulation studies. Finally, we apply the proposed methods to the GTEx data and a variety of GWAS summary statistics derived from complex and molecular traits previously analyzed by Hukku et al. and Sinnott-Armstrong et al. We find empirical evidence that the proposed methods improve and complement existing putative gene implication methods and are advantageous in evaluating and identifying key gene sets and biological pathways.

genetics↗

bioRxiv2022

Pleural macrophages promote recovery from influenza virus infection

Seasonal influenza results in 3 to 5 million cases of severe disease and 250,000 to 500,000 deaths annually. Macrophages have been implicated in both the resolution and progression of the disease, but the drivers of these outcomes are poorly understood. We probed mouse lung transcriptomic datasets using the Digital Cell Quantifier algorithm to predict immune cell subsets that correlated with mild or severe influenza A virus (IAV) infection outcomes. We identified a novel lung macrophage population that transcriptionally resembled small serosal cavity macrophages and correlated with mild disease. Until now, the study of serosal macrophage translocation in the context of infections has been neglected. Here, we show that pleural macrophages (PMs) migrate from the pleural cavity to the lung after infection with pH1N1 A/California/04/2009 IAV. We found that the depletion of PMs increased morbidity and pulmonary inflammation. There were increased proinflammatory cytokines in the pleural cavity and an influx of neutrophils within the lung. Our results show PMs are recruited to the lung during IAV infection and contribute to recovery from influenza. This study expands our knowledge of PM plasticity and provides a new source of lung macrophages independent of monocyte recruitment and local proliferation. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/493482v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@1e8880borg.highwire.dtl.DTLVardef@13b987dorg.highwire.dtl.DTLVardef@19856f9org.highwire.dtl.DTLVardef@76277d_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2020

On the automatic annotation of gene functions using observational data and phylogenetic trees

MotivationGene function annotation is important for a variety of downstream analyses of genetic data. Yet experimental characterization of function remains costly and slow, making computational prediction an important endeavor. In this paper we use a probabilistic evolutionary model built upon phylogenetic trees and experimental Gene Ontology functional annotations that allows automated prediction of function for unannotated genes. ResultsWe have developed a computationally efficient model of evolution of gene annotations using phylogenies based on a Bayesian framework using Markov Chain Monte Carlo for parameter estimation. Unlike previous approaches, our method is able to estimate parameters over many different phylogenetic trees and functions. The resulting parameters agree with biological intuition, such as the increased probability of function change following gene duplication. The method performs well on leave-one-out validation, and we further validated some of the predictions in the experimental scientific literature. AvailabilityOur method has been implemented as an R package and it is available online at https://github.com/USCBiostats/aphylo. Code needed to reproduce the tables and figures can be found in https://github.com/USCbiostats/aphylo-simulations. Author summaryUnderstanding the individual role that genes play in life is a key issue in biomedical-sciences. While information regarding gene functions is continuously growing, the number of genes with unknown biological purpose is yet greater. Because of this, scientists have dedicated much of their time to build and design tools that automatically infer gene functions. In this paper, we present yet another attempt to do such. While very simple, our model of gene-function evolution has some key features that have the potential to generate an impact in the field: (a) compared to other methods, ours is highly-scalable, which means that it is possible to simultaneously analyze hundreds of what are known as gene-families, compromising thousands of genes, (b) supports our biological intuition as our models data-driven results coherently agree with what theory dictates regarding how gene-functions evolved, (c) notwithstanding its simplicity, the models prediction accuracy is comparable to other more complex alternatives, and (d) perhaps most importantly, it can be used to both support new annotations and to suggest areas in which existing annotations show inconsistencies that may indicate errors or controversies.

bioinformatics↗

bioRxiv2020

Transcriptome and regulatory maps of decidua-derived stromal cells inform gene discovery in preterm birth

While a genetic component of preterm birth (PTB) has long been recognized and recently mapped by genome-wide association studies (GWAS), the molecular determinants underlying PTB remain elusive. This stems in part from an incomplete availability of comprehensive functional genomic annotations in human cell types relevant to pregnancy and PTB. Here, we generated extensive transcriptional and chromatin annotations of cultured primary decidua-derived mesenchymal stromal/stem cells (MSCs) and in vitro differentiated decidual stromal cells (DSCs) and developed a computational framework to integrate these functional annotations with results from a GWAS of gestational duration in 56,384 women. This resulted in a significant enrichment of heritability estimates in functional noncoding regions in stromal cells, as well as in the discovery of additional loci associated with gestational duration and target genes of associated loci. Our strategy illustrates how systematic functional annotations in pregnancy-relevant cell types aid in the experimental follow-up of GWAS for PTB and, likely, other pregnancy-related conditions.

genomics↗

bioRxiv2019

Mendelian randomization accounting for horizontal and correlated pleiotropic effects using genome-wide summary statistics.

Mendelian randomization (MR) is a valuable tool for detecting evidence of causal relationships using genetic variant associations. Opportunities to apply MR are growing rapidly with the number of genome-wide association studies (GWAS) with publicly available results. However, existing MR methods rely on strong assumptions that are often violated, leading to false positives. Many methods have been proposed loosening these assumptions. However, it has remained challenging to account for correlated pleiotropy, which arises when variants affect both traits through a heritable shared factor. We propose a new MR method, Causal Analysis Using Summary Effect Estimates (CAUSE), that accounts for correlated and uncorrelated horizontal pleiotropic effects. We demonstrate in simulations that CAUSE is more robust to correlated pleiotropy than other methods. Applied to traits studied in recent GWAS, we find that CAUSE detects causal relationships with strong literature support and avoids identifying most unlikely relationships. Our results suggest that many pairs of traits identified as causal using alternative methods may be false positives due to horizontal pleiotropy.

genetics↗

bioRxiv2019

Leaf -- An open-source, model-agnostic, data-driven web application for cohort discovery and translational biomedical research

ObjectiveAcademic medical centers and health systems are increasingly challenged with supporting appropriate secondary use of data that originate from multiple sources. Enterprise Data Warehouses (EDWs) have emerged as central resources for these data, but they often require an informatician to extract meaningful information, thereby limiting direct access by end users. To overcome this challenge, we have developed Leaf, a lightweight self-service web application for querying and extracting clinical data from heterogeneous data sources.\n\nMaterials and MethodsLeaf utilizes a flexible biomedical concept system to define hierarchical items and ontologies. Each Leaf concept contains both textual representations and associated SQL query building blocks, exposed by a simple drag-and-drop user interface. Leaf generates abstract syntax trees which are compiled into dynamic SQL queries.\n\nResultsLeaf is a successful production-supported tool at the University of Washington, which hosts a central Leaf instance querying an EDW with over 300 active users. Through the support of UW Medicine (https://uwmedicine.org), the Institute of Translational Health Sciences (https://www.iths.org) and the National Center for Data to Health (https://ctsa.ncats.nih.gov/cd2h/), Leaf source code has been released into the public domain at https://github.com/uwrit/leaf.\n\nDiscussionLeaf allows the querying of single or multiple clinical databases simultaneously, even those of different data models. This enables fast installation without costly extraction or duplication from existing databases.\n\nConclusionLeaf differs from existing cohort discovery tools because it does not specify a required data model and is designed to seamlessly integrate with existing enterprise user authentication systems and clinical databases in situ. We demonstrate its unique technical strengths and success alongside its friendly user interface. We believe Leaf to be useful for health system analytics, clinical research data warehouses, precision medicine biobanks and clinical studies involving large patient cohorts.

bioinformatics↗