Biology subjects

Taylor, D. M.

Publications and source records attributed to Taylor, D. M..

At least 19 recordsLinked to original sources

bioRxiv2026

ddkg.skill: A Compositional Agent Skill for Translating Biomedical and Bioinformatics Questions into Cypher for the Data Distillery Knowledge Graph

Biomedical knowledge graphs can connect information across genes, phenotypes, tissues, pathways, experiments, and clinical resources, but they are difficult to query correctly without detailed knowledge of the graph. Large language models can help write Cypher code for knowledge graphs, yet a query focused on a bioinformatics task that looks reasonable may still use the wrong identifier, relationship direction, source, intermediate node, or output unit if the user is not completely trained on the schema. We developed ddkg.skill, an Agent Skill that can be loaded into a compatible LLM session to support querying of the NIH Common Fund Data Ecosystem Data Distillery Knowledge Graph (DDKG), a biomedical property graph integrating more than 180 ontologies, over 40 genomics datasets, and dozens of additional cross-domain biomedical datasets. Rather than utilizing a simple markdown prompt, ddkg.skill contains a controller, a compositional library of DDKG-specific references and structured tables, primary documentation, validated query patterns, a routing table, and a Python script that checks the internal links among these materials. The skill build for the December 2025 DDKG release contains 38 bundled files and 239 routing relationships and is identified by checksum so users can state the exact build used to compose a query. The evaluation reported here was performed separately on an earlier build with 38 files and 227 routing entries. In that orthogonal nine-test evaluation against a live December 2025 DDKG instance, five of seven tests targeting sources absent from the worked examples produced correct executed results. The evaluation also exposed a failed query, a cross-source comparison that was not biologically well posed, and errors in the skills own reference material that informed later revisions. The skill guides an AI through entity resolution, graph inspection, query construction, and validation for biomedical and bioinformatics queries, while keeping the DDKG itself as the source of returned results. This design provides a portable and versioned method for giving general-purpose AI systems practical knowledge of a complex biomedical knowledge graph to empower complex bioinformatics data integration. Three executed biomedical use cases further demonstrate cross-species phenotype-to-expression querying, genomic overlap between 4DN chromatin-loop anchors and GTEx eQTLs, and integration of congenital heart phenotype associations with ClinVar and GTEx heart evidence.

bioinformatics↗

bioRxiv2026

MOSAIC: Learning Graph Node Embeddings from Spectrally Isolated Dominant Subspaces of Accumulated Diffusion Operators

Graph embedding transforms complex networks into low-dimensional representations for analysis and downstream machine learning. Existing methods often rely on eigenvalue ordering, random-walk sampling, or neural optimization. We introduce MOSAIC, Multistep Operator Spectral Accumulation with Isolated Components, an unsupervised framework that constructs an accumulated diffusion operator from successive powers of a graph transition matrix. MOSAIC uses Krylov subspace analysis to identify dominant spectral components according to their convergence behavior. This approach emphasizes persistent and spectrally separated diffusion modes rather than selecting embedding coordinates solely by eigenvalue magnitude. We evaluated MOSAIC using a biomedical graph projection derived from the Data Distillery Knowledge Graph that contained 5,171 human genes and 10,147 phenotypes. Across embedding dimensions from 16 to 128, MOSAIC achieved node-type classification AUROCs from 0.9863 to 0.9926 and exceeded FastRP, Node2Vec, HashGNN, and GraphSAGE. For gene-phenotype link prediction, validation and test associations were withheld before embedding generation. Under progressive removal of all edges or specific edge classes, MOSAIC retained more node-label information than FastRP, including after extreme graph sparsification. In a focused DICER1 analysis, 106 of 137 documented phenotype associations received scores above 0.90, demonstrating concordance with known pleiotropic biology and prioritizing additional relationships for independent evaluation. These results support MOSAIC as a fully unsupervised framework for learning informative graph representations. Although demonstrated using a biomedical graph projection, MOSAIC is domain independent and can be applied to other complex networks

bioinformatics↗

bioRxiv2026

BIFO: A Biological Information Flow Ontology for Directed Propagation in Heterogeneous Biomedical Knowledge Graphs

