Biology subjects

Smedley, D.

Publications and source records attributed to Smedley, D..

13 recordsLinked to original sources

bioRxiv2026

AI semantics for biomedical data integration

Researchers increasingly need to explore hypotheses that span multimodal data across different scales, organisms, and domains. In practice, this requires connecting knowledge across fragmented databases with incompatible APIs and heterogeneous annotation practices. Large language model (LLM) agents can automate this data integration process, but grounding LLM agent outputs in scientifically correct sources of truth remains a significant challenge. Here we describe our deployment of a novel AI semantics workflow using LLM agents to enable scalable data integration, grounded in biological knowledge in the form of ontologies. Our workflow comprises (1) a multi-agent system curating scientific knowledge across ontologies using the Ontology Lookup Service (OLS) as grounding; (2) an LLM embedding service to enable interoperability between scientific databases by mapping ontology terms; and (3) GrEBI, a knowledge graph and Model Context Protocol (MCP) server enabling LLM agents to conduct cross-cutting, multi-omic biomedical queries.

bioinformatics↗

bioRxiv2025

International Mouse Phenotyping Consortium: Investigating gene function and providing insights into human disease.

The International Mouse Phenotyping Consortium (IMPC; https://www.mousephenotype.org/) web portal contains phenotype data for mouse protein-coding genes derived from analysis of data obtained in a systematic and high-throughput fashion from knock-out lines produced by IMPC. The project has produced >1,400 mouse models of human disease that recapitulate phenotypes observed in patients. Over 8000 papers rely on data or reagents generated by IMPC, demonstrating the impact of the project on the research and clinical communities, and IMPC data is incorporated into other resources, such as MGI, Open Targets and UniProt. Data release (DR23.0, 2025) contains > 100 million data points from 9,277 genes and identified 113,803 significant phenotypes. To manage efficient access to this quantity of high dimensional data the IMPC web portal has been rebuilt using a cloud native architecture. The modern user interface retains the look and feel of the original portal with improvements identified through a usability study. New data visualisation and training materials for large scale data access through the API have also been developed to make the resource easier to use. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/681205v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@199243borg.highwire.dtl.DTLVardef@119c12forg.highwire.dtl.DTLVardef@1da1a27org.highwire.dtl.DTLVardef@1eb141d_HPS_FORMAT_FIGEXP M_FIG C_FIG

genetics↗

bioRxiv2025

Loss of CTLH component MAEA impairs DNA repair and replication and leads to developmental delay

Ubiquitin E3 ligases play crucial roles in the DNA damage response (DDR) by modulating the turnover, localization, activation, and interactions of DDR and DNA replication proteins. To gain further insight into how the ubiquitin system regulates the DDR, we performed a CRISPR-Cas9 knockout screen focused on E3 ligases and related proteins with the DNA topoisomerase I inhibitor, camptothecin. This uncovered the CTLH ubiquitin E3 ligase complex -- and particularly one of its core subunits, MAEA -- as a critical regulator of the cellular response to single-ended DNA double-strand breaks (seDSBs) and replication stress. In tandem, we identified patients with variants in MAEA who present with neurodevelopmental deficits including global developmental delay, dysmorphic facial features, brain abnormalities, intellectual disability, and abnormal movement. Analysis of patient-derived cell lines and mutation modeling reveal an underlying defect in HR-dependent DNA repair and replication fork restart as a likely cause of disease. We propose that MAEA dysfunction hinders DNA repair by reducing the efficiency of RAD51 loading at sites of DNA damage, which compromises genome integrity and cell division during development.

genetics↗

bioRxiv2024

The Unified Phenotype Ontology (uPheno): A framework for cross-species integrative phenomics

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

bioinformatics↗

bioRxiv2024

Towards a standard benchmark for variant and gene prioritisation algorithms: PhEval - Phenotypic inference Evaluation framework

