The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species

Harnessing genotype and phenotype data for population-scale variant classification using large language models and bayesian inference

Variants of Uncertain Significance (VUS) in genetic testing for hereditary diseases burden patients and clinicians, yet clinical data that could reduce VUS are underutilized due to a lack of scalable strategies. We assessed whether a machine learning approach using genotype and phenotype data could improve variant classification and reduce VUS. In this cohort study of a multi-step machine learning approach, patient data from test requisition forms were used to distinguish patients with molecular diagnoses from controls ("patient score"). A generative Bayesian model then used patient scores and variant classifications to infer variant pathogenicity ("variant score"). The study included 3.5 million patients referred for clinical genetic testing across various conditions. Primary outcomes were model- and gene-level discrimination, classification performance, probabilistic calibration, and concordance with orthogonal pathogenicity measures. Integration into a semi-quantitative classification framework was based on posterior pathogenicity probabilities matching PPV ≥ 0.99/NPV ≥ 0.95 thresholds, followed by expert review. We generated 1,334 clinical variant models (CVMs); 595 showed high performance in both machine learning steps (AUROCpatient ≥ 0.8 and AUROCvariant ≥ 0.8) on held-out data. High-confidence predictions from these CVMs provided evidence for 5,362 VUS observed in 200,174 patients, representing 23.4% of all VUS observations in these genes. In 17 frequently tested genes, CVMs reclassified over 1,000 unique VUS, reducing VUS report rates by 9-49% per condition. In conclusion, a scalable machine learning approach using underutilized clinical data improved variant classification and reduced VUS.

First steps toward building natural history of diseases computationally: Lessons learned from the Noonan syndrome use case

Rare diseases (RDs) are conditions affecting fewer than 1 in 2,000 people, with over 7,000 identified, primarily genetic in nature, and more than half impacting children. Although each RD affects a small population, collectively, between 3.5% and 5.9% of the global population, or 262.9-446.2 million people, live with an RD. Most RDs lack established treatment protocols, highlighting the need for proper care pathways addressing prognosis, diagnosis, and management. Advances in generative AI and large language models (LLMs) offer new opportunities to document the temporal progression of phenotypic features, addressing gaps in current knowledge bases. This study proposes an LLM-based framework to capture the natural history of diseases, specifically focusing on Noonan syndrome. The framework aims to document phenotypic trajectories, validate against RD knowledge bases, and integrate insights into care coordination using electronic health record (EHR) data from the Undiagnosed Diseases Program Singapore.

MedKG: enabling drug discovery through a unified biomedical knowledge graph

Biomedical knowledge graphs have emerged as powerful tools for drug discovery, but existing platforms often suffer from outdated information, limited accessibility, and insufficient integration of complex data. This study presents MedKG, a comprehensive and continuously updated knowledge graph designed to address these challenges in precision medicine and drug discovery. MedKG integrates data from 35 authoritative sources, encompassing 34 node types and 79 relationships. A Continuous Integration/Continuous Update pipeline ensures MedKG remains current, addressing a critical limitation of static knowledge bases. The integration of molecular embeddings enhances semantic analysis capabilities, bridging the gap between chemical structures and biological entities. To demonstrate MedKG's utility, a novel hybrid Relational Graph Convolutional Network for disease-drug link prediction, MedLINK was developed and used in case studies on clinical trial data for disease drug link prediction. Furthermore, a web-based application with user-friendly APIs and visualization tools was built, making MedKG accessible to both technical and non-technical users, which is freely available at http://pitools.niper.ac.in/medkg/.

A simplified retriever to improve accuracy of phenotype normalizations by large language models

Large language models have shown improved accuracy in phenotype term normalization tasks when augmented with retrievers that suggest candidate normalizations based on term definitions. In this work, we introduce a simplified retriever that enhances large language model accuracy by searching the Human Phenotype Ontology (HPO) for candidate matches using contextual word embeddings from BioBERT without the need for explicit term definitions. Testing this method on terms derived from the clinical synopses of Online Mendelian Inheritance in Man (OMIM®), we demonstrate that the normalization accuracy of GPT-4o increases from a baseline of 62% without augmentation to 85% with retriever augmentation. This approach is potentially generalizable to other biomedical term normalization tasks and offers an efficient alternative to more complex retrieval methods.

Ontology-based expansion of virtual gene panels to improve diagnostic efficiency for rare genetic diseases

BACKGROUND

Virtual Gene Panels (VGP) comprising disease-associated causal genes are utilized in the diagnosis of rare genetic diseases to evaluate candidate genes identified by whole-genome and whole-exome sequencing. VGPs generated by the PanelApp software were utilized in a UK 100,000 Genome Project pilot study to filter candidate genes, thus enhancing diagnostic efficiency for rare diseases. However, PanelApp also filtered out disease-causing genes in nearly 50% of the cases.

METHODS

Here, we propose various methods for optimized approach to design VGPs that significantly improve the diagnostic efficiency by leveraging the hierarchical structure of the Mondo disease ontology, without excluding disease-causing genes. We also performed computational experiments on an evaluation dataset comprising 74 patients to determine the optimal VGP design method.

RESULTS

Our results demonstrate that the proposed method can significantly enhance rare disease diagnosis efficiency by automatically identifying candidate genes. The proposed method successfully designed VGPs that improve diagnosis efficiency without excluding disease-causing genes.

CONCLUSION

We have developed novel methods for VGP design that leverage the hierarchical structure of the Mondo disease ontology to improve rare genetic disease diagnosis efficiency. This approach identifies candidate genes without excluding disease-causing genes, and thereby improves diagnostic efficiency.

Systematic dissection of pleiotropic loci and critical regulons in excitatory neurons and microglia relevant to neuropsychiatric and ocular diseases

Advancements in single-cell multimodal techniques have greatly enhanced our understanding of disease-relevant loci identified through genome-wide association studies (GWASs). To investigate the biological connections between the eye and brain, we integrated bulk and single-cell multiomic profiles with GWAS summary statistics for eight neuropsychiatric and five ocular diseases. Our analysis uncovered five latent factors explaining 61.7% of the genetic variance across these 13 diseases, revealing diverse correlational patterns among them. We identified 45 pleiotropic loci with 91 candidate genes that contribute to disease risk. By integrating GWAS and single-cell profiles, we implicated excitatory neurons and microglia as key contributors in the eye-brain connections. Polygenic enrichment analysis further identified 15 pleiotropic regulons in excitatory neurons and 16 in microglia that were linked to comorbid conditions. Functionally, excitatory neuron-specific regulons were involved in axon guidance and synaptic activity, while microglia-specific regulons were associated with immune response and cell activation. In sum, these findings underscore the genetic link between psychiatric disorders and ocular diseases.

Better understanding the phenotypic effects of drugs through shared targets in genetic disease networks

Introduction

Most drugs fail during development and there is a clear and unmet need for approaches to better understand mechanistically how drugs exert both their intended and adverse effects. Gaining traction in this field is the use of disease data linking genes with pathological phenotypes and combining this with drugtarget interaction data.

Methods

We introduce methodology to associate drugs with effects, both intended and adverse, using a tripartite network approach that combines drug-target and target-phenotype data, in which targets can be represented as proteins and protein domains.