Biomedical knowledge graphs integrate heterogeneous data by connecting many entity types through many relationship types. Computational analyses that propagate signal across these graphs (random walks, diffusion, and message passing) implicitly assume that every traversable edge can carry a biological signal. In a heterogeneous KG this is rarely true: hierarchical, lexical, and purely statistical edges do not, by themselves, define an admissible directed state transformation, and traversing them propagates signal along paths that are not biologically meaningful. We present the Biological Information Flow Ontology (BIFO), a graph-agnostic specification of which directed transformations are biologically admissible for computable information flow. BIFO defines fourteen entity classes, a taxonomy of flow classes organized around the backbone G+CH [->]RNA [->]P [->]PW [->]C [->]PH [->]DS, a set of admissibility constraints, and a two-level CURIE mapping that can be applied without schema-specific code to any graph whose identifiers and predicates are resolvable through, or extendable to, the BIFO mapping tables. A four-step conditioning protocol converts a raw property graph into a conditioned propagation graph in which only admissible, direction-aware edges remain. We provide a reference implementation on the Data Distillery Knowledge Graph (DDKG); conditioning a cohort-independent, gene-anchored subgraph as a BIFO substrate of 33.6 million edges retained 23.7 million (70.7%) as BIFO-classified relationships, cleanly separating 13.3 million propagating mechanistic edges from 10.5 million retained-but-non-propagating observational associations, and confirming that pathway concepts are configured as scoring accumulation endpoints for BIFO-PPR pathway scoring. BIFO is an admissibility specification for computable propagation of signal over knowledge graphs. It is released as an open specification with versioned mapping tables and tooling, providing a reusable substrate for biologically interpretable, direction-aware analysis of biomedical knowledge graphs.

bioinformatics↗

bioRxiv2026

BiomarkerKB: FAIR and Integrated Biomarker Knowledge Connecting Biomolecular and Clinical Data Types

Biomarkers are essential tools for disease detection, risk assessment, therapeutic monitoring, and precision medicine. However, biomarker data are dispersed across heterogeneous resources, inconsistently reported in the literature, and rarely standardized for computational use. This fragmentation limits reproducibility, cross-study integration, and the discovery of novel biomarker and disease relationships. We developed BiomarkerKB, a knowledgebase designed to harmonize and integrate biomarker information under a standardized data model. The model follows the FDA-NIH BEST biomarker definition and captures both core fields (biomarker entity, disease/condition, exposure agent) and contextual metadata (specimen, biomarker role, evidence, provenance). Biomarker data and related annotations were either curated from publications or collected from public resources (e.g., OpenTargets, GWAS Catalog, ClinVar, CIViC, OncoMX) and were also contributed by the Common Fund Data Coordinating Centers and the Early Detection Research Network (EDRN). Standardization was achieved using ontologies and reference resources such as Disease Ontology, UBERON, UniProtKB, and HUGO Gene Nomenclature Committee (HGNC) gene symbols. BiomarkerKB data were ingested into a Neo4j-based knowledge graph and integrated with the Common Fund Data Ecosystem (CFDE) Knowledge Graph. The initial release of BiomarkerKB contains over 200,000 biomarker-disease associations spanning genes, proteins, metabolites, glycans, and chemical elements. The knowledge graph comprises more than 300,000 nodes and 1.2 million edges, enabling structured exploration of biomarker relationships within CFDE data as demonstrated through the knowledge graph query-based use cases presented in this study. A publicly accessible web portal (https://biomarkerkb.org) provides keyword search, filtering, data downloads, and access to graph visualization to support both researchers and computational analyses. BiomarkerKB addresses a critical gap in biomarker informatics by providing an integrated, FAIR (Findable, Accessible, Interoperable, and Reusable), and unified framework for biomarker knowledge exploration and discovery.

bioinformatics↗

bioRxiv2026

Neural Mechanisms of Mixed Speech and Grasp Representation in Sensorimotor Cortices