BackgroundComputational approaches to support rare disease diagnosis are challenging to build, requiring the integration of complex data types such as ontologies, gene-to-phenotype associations, and cross-species data into variant and gene prioritisation algorithms (VGPAs). However, the performance of VGPAs has been difficult to measure and is impacted by many factors, for example, ontology structure, annotation completeness or changes to the underlying algorithm. Assertions of the capabilities of VGPAs are often not reproducible, in part because there is no standardised, empirical framework and openly available patient data to assess the efficacy of VGPAs - ultimately hindering the development of effective prioritisation tools. ResultsIn this paper, we present our benchmarking tool, PhEval, which aims to provide a standardised and empirical framework to evaluate phenotype-driven VGPAs. The inclusion of standardised test corpora and test corpus generation tools in the PhEval suite of tools allows open benchmarking and comparison of methods on standardised data sets. ConclusionsPhEval and the standardised test corpora solve the issues of patient data availability and experimental tooling configuration when benchmarking and comparing rare disease VGPAs. By providing standardised data on patient cohorts from real-world case-reports and controlling the configuration of evaluated VGPAs, PhEval enables transparent, portable, comparable and reproducible benchmarking of VGPAs. As these tools are often a key component of many rare disease diagnostic pipelines, a thorough and standardised method of assessment is essential for improving patient diagnosis and care.

bioinformatics↗

bioRxiv2023

The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

bioinformatics↗

bioRxiv2021

A basement membrane discovery pipeline uncovers network complexity, new regulators, and human disease associations

Basement membranes (BMs) are ubiquitous extracellular matrices whose composition remains elusive, limiting our understanding of BM regulation and function. By developing a bioinformatic and in vivo discovery pipeline, we define a network of 222 human proteins localized to BMs. Network analysis and screening in C. elegans and zebrafish identified new BM regulators, including ADAMTS, ROBO, and TGF{beta}. More than 100 BM-network genes associate with human phenotypes and by screening 63,039 genomes from families with rare disorders, we discovered loss-of-function variants in LAMA5, MPZL2, and MATN2, and show they regulate BM composition and function. This cross-disciplinary study establishes the immense complexity and role of BMs in human health.

cell biology↗

bioRxiv2021

SvAnna: efficient and accurate pathogenicity prediction for coding and regulatory structural variants in long-read genome sequencing

Structural variants (SVs) are implicated in the etiology of Mendelian diseases but have been systematically underascertained owing to limitations of existing technology. Recent technological advances such as long-read sequencing (LRS) enable more comprehensive detection of SVs, but approaches for clinical prioritization of candidate SVs are needed. Existing computational approaches do not specifically target LRS data, thereby missing a substantial proportion of candidate SVs, and do not provide a unified computational model for assessing all types of SVs. Structural Variant Annotation and Analysis (SvAnna) assesses all classes of SV and their intersection with transcripts and regulatory sequences in the context of topologically associating domains, relating predicted effects on gene function with clinical phenotype data. We show with a collection of 182 published case reports with pathogenic SVs that SvAnna places over 90% of pathogenic SVs in the top ten ranks. The interpretable prioritizations provided by SvAnna will facilitate the widespread adoption of LRS in diagnostic genomics.

bioinformatics↗

bioRxiv2021

Dimensional reduction of phenotypes from 53,000 mouse models reveals a diverse landscape of gene function

Animal models have long been used to study gene function and the impact of genetic mutations on phenotype. Through the research efforts of thousands of research groups, systematic curation of published literature, and high-throughput phenotyping screens, the collective body of knowledge for the mouse now covers the majority of protein-coding genes. We here collected data for over 53,000 mouse models with mutations in over 15,000 genomic markers and characterized by more than 254,000 annotations using more than 9,000 distinct ontology terms. We investigated dimensional reduction and embedding techniques as means to facilitate access to this diverse and high-dimensional information. Our analyses provide the first visual maps of the landscape of mouse phenotypic diversity. We also summarize some of the difficulties in producing and interpreting embeddings of sparse phenotypic data. In particular, we show that data preprocessing, filtering, and encoding have as much impact on the final embeddings as the process of dimensional reduction. Nonetheless, techniques developed in the context of dimensional reduction create opportunities for explorative analysis of this large pool of public data, including for searching for mouse models suited to study human diseases.

bioinformatics↗

bioRxiv2021

A pan-ontology view of machine-derived knowledge representations and feedback mechanisms for curation