Results

We were able to detect associations for over 140,000 ChEMBL drugs and 3,800 phenotypes, represented as Human Phenotype Ontology (HPO) terms. The overlap of these results with the SIDER databases of known drug side effects was up to 10 times higher than random, depending on the target type, disease database and score threshold used. In terms of overlap with drug-phenotype pairs extracted from the literature, the performance of our methodology was up to 17.47 times greater than random. The top results include phenotype-drug associations that represent intended effects, particularly for cancers such as chronic myelogenous leukemia, which was linked with nilotinib. They also include adverse side effects, such as blurred vision being linked with tetracaine.

Discussion

This work represents an important advance in our understanding of how drugs cause intended and adverse side effects through their action on disease causing genes and has potential applications for drug development and repositioning.

A corpus of GA4GH phenopackets: Case-level phenotyping for genomic diagnostics and discovery

The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments. There has been a great need for a collection of phenopackets to test software pipelines and algorithms. Here, we present Phenopacket Store. Phenopacket Store v.0.1.19 includes 6,668 phenopackets representing 475 Mendelian and chromosomal diseases associated with 423 genes and 3,834 unique pathogenic alleles curated from 959 different publications. This represents the first large-scale collection of case-level, standardized phenotypic information derived from case reports in the literature with detailed descriptions of the clinical data and will be useful for many purposes, including the development and testing of software for prioritizing genes and diseases in diagnostic genomics, machine learning analysis of clinical phenotype data, patient stratification, and genotype-phenotype correlations. This corpus also provides best-practice examples for curating literature-derived data using the GA4GH Phenopacket Schema.

Hypothesis generation for rare and undiagnosed diseases through clustering and classifying time-versioned biological ontologies

Rare diseases affect 1-in-10 people in the United States and despite increased genetic testing, up to half never receive a diagnosis. Even when using advanced genome sequencing platforms to discover variants, if there is no connection between the variants found in the patient's genome and their phenotypes in the literature, then the patient will remain undiagnosed. When a direct variant-phenotype connection is not known, putting a patient's information in the larger context of phenotype relationships and protein-protein interactions may provide an opportunity to find an indirect explanation. Databases such as STRING contain millions of protein-protein interactions, and the Human Phenotype Ontology (HPO) contains the relations of thousands of phenotypes. By integrating these networks and clustering the entities within, we can potentially discover latent gene-to-phenotype connections. The historical records for STRING and HPO provide a unique opportunity to create a network time series for evaluating the cluster significance. Most excitingly, working with Children's Hospital Colorado, we have provided promising hypotheses about latent gene-to-phenotype connections for 38 patients. We also provide potential answers for 14 patients listed on MyGene2. Clusters our tool finds significant harbor 2.35 to 8.72 times as many gene-to-phenotype edges inferred from known drug interactions than clusters found to be insignificant. Our tool, BOCC, is available as a web app and command line tool.

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

Over the past decade, there has been substantial growth in both the quantity and complexity of available biomedical data. In order to more efficiently harness this extensive data and alleviate challenges associated with integration of multi-omics data, we developed Petagraph, a biomedical knowledge graph that encompasses over 32 million nodes and 118 million relationships. Petagraph leverages more than 180 ontologies and standards in the Unified Biomedical Knowledge Graph (UBKG) to embed millions of quantitative genomics data points. Petagraph provides a cohesive data environment that enables users to efficiently analyze, annotate, and discern relationships within and across complex multi-omics datasets supported by UBKG's annotation scaffold. We demonstrate how queries on Petagraph can generate meaningful results across various research contexts and use cases.

CancerPro: deciphering the pan-cancer prognostic landscape through combinatorial enrichment analysis and knowledge network insights

Gene expression levels serve as valuable markers for assessing prognosis in cancer patients. To understand the mechanisms underlying prognosis and explore potential therapeutics across diverse cancers, we developed CancerPro (https:/medcode.link/cancerpro). This knowledge network platform integrates comprehensive biomedical data on genes, drugs, diseases and pathways, along with their interactions. By integrating ontology and knowledge graph technologies, CancerPro offers a user-friendly interface for analyzing pan-cancer prognostic markers and exploring genes or drugs of interest. CancerPro implements three core functions: gene set enrichment analysis based on multiple annotations; in-depth drug analysis; and in-depth gene list analysis. Using CancerPro, we categorized genes and cancers into distinct groups and utilized network analysis to identify key biological pathways associated with unfavorable prognostic genes. The platform further pinpoints potential drug targets and explores potential links between prognostic markers and patient characteristics such as glutathione levels and obesity. For renal and prostate cancer, CancerPro identified risk genes linked to immune deficiency pathways and alternative splicing abnormalities. This research highlights CancerPro's potential as a valuable tool for researchers to explore pan-cancer prognostic markers and uncover novel therapeutic avenues. Its flexible tools support a wide range of biological investigations, making it a versatile asset in cancer research and beyond.

Bioinformatics and validation reveal the potential target of curcumin in the treatment of diabetic peripheral neuropathy

Diabetic peripheral neuropathy (DPN) is a common nerve-damaging complication of diabetes mellitus. Effective treatments are needed to alleviate and reverse diabetes-associated damage to the peripheral nerves. Curcumin is an effective neuroprotectant that plays a protective role in DPN promoted by Schwann cells (SCs) lesions. However, the potential molecular mechanism of curcumin remains unclear. Therefore, our aim is to study the detailed molecular mechanism of curcumin-mediated SCs repair in order to improve the efficacy of curcumin in the clinical treatment of DPN. First, candidate target genes of curcumin in rat SC line RSC96 cells stimulated by high glucose were identified by RNA sequencing and bioinformatic analyses. Enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was carried out by Metascape, followed by 8 algorithms on Cytoscape to determine 4 hub genes, namly Hmox1, Pten, Vegfa and Myc. Next, gene set enrichment analysis (GSEA) and Pearson function showed that Hmox1 was significantly correlated with apoptosis. Subsequently, qRT-PCR, MTT assay, flow cytometry, caspase-3 activity detection and westernblot showed that curcumin treatment increased RSC96 cell viability, reduced cell apoptosis, increased Hmox1, Pten, Vegfa and Myc expression, and up-regulated Akt phosphorylation level under high glucose environment. Finally, molecular docking predicted the binding site of curcumin to Hmox1. These results suggest that curcumin can reduce the apoptosis of SCs induced by high glucose, and Hmox1 is a potential target for curcumin. Our findings provide new insights about the mechanism of action of curcumin on SC as a potential treatment in DPN.

Systematic benchmarking demonstrates large language models have not reached the diagnostic accuracy of traditional rare-disease decision support tools

Large language models (LLMs) show promise in supporting differential diagnosis, but their performance is challenging to evaluate due to the unstructured nature of their responses. To assess the current capabilities of LLMs to diagnose genetic diseases, we benchmarked these models on 5,213 case reports using the Phenopacket Schema, the Human Phenotype Ontology and Mondo disease ontology. Prompts generated from each phenopacket were sent to three generative pretrained transformer (GPT) models. The same phenopackets were used as input to a widely used diagnostic tool, Exomiser, in phenotype-only mode. The best LLM ranked the correct diagnosis first in 23.6% of cases, whereas Exomiser did so in 35.5% of cases. While the performance of LLMs for supporting differential diagnosis has been improving, it has not reached the level of commonly used traditional bioinformatics tools. Future research is needed to determine the best approach to incorporate LLMs into diagnostic pipelines.