Recent brain-machine interface (BMI) studies have challenged traditional views of functional specialization in human motor cortices, suggesting that regions associated with hand control also support speech. The extent of this dual functionality as well as the neural mechanisms underlying it are unclear. We address this by analyzing intracortical neural activity from seven brain regions (spanning motor, premotor, somatosensory and parietal regions) across two human participants with tetraplegia. Across all regions, grasp decoding was robust. In addition, we achieved reliable discrete-word decoding during silent reading as well as vocalized speech. Both tasks largely recruited overlapping neural populations within each region, yet these populations reconfigured their functional connectivity between tasks. Additionally, subspace analyses revealed segregated computations for speech and grasping despite mixed selectivity at the single channel level. Our findings support multi-functional BMIs capable of decoding speech and grasping from the same implant and highlight ventral premotor area 6r as a novel target.

neuroscience↗

bioRxiv2025

ndufs2-/- zebrafish have impaired survival, neuromuscular activity, morphology, and one-carbon metabolism treatable with folic acid

Mitochondrial complex I (CI) deficiency represents a common biochemical pathophysiology underlying Leigh syndrome spectrum (LSS), manifesting with progressive multi-system dysfunction, lactic acidemia, and early mortality. To facilitate mechanistic studies and rigorous screening of therapeutic candidates for CI deficient LSS, we used CRISPR/Cas9 to generate an ndufs2-/- 16 bp deletion zebrafish strain. ndufs2-/- larvae exhibit markedly reduced survival, severe neuromuscular dysfunction including impaired swimming capacity, multiple morphologic malformations, reduced growth, hepatomegaly, uninflated swim bladder, yolk retention, small intestines, and small eyes and pupils with abnormal retinal ganglion cell layer. Transcriptome profiling of ndufs2-/- larvae revealed dysregulation of the electron transport chain, TCA cycle, fatty acid beta-oxidation, and one-carbon metabolism. Similar transcriptomic profiles were observed in ndufs2-/- missense mutant C. elegans (gas-1(fc21)) and two human CI-disease fibroblast cell lines stressed in galactose media. ndufs2-/- zebrafish had 80% reduced CI enzyme activity. Unbiased metabolomic profiling showed increased lactate, TCA cycle intermediates, and acyl-carnitine species. One-carbon metabolism associated pathway alterations appear to contribute to CI disease pathophysiology, as folic acid treatment rescued the growth defect and hepatomegaly in ndufs2-/-larvae. Overall, ndufs2-/- zebrafish recapitulate severe CI deficiency, complex metabolic pathophysiology, and relevant LSS neuromuscular and survival phenotypes, enabling future translational studies of therapeutic candidates.

pharmacology and toxicology↗

bioRxiv2025

Attenuation of mosaic aneuploidy and erroneous first mitotic division of human embryos

Human reproduction is inherently inefficient1 and 1 in 6 people worldwide suffer infertility. In vitro fertilisation (IVF) can help some couples conceive, but only ~30% of cycles are successful. One factor affecting IVF efficacy is mitotic-origin (mosaic) aneuploidy in which embryos contain a mixture of cells with different numbers of chromosomes2. We previously showed that chromosome segregation error phenotypes are frequent in the first mitotic division of the human embryo3. However, the cause of these errors and impact on daughter cell karyotype is unknown. Here, using live chromosome imaging and next generation sequencing we show that activation of the microtubule depolymerase KIF2C reduces chromosome segregation errors and mitotic-origin aneuploidy at the 2-cell stage. The number of first divisions that show alternative cleavage patterns (associated with failed embryo development in IVF clinics) are also reduced with KIF2C activation. Our findings demonstrate that modulation of microtubule dynamics is a potential therapeutic route to improving human embryo quality and IVF outcomes.

cell biology↗

bioRxiv2024

Leukemia confers a durable imprint on healthy hematopoietic stem and progenitor cells

