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Single-cell studies have delineated cellular diversity and uncovered increasing numbers of previously uncharacterized cell types in complex tissues. Thus, synthesizing growing knowledge of cellular characteristics is critical for dissecting cellular heterogeneity, developmental processes and tumorigenesis at single-cell resolution. Here, we present Cell Taxonomy (https://ngdc.cncb.ac.cn/celltaxonomy), a comprehensive and curated repository of cell types and associated cell markers encompassing a wide range of species, tissues and conditions. Combined with literature curation and data integration, the current version of Cell Taxonomy establishes a well-structured ontology for 3,143 cell types and houses a comprehensive collection of 26,613 associated cell markers in 257 conditions and 387 tissues across 34 species. Based on 4,299 publications and single-cell transcriptomic profiles of ∼3.5 million cells, Cell Taxonomy features multifaceted characterization for cell types and cell markers, involving quality assessment of cell markers and cell clusters, cross-species comparison, cell composition of tissues and cellular similarity based on markers. Taken together, Cell Taxonomy represents a fundamentally useful reference to systematically and accurately characterize cell types and thus lays an important foundation for deeply understanding and exploring cellular biology in diverse species.

Physiology and gene expression patterning are intrinsically linked. Yet, high-throughput sequencing methods often produce daunting amounts of data. Hence, functional enrichment analyses are important for deriving meaningful biological insights. Standard enrichment tools help characterise a feature of interest (e.g., disease-relevant cell type) in contrast to a background distribution (e.g., wildtype). As of now, they focus on the comparison of two conditions and thus do not allow for inferences about certain genes of interest (GOIs).
Here we present an approach to the comparative co-expression analysis focussed on a specific gene of interest (Gene-COCOA ). Gene-COCOA takes a list of curated gene sets as an input and ranks them according to their strength of association with the gene of interest (GOI). From each gene set, n genes are sampled as predictor variables in a linear regression modelling the expression of the GOI as the outcome variable. For bootstrapping, this procedure is repeated 10k times. The resulting model errors are compared in a t-test. Gene sets with adjusted P-values of <0.05 model the GOI expression better than random. This method provides insights on the functional association networks of a GOI whilst avoiding common artefacts arising from gene expression data.

The recent maturing of high-throughput sequencing technologies has revolutionized biology. Notably, it has enabled laboratories to routinely measure the global transcriptomic profiles of millions of biological entities, such as tissues or individual cells.

The analysis of these data often results in the identification of a gene signature, and its subsequent biological characterization is usually based on a singular source of curated knowledge, notably the Gene Ontology.

To study a gene signature under the scope of multiple controlled vocabularies instead, we have developed a computational tool called GORi (Gene-based Ontologies Relationships Inferences).

Given the gene annotations of the Gene Ontology, the Medical Subject Headings thesaurus, and the gene signature of a biological entity, GORi identifies the biological processes undertaken by the entity, and infers their associations to various diseases.

These results are obtained by measuring the co-occurence of gene annotations across the two controlled vocabularies, at every semantic resolution.
By coupling metrics borrowed from data-mining and biostatistical approaches, GORi identifes, characterizes and visualizes associations of interest.

Ongoing developments of GORi aim to build a data warehouse of seven controlled vocabularies, and to develop a local web application to make the tool more easily usable, for both biologists and bio-informaticians.

Rare diseases (RDs) are individually uncommon, but collectively they contribute to an enormous global disease burden. Yet we still do not understand the biological mechanisms by which most of these diseases act. Therefore, we utilised the gene Human Phenotype Ontology gene annotations and single-cell transcriptomic atlases to identify the cell types underlying > 6,000 phenotypes associated with >8,000 RDs. Our results both confirm well-known cell type-phenotype relationships and reveal previously unknown connections. We also demonstrate that the particular cell types underlying phenotypes (e.g. neonatal hypotonia, brachydactyly) predict differential clinical outcomes (age of death, severity) across diseases, opening avenues for mechanism-driven differential diagnosis in the clinic. Next, we identified candidate gene therapy targets based on phenotype severity, onset, and viral vector compatibility. Top candidates included respiratory failure (alveolar cells via CCNO), mental deterioration (neurons via APOE/CSTB), and coma (islet endocrine cells via INS/KCNJ11). Finally, we provide a user-friendly web app to enable clinicians, researchers, and patients to trace disease mechanisms down to the level of symptoms, cell types and genes. In summary, our findings have important implications for understanding disease biology at multi-scale resolution, and for the development of gene therapies to treat patients in a more targeted, mechanism-driven manner.