Curating genomic disease-gene relationships with Gene2Phenotype (G2P)

Genetically determined disorders are highly heterogenous in clinical presentation and underlying molecular mechanism. The evidence underpinning these conditions in the peer-reviewed literature requires robust critical evaluation for diagnostic use. Here, we present a structured curation process for Gene2Phenotype (G2P). This draws on multiple lines of clinical, bioinformatic and functional evidence. The process utilises and extends existing terminologies, allows for precise definition of the molecular basis of disease, and confidence levels to be attributed to a given gene-disease assertion. In-depth disease curation using this process will prove useful in applications including in diagnostics, research and development of targeted therapeutics. G2P: www.ebi.ac.uk/gene2phenotype .

Working with Miraculous Mice: Mus musculus as a Model Organism

The laboratory mouse has been described as a "miracle" model organism, providing a window by which we may gain an understanding of ourselves. Since the first recorded mouse experiment in 1664, the mouse has become the most used animal model in biomedical research. Mice are ideally suited as a model organism because of their small size, short gestation period, large litter size, and genetic similarity to humans. This article provides a broad overview of the laboratory mouse as a model organism and is intended for undergraduates and those new to working with mice. We delve into the history of the laboratory mouse and outline important terminology to accurately describe research mice. The types of laboratory mice available to researchers are reviewed, including outbred stocks, inbred strains, immunocompromised mice, and genetically engineered mice. The critical role mice have played in advancing knowledge in the areas of oncology, immunology, and pharmacology is highlighted by examining the significant contribution of mice to Nobel Prize winning research. International mouse mutagenesis programs and accurate phenotyping of mouse models are outlined. We also explain important considerations for working with mice, including animal ethics; the welfare principles of replacement, refinement, and reduction; and the choice of mouse model in experimental design. Finally, we present practical advice for maintaining a mouse colony, which involves adequate training of staff, the logistics of mouse housing, monitoring colony health, and breeding strategies. Useful resources for working with mice are also listed. The aim of this overview is to equip the reader with a broad appreciation of the enormous potential and some of the complexities of working with the laboratory mouse in a quest to improve human health. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC.

Talking about diseases; developing a model of patient and public-prioritised disease phenotypes

Deep phenotyping describes the use of standardised terminologies to create comprehensive phenotypic descriptions of biomedical phenomena. These characterisations facilitate secondary analysis, evidence synthesis, and practitioner awareness, thereby guiding patient care. The vast majority of this knowledge is derived from sources that describe an academic understanding of disease, including academic literature and experimental databases. Previous work indicates a gulf between the priorities, perspectives, and perceptions held by different healthcare stakeholders. Using social media data, we develop a phenotype model that represents a public perspective on disease and compare this with a model derived from a combination of existing academic phenotype databases. We identified 52,198 positive disease-phenotype associations from social media across 311 diseases. We further identified 24,618 novel phenotype associations not shared by the biomedical and literature-derived phenotype model across 304 diseases, of which we considered 14,531 significant. Manifestations of disease affecting quality of life, and concerning endocrine, digestive, and reproductive diseases were over-represented in the social media phenotype model. An expert clinical review found that social media-derived associations were considered similarly well-established to those derived from literature, and were seen significantly more in patient clinical encounters. The phenotype model recovered from social media presents a significantly different perspective than existing resources derived from biomedical databases and literature, providing a large number of associations novel to the latter dataset. We propose that the integration and interrogation of these public perspectives on the disease can inform clinical awareness, improve secondary analysis, and bridge understanding and priorities across healthcare stakeholders.

Leaving no patient behind! Expert recommendation in the use of innovative technologies for diagnosing rare diseases

Genetic diagnosis plays a crucial role in rare diseases, particularly with the increasing availability of emerging and accessible treatments. The International Rare Diseases Research Consortium (IRDiRC) has set its primary goal as: "Ensuring that all patients who present with a suspected rare disease receive a diagnosis within one year if their disorder is documented in the medical literature". Despite significant advances in genomic sequencing technologies, more than half of the patients with suspected Mendelian disorders remain undiagnosed. In response, IRDiRC proposes the establishment of "a globally coordinated diagnostic and research pipeline". To help facilitate this, IRDiRC formed the Task Force on Integrating New Technologies for Rare Disease Diagnosis. This multi-stakeholder Task Force aims to provide an overview of the current state of innovative diagnostic technologies for clinicians and researchers, focusing on the patient's diagnostic journey. Herein, we provide an overview of a broad spectrum of emerging diagnostic technologies involving genomics, epigenomics and multi-omics, functional testing and model systems, data sharing, bioinformatics, and Artificial Intelligence (AI), highlighting their advantages, limitations, and the current state of clinical adaption. We provide expert recommendations outlining the stepwise application of these innovative technologies in the diagnostic pathways while considering global differences in accessibility. The importance of FAIR (Findability, Accessibility, Interoperability, and Reusability) and CARE (Collective benefit, Authority to control, Responsibility, and Ethics) data management is emphasized, along with the need for enhanced and continuing education in medical genomics. We provide a perspective on future technological developments in genome diagnostics and their integration into clinical practice. Lastly, we summarize the challenges related to genomic diversity and accessibility, highlighting the significance of innovative diagnostic technologies, global collaboration, and equitable access to diagnosis and treatment for people living with rare disease.

Paving the way to a neural fate - RNA signatures in naive and trans-differentiating mesenchymal stem cells

Mesenchymal Stem Cells (MSCs) derived from the embryonic mesoderm persist as a viable source of multipotent cells in adults and have a crucial role in tissue repair. One of the most promising aspects of MSCs is their ability to trans-differentiate into cell types outside of the mesodermal lineage, such as neurons. This characteristic positions MSCs as potential therapeutic tools for neurological disorders. However, the definition of a clear MSC signature is an ongoing topic of debate. Likewise, there is still a significant knowledge gap about functional alterations of MSCs during their transition to a neural fate. In this study, our focus is on the dynamic expression of RNA in MSCs as they undergo trans-differentiation compared to undifferentiated MSCs. To track and correlate changes in cellular signaling, we conducted high-throughput RNA expression profiling during the early time-course of human MSC neurogenic trans-differentiation. The expression of synapse maturation markers, including NLGN2 and NPTX1, increased during the first 24 h. The expression of neuron differentiation markers, such as GAP43 strongly increased during 48 h of trans-differentiation. Neural stem cell marker NES and neuron differentiation marker, including TUBB3 and ENO1, were highly expressed in mesenchymal stem cells and remained so during trans-differentiation. Pathways analyses revealed early changes in MSCs signaling that can be linked to the acquisition of neuronal features. Furthermore, we identified microRNAs (miRNAs) as potential drivers of the cellular trans-differentiation process. We also determined potential risk factors related to the neural trans-differentiation process. These factors include the persistence of stemness features and the expression of factors involved in neurofunctional abnormalities and tumorigenic processes. In conclusion, our findings contribute valuable insights into the intricate landscape of MSCs during neural trans-differentiation. These insights can pave the way for the development of safer treatments of neurological disorders.