Recent models of infection and experimental inflammation reveal that hematopoietic stem and progenitor cells (HSPCs) can generate a memory of the exposure that heightens the response to subsequent stimulation, a process termed central trained immunity. Inflammation is also a constitutive feature of cancer, including hematologic malignancies. Here, we adapt a translationally relevant model of acute myeloid leukemia (AML) to determine if inflammation in the bone marrow (BM) niche durably reprograms resident healthy HSPCs. To simulate the onset of malignancy along with the associated inflammatory surge as well as the subsequent remission, we generated hematopoietic chimera with healthy HSPCs and HSPCs bearing a doxycycline-responsive oncogene (hMLL-AF9) expression cassette, a validated model of AML. Results show that the exposure to AML blasts in the BM leaves healthy HSPCs during experimental remission with broad transcriptomic, epigenetic changes and enhanced reliance on glycolysis. A heterologous secondary challenge of AML-experienced animals resulted in pronounced gene expression changes in inflammatory and metabolic pathways. These augmented responses coincided with altered chromatin accessibility in AML-experienced HSPCs. Motif analysis of the epigenome in AML trained HSPC points to the involvement of core hematopoietic transcription factors. Altogether, these observations provide first evidence for the durable inflammatory reprogramming of healthy HSPCs in the cancer microenvironment.

cancer biology↗

bioRxiv2024

Playbook Workflow Builder: Interactive Construction of Bioinformatics Workflows from a Network of Microservices

Many biomedical research projects produce large-scale datasets that may serve as resources for the research community for hypothesis generation, facilitating diverse use cases. Towards the goal of developing infrastructure to support the findability, accessibility, interoperability, and reusability (FAIR) of biomedical digital objects and maximally extracting knowledge from data, complex queries that span across data and tools from multiple resources are currently not easily possible. By utilizing existing FAIR application programming interfaces (APIs) that serve knowledge from many repositories and bioinformatics tools, different types of complex queries and workflows can be created by using these APIs together. The Playbook Workflow Builder (PWB) is a web-based platform that facilitates interactive construction of workflows by enabling users to utilize an ever-growing network of input datasets, semantically annotated API endpoints, and data visualization tools contributed by an ecosystem. Via a user-friendly web-based user interface (UI), workflows can be constructed from contributed building-blocks without technical expertise. The output of each step of the workflows are provided in reports containing textual descriptions, as well as interactive and downloadable figures and tables. To demonstrate the ability of the PWB to generate meaningful hypotheses that draw knowledge from across multiple resources, we present several use cases. For example, one of these use cases sieves novel targets for individual cancer patients using data from the GTEx, LINCS, Metabolomics, GlyGen, and the ExRNA Communication Consortium (ERCC) Common Fund (CF) Data Coordination Centers (DCCs). The workflows created with the PWB can be published and repurposed to tackle similar use cases using different inputs. The PWB platform is available from: https://playbook-workflow-builder.cloud/.

bioinformatics↗

bioRxiv2024

Positioning Genomic Features in Biomedical Knowledge Graphs using the Homo sapiens Chromosomal Location Ontology for GRCh38 (HSCLO38)

The Homo sapiens Chromosomal Location Ontology for GRCh38 (HSCLO38) represents a knowledge-graph-ready framework for connecting genomic features at multiple resolutions. We present the methodology behind the development of HSCLO38 and its integration with current genomic standards for application in biomedical research. We explore the performance and scalability of HSCLO38 in specific use cases in handling large-scale genomic data in a biomedical knowledge graph.

bioinformatics↗

bioRxiv2023

Inflammatory Recruitment of Healthy Hematopoietic Stem and Progenitor Cells in the Acute Myeloid Leukemia Niche

Inflammation in the bone marrow (BM) microenvironment is a constitutive component of leukemogenesis in acute myeloid leukemia (AML). Current evidence suggests that both leukemic blasts and stroma secrete proinflammatory factors that actively suppress the function of healthy hematopoietic stem and progenitor cells (HSPCs). HSPCs are also cellular components of the innate immune system, and we reasoned that they may actively propagate the inflammation in the leukemic niche. In two separate congenic models of AML we confirm by evaluation of the BM plasma secretome and HSPC-selective single-cell RNA sequencing (scRNA-Seq) that multipotent progenitors and long-lived stem cells adopt inflammatory gene expression programs, even at low BM leukemic burden. In particular, we observe interferon gamma (IFN-{gamma}) pathway activation, along with secretion of its chemokine target, CXCL10. We show that AML-derived nanometer-sized extracellular vesicles (EVAML) are sufficient to trigger this inflammatory HSPC response, both in vitro and in vivo. Altogether, our studies indicate that HSPCs are an unrecognized component of the inflammatory adaptation of the BM by leukemic cells. The pro-inflammatory conversion and long-lived presence of HSPC in the BM along with their regenerative re-expansion during remission may impact clonal selection and disease evolution.