Biomedical ontologies are established tools that organize knowledge in specialized research areas. They can also be used to train machine-learning models. However, it is unclear to what extent representations of ontology concepts learned by machine-learning models capture the relationships intended by ontology curators. It is also unclear whether the representations can provide insights to improve the curation process. Here, we investigate ontologies from across the spectrum of biological research and assess the concordance of formal ontology hierarchies with representations based on plain-text definitions. By comparing the internal properties of each ontology, we describe general patterns across the pan-ontology landscape and pinpoint areas with discrepancies in individual domains. We suggest specific mechanisms through which machine-learning approaches can lead to clarifications of ontology definitions. Synchronizing patterns in machine-derived representations with those intended by the ontology curators will likely streamline the use of ontologies in downstream applications.

bioinformatics↗

bioRxiv2021

Interpretable prioritization of splice variants in diagnostic next-generation sequencing

A critical challenge in genetic diagnostics is the computational assessment of candidate splice variants, specifically the interpretation of nucleotide changes located outside of the highly conserved dinucleotide sequences at the 5' and 3' ends of introns. To address this gap, we developed the Super Quick Informationcontent Random-forest Learning of Splice variants (SQUIRLS) algorithm. SQUIRLS generates a small set of interpretable features for machine learning by calculating the information-content (IC) of wildtype and variant sequences of canonical and cryptic splice sites, assessing changes in candidate splicing regulatory sequences, and incorporating characteristics of the sequence such as exon length, disruptions of the AG exclusion zone, and conservation. We curated a comprehensive collection of disease-associated splicealtering variants at positions outside of the highly conserved AG/GT dinucleotides at the termini of introns. SQUIRLS trains two random-forest classifiers for the donor and for the acceptor and combines their outputs by logistic regression to yield a final score. We show that SQUIRLS transcends previous state of the art accuracy in classifying splice variants as assessed by rank analysis in simulated exomes and is significantly faster than competing methods. SQUIRLS provides tabular output files for incorporation into diagnostic pipelines for exome and genome analysis, as well as visualizations that contextualize predicted effects of variants on splicing to make it easier to interpret splice variants in diagnostic settings

bioinformatics↗

bioRxiv2020

OpenStats: A Robust and Scalable Software Package for Reproducible Analysis of High-Throughput Phenotypic Data

Reproducibility in the statistical analyses of data from high-throughput phenotyping screens requires a robust and reliable analysis foundation that allows modelling of different possible statistical scenarios. Regular challenges are scalability and extensibility of the analysis software. In this manuscript, we describe OpenStats, a freely available software package that addresses these challenges. We show the performance of the software in a high-throughput phenomic pipeline in the International Mouse Phenotyping Consortium (IMPC) and compare the agreement of the results with the most similar implementation in the literature. OpenStats has significant improvements in speed and scalability compared to existing software packages including a 13-fold improvement in computational time to the current production analysis pipeline in the IMPC. Reduced complexity also promotes FAIR data analysis by providing transparency and benefiting other groups in reproducing and re-usability of the statistical methods and results. OpenStats is freely available under a Creative Commons license at www.bioconductor.org/packages/OpenStats.

bioinformatics↗

bioRxiv2016

The Monarch Initiative: Insights across species reveal human disease mechanisms

The principles of genetics apply across the whole tree of life: on a cellular level, we share mechanisms with species from which we diverged millions or even billions of years ago. We can exploit this common ancestry at the level of sequences, but also in terms of observable outcomes (phenotypes), to learn more about health and disease for humans and all other species. Applying the range of available knowledge to solve challenging disease problems requires unified data relating genomics, phenotypes, and disease; it also requires computational tools that leverage these multimodal data to inform interpretations by geneticists and to suggest experiments. However, the distribution and heterogeneity of databases is a major impediment: databases tend to focus either on a single data type across species, or on single species across data types. Although each database provides rich, high-quality information, no single one provides unified data that is comprehensive across species, biological scales, and data types. Without a big-picture view of the data, many questions in genetics are difficult or impossible to answer. The Monarch Initiative (https://monarchinitiative.org) is an international consortium dedicated to providing computational tools that leverage a computational representation of phenotypic data for genotype-phenotype analysis, genomic diagnostics, and precision medicine on the basis of a large-scale platform of multimodal data that is deeply integrated across species and covering broad areas of disease.

evolutionary biology↗