Graph databases in systems biology: a systematic review

Graph databases are becoming increasingly popular across scientific disciplines, being highly suitable for storing and connecting complex heterogeneous data. In systems biology, they are used as a backend solution for biological data repositories, ontologies, networks, pathways, and knowledge graph databases. In this review, we analyse all publications using or mentioning graph databases retrieved from PubMed and PubMed Central full-text search, focusing on the top 16 available graph databases, Publications are categorized according to their domain and application, focusing on pathway and network biology and relevant ontologies and tools. We detail different approaches and highlight the advantages of outstanding resources, such as UniProtKB, Disease Ontology, and Reactome, which provide graph-based solutions. We discuss ongoing efforts of the systems biology community to standardize and harmonize knowledge graph creation and the maintenance of integrated resources. Outlining prospects, including the use of graph databases as a way of communication between biological data repositories, we conclude that efficient design, querying, and maintenance of graph databases will be key for knowledge generation in systems biology and other research fields with heterogeneous data.

Triangulating evidence in health sciences with Annotated Semantic Queries

MOTIVATION

Integrating information from data sources representing different study designs has the potential to strengthen evidence in population health research. However, this concept of evidence "triangulation" presents a number of challenges for systematically identifying and integrating relevant information. These include the harmonization of heterogenous evidence with common semantic concepts and properties, as well as the priortization of the retrieved evidence for triangulation with the question of interest.

RESULTS

We present Annotated Semantic Queries (ASQ), a natural language query interface to the integrated biomedical entities and epidemiological evidence in EpiGraphDB, which enables users to extract "claims" from a piece of unstructured text, and then investigate the evidence that could either support, contradict the claims, or offer additional information to the query. This approach has the potential to support the rapid review of preprints, grant applications, conference abstracts, and articles submitted for peer review. ASQ implements strategies to harmonize biomedical entities in different taxonomies and evidence from different sources, to facilitate evidence triangulation and interpretation.

AVAILABILITY AND IMPLEMENTATION

ASQ is openly available at https://asq.epigraphdb.org and its source code is available at https://github.com/mrcieu/epigraphdb-asq under GPL-3.0 license.

XGRm: A Web Server for Interpreting Mouse Summary-level Genomic Data

We introduce XGR-model (or XGRm), a web server made accessible at http://www.xgrm.pro, with the aim of meeting the increasing demand for effectively interpreting summary-level genomic data in model organisms. Currently, it hosts two enrichment analysers and two subnetwork analysers to support enrichment and subnetwork analyses for user-input mouse genomic data, whether gene-centric or genomic region-centric. The enrichment analysers identify ontology term enrichments for input genes (GElyser) or for genes linked from input genomic regions (RElyser). The subnetwork analysers rely on our previously established network algorithm to identify gene subnetworks from input gene-centric summary data (GSlyser) or from input region-centric summary data (RSlyser), leveraging network information about either functional interactions or pathway-derived interactions. Collectively, XGRm offers an all-in-one solution for gaining systems biology insights into summary-level genomic data in mice, underpinned by our commitment to regular updates as well as natural extensions to other model organisms.

An ontology-based knowledge graph for representing interactions involving RNA molecules

The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to each patient's biomolecular characteristics. Although scientific data about coding and non-coding RNA molecules are constantly produced and available from public repositories, they are scattered across different databases and a centralized, uniform, and semantically consistent representation of the "RNA world" is still lacking. We propose RNA-KG, a knowledge graph (KG) encompassing biological knowledge about RNAs gathered from more than 60 public databases, integrating functional relationships with genes, proteins, and chemicals and ontologically grounded biomedical concepts. To develop RNA-KG, we first identified, pre-processed, and characterized each data source; next, we built a meta-graph that provides an ontological description of the KG by representing all the bio-molecular entities and medical concepts of interest in this domain, as well as the types of interactions connecting them. Finally, we leveraged an instance-based semantically abstracted knowledge model to specify the ontological alignment according to which RNA-KG was generated. RNA-KG can be downloaded in different formats and also queried by a SPARQL endpoint. A thorough topological analysis of the resulting heterogeneous graph provides further insights into the characteristics of the "RNA world". RNA-KG can be both directly explored and visualized, and/or analyzed by applying computational methods to infer bio-medical knowledge from its heterogeneous nodes and edges. The resource can be easily updated with new experimental data, and specific views of the overall KG can be extracted according to the bio-medical problem to be studied.

Computational strategies for cross-species knowledge transfer and translational biomedicine

Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. We introduce the term "agnology" to describe the functional equivalence of molecular components regardless of evolutionary origin, as this concept is becoming pervasive in integrative data-driven models where the role of evolutionary origin can become unclear. Our review addresses four key areas of information and knowledge transfer across species: (1) transferring disease and gene annotation knowledge, (2) identifying agnologous molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying agnologous cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer.

Understanding the molecular mechanisms of human diseases: the benefits of fission yeasts

The role of model organisms such as yeasts in life science research is crucial. Although the baker's yeast (Saccharomyces cerevisiae) is the most popular model among yeasts, the contribution of the fission yeasts (Schizosaccharomyces) to life science is also indisputable. Since both types of yeasts share several thousands of common orthologous genes with humans, they provide a simple research platform to investigate many fundamental molecular mechanisms and functions, thereby contributing to the understanding of the background of human diseases. In this review, we would like to highlight the many advantages of fission yeasts over budding yeasts. The usefulness of fission yeasts in virus research is shown as an example, presenting the most important research results related to the Human Immunodeficiency Virus Type 1 (HIV-1) Vpr protein. Besides, the potential role of fission yeasts in the study of prion biology is also discussed. Furthermore, we are keen to promote the uprising model yeast Schizosaccharomyces japonicus, which is a dimorphic species in the fission yeast genus. We propose the hyphal growth of S. japonicus as an unusual opportunity as a model to study the invadopodia of human cancer cells since the two seemingly different cell types can be compared along fundamental features. Here we also collect the latest laboratory protocols and bioinformatics tools for the fission yeasts to highlight the many possibilities available to the research community. In addition, we present several limiting factors that everyone should be aware of when working with yeast models.

Predicting the potential risks posed by antidepressants as emerging contaminants in fish based on network pharmacological analysis

This study conducted a network pharmacology-based analysis to simultaneously discern a broad spectrum of potential environmental risks and health effects of antidepressants, a common class of pharmaceutical emerging contaminants (PECs) possessing a complex pharmacological profile, and in silico predict the adverse phenotypes potentially occurring in fish associated with exposure to antidepressants and their mixtures under realistic exposure scenarios. Results showed that 24 of the included 39 antidepressants had been detected worldwide in water environment across 50 countries. Using the environmentally realistic exposure scenario for China as an example, the predicted blood concentrations of antidepressant residues that were generated based on the Fish Plasma Model ranged from 37.89 (Alprazolam) to 16,772.05 (Sertraline) ng/L in exposed fish. Hazard-based bioactivity network without regard to concentration data was composed of 148 potential targets and 701 antidepressant-target interactions. After filtering each antidepressant-target interaction node using the predicted drug concentrations in the blood of fish under realistic exposure scenarios in China, an environmental risk-based network was refined and showed that 11 targets, including muscarinic acetylcholine receptor M1, alpha-2B adrenergic receptor, serotonin 2 A receptor, etc. might be modulated by antidepressants at concentrations equal to or below the environmental exposure levels and their mixtures in fish. Environmentally relevant concentrations of antidepressants in water samples from China might perturb the behavior, stress response, phototaxis, and development in exposed fish.