cancer biology↗

bioRxiv2023

Petagraph: A large-scale unifying knowledge graph framework for integrating biomolecular and biomedical data

The use of biomedical knowledge graphs (BMKG) for knowledge representation and data integration has increased drastically in the past several years due to the size, diversity, and complexity of biomedical datasets and databases. Data extraction from a single dataset or database is usually not particularly challenging. However, if a scientific question must rely on integrative analysis across multiple databases or datasets, it can often take many hours to correctly and reproducibly extract and integrate data towards effective analysis. To overcome this issue, we created Petagraph, a large-scale BMKG that integrates biomolecular data into a schema incorporating the Unified Medical Language System (UMLS). Petagraph is instantiated on the Neo4j graph platform, and to date, has fifteen integrated biomolecular datasets. The majority of the data consists of entities or relationships related to genes, animal models, human phenotypes, drugs, and chemicals. Quantitative data sets containing values from gene expression analyses, chromatin organization, and genetic analyses have also been included. By incorporating models of biomolecular data types, the datasets can be traversed with hundreds of ontologies and controlled vocabularies native to the UMLS, effectively bringing the data to the ontologies. Petagraph allows users to analyze relationships between complex multi-omics data quickly and efficiently.

bioinformatics↗

bioRxiv2021

Betacoronavirus-specific alternate splicing

Viruses can subvert a number of cellular processes in order to block innate antiviral responses, and many viruses interact with cellular splicing machinery. SARS-CoV-2 infection was shown to suppress global mRNA splicing, and at least 10 SARS-CoV-2 proteins bind specifically to one or more human RNAs. Here, we investigate 17 published experimental and clinical datasets related to SARS-CoV-2 infection as well as datasets from the betacoronaviruses SARS-CoV and MERS as well as Streptococcus pneumonia, HCV, Zika virus, Dengue virus, influenza H3N2, and RSV. We show that genes showing differential alternative splicing in SARS-CoV-2 have a similar functional profile to those of SARS-CoV and MERS and affect a diverse set of genes and biological functions, including many closely related to virus biology. Additionally, the differentially spliced transcripts of cells infected by coronaviruses were more likely to undergo intron-retention, contain a pseudouridine modification and a smaller number of exons than differentially spliced transcripts in the control groups. Viral load in clinical COVID-19 samples was correlated with isoform distribution of differentially spliced genes. A significantly higher number of ribosomal genes are affected by DAS and DGE in betacoronavirus samples, and the betacoronavirus differentially spliced genes are depleted for binding sites of RNA-binding proteins. Our results demonstrate characteristic patterns of differential splicing in cells infected by SARS-CoV-2, SARS-CoV, and MERS, potentially modifying a broad range of cellular functions and affecting a diverse set of genes and biological functions.

genomics↗

bioRxiv2021

Promoter-sequence determinants and structural basis of primer-dependent transcription initiation in Escherichia coli

Chemical modifications of RNA 5' ends enable "epitranscriptomic" regulation, influencing multiple aspects of RNA fate. In transcription initiation, a large inventory of substrates compete with nucleoside triphosphates (NTPs) for use as initiating entities, providing an ab initio mechanism for altering the RNA 5' end. In Escherichia coli cells, RNAs with a 5'-end hydroxyl are generated by use of dinucleotide RNAs as primers for transcription initiation, "primer-dependent initiation." Here we use massively systematic transcript end readout ("MASTER") to detect and quantify RNA 5' ends generated by primer-dependent initiation for ~410 (~1,000,000) promoter sequences in E. coli. The results show primer-dependent initiation in E. coli involves any of the 16 possible dinucleotide primers and depends on promoter sequences in, upstream, and downstream of the primer binding site. The results yield a consensus sequence for primer-dependent initiation, YTSS-2NTSS-1NTSSWTSS+1, where TSS is the transcription start site, NTSS-1NTSS is the primer binding site, Y is pyrimidine, and W is A or T. Biochemical and structure-determination studies show that the base pair (nontemplate-strand base:template-strand base) immediately upstream of the primer binding site (Y:RTSS-2, where R is purine) exerts its effect through the base on the DNA template strand (RTSS-2) through inter-chain base stacking with the RNA primer. Results from analysis of a large set of natural, chromosomally-encoded E. coli promoters support the conclusions from MASTER. Our findings provide a mechanistic and structural description of how TSS-region sequence hard-codes not only the TSS position, but also the potential for epitranscriptomic regulation through primer-dependent transcription initiation.