The Human Disease Ontology (DO) has established rigorous quality control and release procedures to enhance data rigor and discovery across the human disease open science data ecosystem. As a CC0 resource, the DO Knowledgebase (DO-KB) models, develops and shares models of complex diseases, software for capturing resource usage, ML-ready datasets and novel mechanisms for querying and retrieving disease datasets. Modeling best practices for ontology development, over the past 20 years, the DO has led the field in how to engage data contributors, to collaborate with other data repositories and to support software development for projects utilizing the DO to conduct analysis of disease-gene networks, disease repurposing, representing animal models of human diseases and for developing application ontologies that mine the DO content and structure to formulate novel data structures for modeling data for new ontological purposes.

Knowledge graphs are used to integrate diverse data in the life sciences as they are capable of representing complex data structures and connecting to other datasets. The RDF Portal of the Database Center for Life Science (DBCLS) operates on approximately 300 billion RDF triples stored in over 70 major bioinformatics databases. To effectively utilize knowledge graphs, novel methods are required to cross-reference multiple databases and efficiently extract subsets of information from massive databases, such as extracting information on human genes from others. The RDF-config tool, developed by DBCLS, allows for the representation of complex RDF data models for each database and the automatic generation of SPARQL queries and schema diagrams. Additionally, RDF-config generates configuration files for Grasp, another tool developed by DBCLS, which enables each database to be searched using GraphQL. These technologies provide a generic framework for extracting the necessary attributes from any combination of databases, facilitating the generation of datasets that are useful for data analysis in data science. In this presentation, we will discuss the current achievements and limitations, as well as future plans.

Sickle Cell Disease (SCD) is one of the world’s most common monogenic pathologies, with the majority of sufferers living in African countries where healthcare services are typically inadequate, leaving SCD management as mainly the responsibility of patients and their communities. The SCD Ontology (SCDO) is being used to standardise data collection across multiple research sites in Africa, however, SCDO terms are generally too technical and inaccessible to laypeople.
We adapted a workflow previously developed by us (for creating the French SCDO), to create a novel workflow which we used to produce the first English layperson SCDO. A subset of the SCDO layperson terms has already been used in a mobile health application prototype developed by the SickleInAfrica Consortium for SCD patients.
We aim to produce a French layperson SCDO and layperson versions of other future translations of the SCDO, to be used in making SCD-centred eHealth platforms more accessible to a broader audience. Furthermore, SCDO layperson terms can potentially be used to facilitate the retrieval of information from layperson sources, potentially leading to the discovery of effective novel alternative therapies employed by SCD patients. Notably, our novel workflow can be reused by ontologists to produce their own layperson ontology versions.

Chemotherapies follow well defined standard regimen (or protocols) recommended by scientific societies. Those are organized in cycles during which cytotoxic molecules, doses and days of administration are precisely specified. But in real life, treatment may not go as planned. Toxicity events, holidays and other factors lead to changes in doses and delay of administration, what may impact the effect of the treatment. Modeling both protocols and their real-word implementation in a unique framework would facilitate further comparisons.
To this aim, we propose an ontology named ChemoOnto to represent both protocols and treatment courses. ChemoOnto, provides 10 classes, 16 object properties and 24 data properties to model the complexity of chemotherapy and cover both standards and administered courses. ChemoOnto reuses several domain ontologies, particularly the Time Ontology and a drug knowledge graph named Romedi. We instantiated ChemoOnto with 1973 chemotherapy protocols and treatment data of 3,923 patients. We added toxicity events detected in a previous work to our knowledge graph and applied temporal reasoning using SWRL rules to detect toxicity events occurring during patient’s chemotherapies.
ChemoOnto is an original model that may support various applications to understand and analyze chemotherapy courses and response, by considering the complexity of their description.

Growth media serve diverse functions in microbial research, encompassing microbial isolation, identification, characterization, and growth. Designing an appropriate medium is vital for the comprehensive study of novel microorganisms. When formulating new media, it is customary to consult existing ones, and reference books and databases containing documented media recipes are valuable resources for this purpose. Nevertheless, the components of media outlined in these information sources often lack standardization. Growth media encompass a broad spectrum of ingredients, ranging from chemical compounds to natural substances, often characterized by multiple synonyms spanning generic and brand names. Consequently, the identification of media containing specific components or the comparative analysis of media based on their components has posed considerable challenges. To overcome these limitations, we have developed the Growth Medium Ontology (GMO), a comprehensive framework that aggregates and organizes growth medium components from literature and culture collections. Currently, the GMO ontology contains 1,345 components. Additionally, we have RDFized existing culture media using the GMO ontology and developed the TogoMedium database (http://togomedium.org) to facilitate access to standardized media. This presentation will explore the potential of leveraging growth medium information described with the ontology.