Gene Set Summarization Using Large Language Models

Molecular biologists frequently interpret gene lists derived from high-throughput experiments and computational analysis. This is typically done as a statistical enrichment analysis that measures the over- or under-representation of biological function terms associated with genes or their properties, based on curated assertions from a knowledge base (KB) such as the Gene Ontology (GO). Interpreting gene lists can also be framed as a textual summarization task, enabling Large Language Models (LLMs) to use scientific texts directly and avoid reliance on a KB. TALISMAN (Terminological ArtificiaL Intelligence SuMmarization of Annotation and Narratives) uses generative AI to perform gene set function summarization as a complement to standard enrichment analysis. This method can use different sources of gene functional information: (1) structured text derived from curated ontological KB annotations, (2) ontology-free narrative gene summaries, or (3) direct retrieval from the model. We demonstrate that these methods are able to generate plausible and biologically valid summary GO term lists for an input gene set. However, LLM-based approaches are unable to deliver reliable scores or p-values and often return terms that are not statistically significant. Crucially, in our experiments these methods were rarely able to recapitulate the most precise and informative term from standard enrichment analysis. We also observe minor differences depending on prompt input information, with GO term descriptions leading to higher recall but lower precision. However, newer LLM models perform statistically significantly better than the oldest model across all performance metrics, suggesting that future models may lead to further improvements. Overall, the results are nondeterministic, with minor variations in prompt resulting in radically different term lists, true to the stochastic nature of LLMs. Our results show that at this point, LLM-based methods are unsuitable as a replacement for standard term enrichment analysis, however they may provide summarization benefits for implicit knowledge integration across extant but unstandardized knowledge, for large sets of features, and where the amount of information is difficult for humans to process.

FastHPOCR: pragmatic, fast, and accurate concept recognition using the human phenotype ontology

MOTIVATION

Human Phenotype Ontology (HPO)-based phenotype concept recognition (CR) underpins a faster and more effective mechanism to create patient phenotype profiles or to document novel phenotype-centred knowledge statements. While the increasing adoption of large language models (LLMs) for natural language understanding has led to several LLM-based solutions, we argue that their intrinsic resource-intensive nature is not suitable for realistic management of the phenotype CR lifecycle. Consequently, we propose to go back to the basics and adopt a dictionary-based approach that enables both an immediate refresh of the ontological concepts as well as efficient re-analysis of past data.

RESULTS

We developed a dictionary-based approach using a pre-built large collection of clusters of morphologically equivalent tokens-to address lexical variability and a more effective CR step by reducing the entity boundary detection strictly to candidates consisting of tokens belonging to ontology concepts. Our method achieves state-of-the-art results (0.76 F1 on the GSC+ corpus) and a processing efficiency of 10 000 publication abstracts in 5 s.

AVAILABILITY AND IMPLEMENTATION

FastHPOCR is available as a Python package installable via pip. The source code is available at https://github.com/tudorgroza/fast_hpo_cr. A Java implementation of FastHPOCR will be made available as part of the Fenominal Java library available at https://github.com/monarch-initiative/fenominal. The up-to-date GCS-2024 corpus is available at https://github.com/tudorgroza/code-for-papers/tree/main/gsc-2024.

Computable species descriptions and nanopublications: applying ontology-based technologies to dung beetles (Coleoptera, Scarabaeinae)

Background

Taxonomy has long struggled with analysing vast amounts of phenotypic data due to computational and accessibility challenges. Ontology-based technologies provide a framework for modelling semantic phenotypes that are understandable by computers and compliant with FAIR principles. In this paper, we explore the use of Phenoscript, an emerging language designed for creating semantic phenotypes, to produce computable species descriptions. Our case study centers on the application of this approach to dung beetles (Coleoptera, Scarabaeinae).

New information

We illustrate the effectiveness of Phenoscript for creating semantic phenotypes. We also demonstrate the ability of the Phenospy python package to automatically translate Phenoscript descriptions into natural language (NL), which eliminates the need for writing traditional NL descriptions. We introduce a computational pipeline that streamlines the generation of semantic descriptions and their conversion to NL. To demonstrate the power of the semantic approach, we apply simple semantic queries to the generated phenotypic descriptions. This paper addresses the current challenges in crafting semantic species descriptions and outlines the path towards future improvements. Furthermore, we discuss the promising integration of semantic phenotypes and nanopublications, as emerging methods for sharing scientific information. Overall, our study highlights the pivotal role of ontology-based technologies in modernising taxonomy and aligning it with the evolving landscape of big data analysis and FAIR principles.

A corpus of GA4GH Phenopackets: case-level phenotyping for genomic diagnostics and discovery

The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments. There has been a great need for a collection of phenopackets to test software pipelines and algorithms. Here, we present phenopacket-store. Version 0.1.12 of phenopacket-store includes 4916 phenopackets representing 277 Mendelian and chromosomal diseases associated with 236 genes, and 2872 unique pathogenic alleles curated from 605 different publications. This represents the first large-scale collection of case-level, standardized phenotypic information derived from case reports in the literature with detailed descriptions of the clinical data and will be useful for many purposes, including the development and testing of software for prioritizing genes and diseases in diagnostic genomics, machine learning analysis of clinical phenotype data, patient stratification, and genotype-phenotype correlations. This corpus also provides best-practice examples for curating literature-derived data using the GA4GH Phenopacket Schema.

PomBase: a Global Core Biodata Resource-growth, collaboration, and sustainability

PomBase (https://www.pombase.org), the model organism database (MOD) for fission yeast, was recently awarded Global Core Biodata Resource (GCBR) status by the Global Biodata Coalition (GBC; https://globalbiodata.org/) after a rigorous selection process. In this MOD review, we present PomBase's continuing growth and improvement over the last 2 years. We describe these improvements in the context of the qualitative GCBR indicators related to scientific quality, comprehensivity, accelerating science, user stories, and collaborations with other biodata resources. This review also showcases the depth of existing connections both within the biocuration ecosystem and between PomBase and its user community.

Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes

Germline and somatic mutations can give rise to proteins with altered activity, including both gain and loss-of-function. The effects of these variants can be captured in disease-specific reactions and pathways that highlight the resulting changes to normal biology. A disease reaction is defined as an aberrant reaction in which a variant protein participates. A disease pathway is defined as a pathway that contains a disease reaction. Annotation of disease variants as participants of disease reactions and disease pathways can provide a standardized overview of molecular phenotypes of pathogenic variants that is amenable to computational mining and mathematical modeling. Reactome (https://reactome.org/), an open source, manually curated, peer-reviewed database of human biological pathways, in addition to providing annotations for >11 000 unique human proteins in the context of ∼15 000 wild-type reactions within more than 2000 wild-type pathways, also provides annotations for >4000 disease variants of close to 400 genes as participants of ∼800 disease reactions in the context of ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, described in wild-type reactions and pathways, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Reactome's data model enables mapping of disease variant datasets to specific disease reactions within disease pathways, providing a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity. Database URL: https://reactome.org/.

Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning

MOTIVATION

Whole-exome and genome sequencing have become common tools in diagnosing patients with rare diseases. Despite their success, this approach leaves many patients undiagnosed. A common argument is that more disease variants still await discovery, or the novelty of disease phenotypes results from a combination of variants in multiple disease-related genes. Interpreting the phenotypic consequences of genomic variants relies on information about gene functions, gene expression, physiology, and other genomic features. Phenotype-based methods to identify variants involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been successfully applied to prioritizing variants, such methods are based on known gene-disease or gene-phenotype associations as training data and are applicable to genes that have phenotypes associated, thereby limiting their scope. In addition, phenotypes are not assigned uniformly by different clinicians, and phenotype-based methods need to account for this variability.