biochemistry↗

bioRxiv2021

Modelling the impact of decidual senescence on embryo implantation in human endometrial assembloids

Decidual remodelling of midluteal endometrium leads to a short implantation window after which the uterine mucosa either breaks down or is transformed into a robust matrix that accommodates the placenta throughout pregnancy. To gain insights into the underlying mechanisms, we established and characterised endometrial assembloids, consisting of gland organoids and primary stromal cells. Single-cell transcriptomics revealed that decidualized assembloids closely resemble midluteal endometrium, harbouring differentiated and senescent subpopulations in both glands and stroma. We show that acute senescence in glandular epithelium drives secretion of multiple canonical implantation factors, whereas in the stroma it calibrates the emergence of anti-inflammatory decidual cells and pro-inflammatory senescent decidual cells. Pharmacological inhibition of stress responses in pre-decidual cells accelerated decidualization by inhibiting senescence and mesenchymal-epithelial transition, processes involved in endometrial breakdown and regeneration, respectively. Accelerated decidualization resulted in entrapment of co-cultured human blastocysts in a largely static decidual matrix. By contrast, the presence of senescent decidual cells created a dynamic implantation environment, enabling embryo expansion and attachment, although their persistence led to gradual disintegration of assembloids. Our findings demonstrate that senescence controls endometrial fate decisions at implantation and highlight how endometrial assembloids may accelerate the discovery of new treatments to prevent reproductive failure.

developmental biology↗

bioRxiv2020

NASA GeneLab RNA-Seq Consensus Pipeline: Standardized Processing of Short-Read RNA-Seq Data

With the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility and reusability of pipeline data, to provide a template for data processing of future spaceflight-relevant datasets, and to encourage cross-analysis of data from other databases with the data available in GeneLab.

bioinformatics↗

bioRxiv2020

The First Mitotic Division of the Human Embryo is Highly Error-prone

Aneuploidy in human embryos is surprisingly prevalent and increases drastically with maternal age, resulting in miscarriages, infertility and birth defects. Frequent errors during the meiotic divisions cause this aneuploidy, while age-independent errors during the first cleavage divisions of the embryo also contribute. However, the underlying mechanisms are poorly understood, largely because these events have never been visualised in living human embryos. Here, using cell-permeable DNA dyes, we film chromosome segregation during the first and second mitotic cleavage divisions in human embryos from women undergoing assisted reproduction following ovarian stimulation. We show that the first mitotic division takes several hours to complete and is highly variable. Timings of key mitotic events were, however, largely consistent with clinical videos of embryos that gave rise to live births. Multipolar divisions and lagging chromosomes during anaphase were frequent with no maternal age association. In contrast, the second mitosis was shorter and underwent mostly bipolar divisions with no detectable lagging chromosomes. We propose that the first mitotic division in humans is a unique and highly error-prone event, which contributes to fetal aneuploidies.

cell biology↗

bioRxiv2020

Modeling metabolic variation with single-cell expression data

Cellular metabolism encompasses the biochemical reactions and transportation of various metabolites in cells and their surroundings, which are integrated at all levels of cellular functions. We developed a method to systematically simulate cellular metabolism using single-cell RNA-seq (scRNA-seq) data through constraint-based context specific metabolic modeling. We simulated the NAD+ biosynthesis activity in 7 different mouse tissues, and the simulated NAD+ biosynthesis flux levels showed significant linear correlation with experimental measurements in previous research. We also show that the simulated NAD+ biosynthesis fluxes are reproducible using two additional scRNA-seq datasets.

systems biology↗