RESULTS

We developed an Embedding-based Phenotype Variant Predictor (EmbedPVP), a computational method to prioritize variants involved in genetic diseases by combining genomic information and clinical phenotypes. EmbedPVP leverages a large amount of background knowledge from human and model organisms about molecular mechanisms through which abnormal phenotypes may arise. Specifically, EmbedPVP incorporates phenotypes linked to genes, functions of gene products, and the anatomical site of gene expression, and systematically relates them to their phenotypic effects through neuro-symbolic, knowledge-enhanced machine learning. We demonstrate EmbedPVP's efficacy on a large set of synthetic genomes and genomes matched with clinical information.

AVAILABILITY AND IMPLEMENTATION

EmbedPVP and all evaluation experiments are freely available at https://github.com/bio-ontology-research-group/EmbedPVP.

An open source knowledge graph ecosystem for the life sciences

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

CoMentG: comprehensive retrieval of generic relationships between biomedical concepts from the scientific literature

The CoMentG resource contains millions of relationships between terms of biomedical interest obtained from the scientific literature. At the core of the system is a methodology for detecting significant co-mentions of concepts in the entire PubMed corpus. That method was applied to nine sets of terms covering the most important classes of biomedical concepts: diseases, symptoms/clinical signs, molecular functions, biological processes, cellular compartments, anatomic parts, cell types, bacteria and chemical compounds. We obtained more than 7 million relationships between more than 74 000 terms, and many types of relationships were not available in any other resource. As the terms were obtained from widely used resources and ontologies, the relationships are given using the standard identifiers provided by them and hence can be linked to other data. A web interface allows users to browse these associations, searching for relationships for a set of terms of interests provided as input, such as between a disease and their associated symptoms, underlying molecular processes or affected tissues. The results are presented in an interactive interface where the user can explore the reported relationships in different ways and follow links to other resources. Database URL: https://csbg.cnb.csic.es/CoMentG/.

Transcriptomics- and Genomics-Guided Drug Repurposing for the Treatment of Vesicular Hand Eczema

Vesicular hand eczema (VHE), a clinical subtype of hand eczema (HE), showed limited responsiveness to alitretinoin, the only approved systemic treatment for severe chronic HE. This emphasizes the need for alternative treatment approaches. Therefore, our study aimed to identify drug repurposing opportunities for VHE using transcriptomics and genomics data. We constructed a gene network by combining 52 differentially expressed genes (DEGs) from a VHE transcriptomics study with 3 quantitative trait locus (QTL) genes associated with HE. Through network analysis, clustering, and functional enrichment analyses, we investigated the underlying biological mechanisms of this network. Next, we leveraged drug-gene interactions and retrieved pharmaco-transcriptomics data from the DrugBank database to identify drug repurposing opportunities for (V)HE. We developed a drug ranking system, primarily based on efficacy, safety, and practical and pricing factors, to select the most promising drug repurposing candidates. Our results revealed that the (V)HE network comprised 78 genes that yielded several biological pathways underlying the disease. The drug-gene interaction search together with pharmaco-transcriptomics lookups revealed 123 unique drug repurposing opportunities. Based on our drug ranking system, our study identified the most promising drug repurposing opportunities (e.g., vitamin D analogues, retinoids, and immunomodulating drugs) that might be effective in treating (V)HE.

Prioritization of oligogenic variant combinations in whole exomes

MOTIVATION

Whole exome sequencing (WES) has emerged as a powerful tool for genetic research, enabling the collection of a tremendous amount of data about human genetic variation. However, properly identifying which variants are causative of a genetic disease remains an important challenge, often due to the number of variants that need to be screened. Expanding the screening to combinations of variants in two or more genes, as would be required under the oligogenic inheritance model, simply blows this problem out of proportion.

RESULTS

We present here the High-throughput oligogenic prioritizer (Hop), a novel prioritization method that uses direct oligogenic information at the variant, gene and gene pair level to detect digenic variant combinations in WES data. This method leverages information from a knowledge graph, together with specialized pathogenicity predictions in order to effectively rank variant combinations based on how likely they are to explain the patient's phenotype. The performance of Hop is evaluated in cross-validation on 36 120 synthetic exomes for training and 14 280 additional synthetic exomes for independent testing. Whereas the known pathogenic variant combinations are found in the top 20 in approximately 60% of the cross-validation exomes, 71% are found in the same ranking range when considering the independent set. These results provide a significant improvement over alternative approaches that depend simply on a monogenic assessment of pathogenicity, including early attempts for digenic ranking using monogenic pathogenicity scores.

AVAILABILITY AND IMPLEMENTATION

Hop is available at https://github.com/oligogenic/HOP.

Classical cannabinoid receptors as target in cancer-induced bone pain: a systematic review, meta-analysis and bioinformatics validation

To test the hypothesis that genetic and pharmacological modulation of the classical cannabinoid type 1 (CB1) and 2 (CB2) receptors attenuate cancer-induced bone pain, we searched Medline, Web of Science and Scopus for relevant skeletal and non-skeletal cancer studies from inception to July 28, 2022. We identified 29 animal and 35 human studies. In mice, a meta-analysis of pooled studies showed that treatment of osteolysis-bearing males with the endocannabinoids AEA and 2-AG (mean difference [MD] - 24.83, 95% confidence interval [95%CI] - 34.89, - 14.76, p < 0.00001) or the synthetic cannabinoid (CB) agonists ACPA, WIN55,212-2, CP55,940 (CB1/2-non-selective) and AM1241 (CB2-selective) (MD - 28.73, 95%CI - 45.43, - 12.02, p = 0.0008) are associated with significant reduction in paw withdrawal frequency. Consistently, the synthetic agonists AM1241 and JWH015 (CB2-selective) increased paw withdrawal threshold (MD 0.89, 95%CI 0.79, 0.99, p < 0.00001), and ACEA (CB1-selective), AM1241 and JWH015 (CB2-selective) reduced spontaneous flinches (MD - 4.85, 95%CI - 6.74, - 2.96, p < 0. 00001) in osteolysis-bearing male mice. In rats, significant increase in paw withdrawal threshold is associated with the administration of ACEA and WIN55,212-2 (CB1/2-non-selective), JWH015 and AM1241 (CB2-selective) in osteolysis-bearing females (MD 8.18, 95%CI 6.14, 10.21, p < 0.00001), and treatment with AM1241 (CB2-selective) increased paw withdrawal thermal latency in males (mean difference [MD]: 3.94, 95%CI 2.13, 5.75, p < 0.0001), confirming the analgesic capabilities of CB1/2 ligands in rodents. In human, treatment of cancer patients with medical cannabis (standardized MD - 0.19, 95%CI - 0.35, - 0.02, p = 0.03) and the plant-derived delta-9-THC (20 mg) (MD 3.29, CI 2.24, 4.33, p < 0.00001) or its synthetic derivative NIB (4 mg) (MD 2.55, 95%CI 1.58, 3.51, p < 0.00001) are associated with reduction in pain intensity. Bioinformatics validation of KEGG, GO and MPO pathway, function and process enrichment analysis of mouse, rat and human data revealed that CB1 and CB2 receptors are enriched in a cocktail of nociceptive and sensory perception, inflammatory, immune-modulatory, and cancer pathways. Thus, we cautiously conclude that pharmacological modulators of CB1/2 receptors show promise in the treatment of cancer-induced bone pain, however further assessment of their effects on bone pain in genetically engineered animal models and cancer patients is warranted.

preon: Fast and accurate entity normalization for drug names and cancer types in precision oncology

MOTIVATION

In precision oncology (PO), clinicians aim to find the best treatment for any patient based on their molecular characterization. A major bottleneck is the manual annotation and evaluation of individual variants, for which usually a range of knowledge bases are screened. To incorporate and integrate the vast information of different databases, fast and accurate methods for harmonizing databases with different types of information are necessary. An essential step for harmonization in PO includes the normalization of tumor entities as well as therapy options for patients.

SUMMARY

preon is a fast and accurate library for the normalization of drug names and cancer types in large-scale data integration.

AVAILABILITY AND IMPLEMENTATION

preon is implemented in Python and freely available via the PyPI repository. Source code and the data underlying this article are available in GitHub at https://github.com/ermshaua/preon/.

Cross-Species Extrapolation of Biological Data to Guide the Environmental Safety Assessment of Pharmaceuticals-The State of the Art and Future Priorities

The extrapolation of biological data across species is a key aspect of biomedical research and drug development. In this context, comparative biology considerations are applied with the goal of understanding human disease and guiding the development of effective and safe medicines. However, the widespread occurrence of pharmaceuticals in the environment and the need to assess the risk posed to wildlife have prompted a renewed interest in the extrapolation of pharmacological and toxicological data across the entire tree of life. To address this challenge, a biological "read-across" approach, based on the use of mammalian data to inform toxicity predictions in wildlife species, has been proposed as an effective way to streamline the environmental safety assessment of pharmaceuticals. Yet, how effective has this approach been, and are we any closer to being able to accurately predict environmental risk based on known human risk? We discuss the main theoretical and experimental advancements achieved in the last 10 years of research in this field. We propose that a better understanding of the functional conservation of drug targets across species and of the quantitative relationship between target modulation and adverse effects should be considered as future research priorities. This pharmacodynamic focus should be complemented with the application of higher-throughput experimental and computational approaches to accelerate the prediction of internal exposure dynamics. The translation of comparative (eco)toxicology research into real-world applications, however, relies on the (limited) availability of experts with the skill set needed to navigate the complexity of the problem; hence, we also call for synergistic multistakeholder efforts to support and strengthen comparative toxicology research and education at a global level. Environ Toxicol Chem 2024;43:513-525. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

TransEFVP: A Two-Stage Approach for the Prediction of Human Pathogenic Variants Based on Protein Sequence Embedding Fusion

Studying the effect of single amino acid variations (SAVs) on protein structure and function is integral to advancing our understanding of molecular processes, evolutionary biology, and disease mechanisms. Screening for deleterious variants is one of the crucial issues in precision medicine. Here, we propose a novel computational approach, TransEFVP, based on large-scale protein language model embeddings and a transformer-based neural network to predict disease-associated SAVs. The model adopts a two-stage architecture: the first stage is designed to fuse different feature embeddings through a transformer encoder. In the second stage, a support vector machine model is employed to quantify the pathogenicity of SAVs after dimensionality reduction. The prediction performance of TransEFVP on blind test data achieves a Matthews correlation coefficient of 0.751, an F1-score of 0.846, and an area under the receiver operating characteristic curve of 0.871, higher than the existing state-of-the-art methods. The benchmark results demonstrate that TransEFVP can be explored as an accurate and effective SAV pathogenicity prediction method. The data and codes for TransEFVP are available at https://github.com/yzh9607/TransEFVP/tree/master for academic use.

Improved phenotypic classification of male infertility to promote discovery of genetic causes

An increasing number of genes are being described in the context of non-syndromic male infertility. Linking the underlying genetic causes of non-syndromic male infertility with clinical data from patients is important to establish new genotype-phenotype correlations. This process can be facilitated by using universal nomenclature, but no standardized vocabulary is available in the field of non-syndromic male infertility. The International Male Infertility Genomics Consortium aimed at filling this gap, providing a standardized vocabulary containing nomenclature based on the Human Phenotype Ontology (HPO). The "HPO tree" was substantially revised compared with the previous version and is based on the clinical work-up of infertile men, including physical examination and hormonal assessment. Some causes of male infertility can already be suspected based on the patient's clinical history, whereas in other instances, a testicular biopsy is needed for diagnosis. We assembled 49 HPO terms that are linked in a logical hierarchy and showed examples of morphological features of spermatozoa and testicular histology of infertile men with identified genetic diagnoses to describe the phenotypes. This work will help to record patients' phenotypes systematically and facilitate communication between geneticists and andrologists. Collaboration across institutions will improve the identification of patients with the same phenotypes, which will promote the discovery of novel genetic causes for non-syndromic male infertility.

An evaluation of GPT models for phenotype concept recognition

OBJECTIVE

Clinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-tractable knowledge in the rare disorders field. These processes rely on using ontology concepts, often from the Human Phenotype Ontology, in conjunction with a phenotype concept recognition task (supported usually by machine learning methods) to curate patient profiles or existing scientific literature. With the significant shift in the use of large language models (LLMs) for most NLP tasks, we examine the performance of the latest Generative Pre-trained Transformer (GPT) models underpinning ChatGPT as a foundation for the tasks of clinical phenotyping and phenotype annotation.

MATERIALS AND METHODS

The experimental setup of the study included seven prompts of various levels of specificity, two GPT models (gpt-3.5-turbo and gpt-4.0) and two established gold standard corpora for phenotype recognition, one consisting of publication abstracts and the other clinical observations.

RESULTS

The best run, using in-context learning, achieved 0.58 document-level F1 score on publication abstracts and 0.75 document-level F1 score on clinical observations, as well as a mention-level F1 score of 0.7, which surpasses the current best in class tool. Without in-context learning, however, performance is significantly below the existing approaches.

CONCLUSION

Our experiments show that gpt-4.0 surpasses the state of the art performance if the task is constrained to a subset of the target ontology where there is prior knowledge of the terms that are expected to be matched. While the results are promising, the non-deterministic nature of the outcomes, the high cost and the lack of concordance between different runs using the same prompt and input make the use of these LLMs challenging for this particular task.

Docetaxel radiosensitizes castration-resistant prostate cancer by downregulating CAV-1

PURPOSE

Docetaxel (DXL), a noted radiosensitizer, is one of the few chemotherapy drugs approved for castration-resistant prostate cancer (CRPC), though only a fraction of CRPCs respond to it. CAV-1, a critical regulator of radioresistance, has been known to modulate DXL and radiation effects. Combining DXL with radiotherapy may create a synergistic anticancer effect through CAV-1 and improve CRPC patients' response to therapy. Here, we investigate the effectiveness and molecular characteristics of DXL and radiation combination therapy in vitro.

MATERIALS AND METHODS

We used live/dead assays to determine the IC50 of DXL for PC3, DU-145, and TRAMP-C1 cells. Colony formation assay was used to determine the radioresponse of the same cells treated with radiation with/without IC50 DXL (4, 8, and 12 Gy). We performed gene expression analysis on public transcriptomic data collected from human-derived prostate cancer cell lines (C4-2, PC3, DU-145, and LNCaP) treated with DXL for 8, 16, and 72 hours. Cell cycle arrest and protein expression were assessed using flow cytometry and western blot, respectively.

RESULTS

Compared to radiation alone, combination therapy with DXL significantly increased CRPC death in PC3 (1.48-fold, p < .0001), DU-145 (1.64-fold, p < .05), and TRAMP-C1 (1.13-fold, p < .05) at 4 Gy of radiation. Gene expression of CRPC treated with DXL revealed downregulated genes related to cell cycle regulation and upregulated genes related to immune activation and oxidative stress. Confirming the results, G2/M cell cycle arrest was significantly increased after treatment with DXL and radiation. CAV-1 protein expression was decreased after DXL treatment in a dose-dependent manner; furthermore, CAV-1 copy number was strongly associated with poor response to therapy in CRPC patients.

CONCLUSIONS

Our results suggest that DXL sensitizes CRPC cells to radiation by downregulating CAV-1. DXL + radiation combination therapy may be effective at treating CRPC, especially subtypes associated with high CAV-1 expression, and should be studied further.

The Human Phenotype Ontology in 2024: phenotypes around the world

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI's ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.

MultifacetedProtDB: a database of human proteins with multiple functions

MultifacetedProtDB is a database of multifunctional human proteins deriving information from other databases, including UniProt, GeneCards, Human Protein Atlas (HPA), Human Phenotype Ontology (HPO) and MONDO. It collects under the label 'multifaceted' multitasking proteins addressed in literature as pleiotropic, multidomain, promiscuous (in relation to enzymes catalysing multiple substrates) and moonlighting (with two or more molecular functions), and difficult to be retrieved with a direct search in existing non-specific databases. The study of multifunctional proteins is an expanding research area aiming to elucidate the complexities of biological processes, particularly in humans, where multifunctional proteins play roles in various processes, including signal transduction, metabolism, gene regulation and cellular communication, and are often involved in disease insurgence and progression. The webserver allows searching by gene, protein and any associated structural and functional information, like available structures from PDB, structural models and interactors, using multiple filters. Protein entries are supplemented with comprehensive annotations including EC number, GO terms (biological pathways, molecular functions, and cellular components), pathways from Reactome, subcellular localization from UniProt, tissue and cell type expression from HPA, and associated diseases following MONDO, Orphanet and OMIM classification. MultiFacetedProtDB is freely available as a web server at: https://multifacetedprotdb.biocomp.unibo.it/.

CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions

Machine Learning-based scoring and classification of genetic variants aids the assessment of clinical findings and is employed to prioritize variants in diverse genetic studies and analyses. Combined Annotation-Dependent Depletion (CADD) is one of the first methods for the genome-wide prioritization of variants across different molecular functions and has been continuously developed and improved since its original publication. Here, we present our most recent release, CADD v1.7. We explored and integrated new annotation features, among them state-of-the-art protein language model scores (Meta ESM-1v), regulatory variant effect predictions (from sequence-based convolutional neural networks) and sequence conservation scores (Zoonomia). We evaluated the new version on data sets derived from ClinVar, ExAC/gnomAD and 1000 Genomes variants. For coding effects, we tested CADD on 31 Deep Mutational Scanning (DMS) data sets from ProteinGym and, for regulatory effect prediction, we used saturation mutagenesis reporter assay data of promoter and enhancer sequences. The inclusion of new features further improved the overall performance of CADD. As with previous releases, all data sets, genome-wide CADD v1.7 scores, scripts for on-site scoring and an easy-to-use webserver are readily provided via https://cadd.bihealth.org/ or https://cadd.gs.washington.edu/ to the community.

The Medical Action Ontology: A tool for annotating and analyzing treatments and clinical management of human disease

BACKGROUND

Navigating the clinical literature to determine the optimal clinical management for rare diseases presents significant challenges. We introduce the Medical Action Ontology (MAxO), an ontology specifically designed to organize medical procedures, therapies, and interventions.

METHODS

MAxO incorporates logical structures that link MAxO terms to numerous other ontologies within the OBO Foundry. Term development involves a blend of manual and semi-automated processes. Additionally, we have generated annotations detailing diagnostic modalities for specific phenotypic abnormalities defined by the Human Phenotype Ontology (HPO). We introduce a web application, POET, that facilitates MAxO annotations for specific medical actions for diseases using the Mondo Disease Ontology.

FINDINGS

MAxO encompasses 1,757 terms spanning a wide range of biomedical domains, from human anatomy and investigations to the chemical and protein entities involved in biological processes. These terms annotate phenotypic features associated with specific disease (using HPO and Mondo). Presently, there are over 16,000 MAxO diagnostic annotations that target HPO terms. Through POET, we have created 413 MAxO annotations specifying treatments for 189 rare diseases.

CONCLUSIONS

MAxO offers a computational representation of treatments and other actions taken for the clinical management of patients. Its development is closely coupled to Mondo and HPO, broadening the scope of our computational modeling of diseases and phenotypic features. We invite the community to contribute disease annotations using POET (https://poet.jax.org/). MAxO is available under the open-source CC-BY 4.0 license (https://github.com/monarch-initiative/MAxO).

FUNDING

NHGRI 1U24HG011449-01A1 and NHGRI 5RM1HG010860-04.

Finding information about uncharacterized Drosophila melanogaster genes

Genes that have been identified in the genome but remain uncharacterized with regards to function offer an opportunity to uncover novel biological information. Novelty is exciting but can also be a barrier. If nothing is known, how does one start planning and executing experiments? Here, we provide a recommended information-mining workflow and a corresponding guide to accessing information about uncharacterized Drosophila melanogaster genes, such as those assigned only a systematic coding gene identifier. The available information can provide insights into where and when the gene is expressed, what the function of the gene might be, whether there are similar genes in other species, whether there are known relationships to other genes, and whether any other features have already been determined. In addition, available information about relevant reagents can inspire and facilitate experimental studies. Altogether, mining available information can help prioritize genes for further study, as well as provide starting points for experimental assays and other analyses.

Term-BLAST-like alignment tool for concept recognition in noisy clinical texts

MOTIVATION

Methods for concept recognition (CR) in clinical texts have largely been tested on abstracts or articles from the medical literature. However, texts from electronic health records (EHRs) frequently contain spelling errors, abbreviations, and other nonstandard ways of representing clinical concepts.

RESULTS

Here, we present a method inspired by the BLAST algorithm for biosequence alignment that screens texts for potential matches on the basis of matching k-mer counts and scores candidates based on conformance to typical patterns of spelling errors derived from 2.9 million clinical notes. Our method, the Term-BLAST-like alignment tool (TBLAT) leverages a gold standard corpus for typographical errors to implement a sequence alignment-inspired method for efficient entity linkage. We present a comprehensive experimental comparison of TBLAT with five widely used tools. Experimental results show an increase of 10% in recall on scientific publications and 20% increase in recall on EHR records (when compared against the next best method), hence supporting a significant enhancement of the entity linking task. The method can be used stand-alone or as a complement to existing approaches.

AVAILABILITY AND IMPLEMENTATION

Fenominal is a Java library that implements TBLAT for named CR of Human Phenotype Ontology terms and is available at https://github.com/monarch-initiative/fenominal under the GNU General Public License v3.0.