Molecular defects in primary ciliary dyskinesia are associated with agenesis of the frontal and sphenoid paranasal sinuses and chronic rhinosinusitis.

Background: Primary ciliary dyskinesia (PCD; MIM 242650) is a rare genetic disorder characterized by malfunction of the motile cilia resulting in reduced mucociliary clearance of the airways. Together with recurring infections of the lower respiratory tract, chronic rhinosinusitis (CRS) is a hallmark symptom of PCD. Data on genotype-phenotype correlations in the upper airways are scarce. Materials and methods: We investigated the prevalence, radiologic severity, and impact on health-related quality of life (HrQoL) of CRS in 58 individuals with genetically confirmed PCD. Subgroup analysis was performed according to the predicted ultrastructural phenotype based on genetic findings. Results: Among 58 individuals harboring pathogenic variants in 22 distinct genes associated with PCD, all were diagnosed with CRS, and 47% underwent sinus surgery. A total of 36 individuals answered a German-adapted version of the 20-item Sinonasal Outcome Test (SNOT-20-GAV) with a mean score of 35.8 ± 17, indicating a remarkably reduced HrQoL. Paranasal sinus imaging of 36 individuals showed moderate-to-severe opacification with an elevated Lund-Mackay Score (LMS) of 10.2 ± 4.4. Bilateral agenesis of frontal sinus (19%) and sphenoid sinus (9.5%) was a frequent finding in individuals aged 16 years or older. Subgroup analysis for predicted ultrastructural phenotypes did not identify differences in HrQoL, extent of sinus opacification, or frequency of aplastic paranasal sinuses. Conclusion: PCD is strongly associated with CRS. The high burden of disease is indicated by decreased HrQoL. Therefore, the upper airways of PCD individuals should be evaluated and managed by ear-nose-throat (ENT) specialists. Genetically determined PCD groups with predicted abnormal versus (near) normal ultrastructure did not differ in disease severity. Further studies are needed to gain evidence-based knowledge of the phenotype and management of upper airway manifestations in PCD. In addition, individuals with agenesis of the frontal and sphenoid paranasal sinuses and chronic respiratory symptoms should be considered for a diagnostic evaluation of PCD.

A Temporal Graph Model to Predict Chemical Transformations in Complex Dissolved Organic Matter.

Dissolved organic matter (DOM) is a complex mixture of thousands of natural molecules that undergo constant transformation in the environment, such as sunlight induced photochemical reactions. Despite molecular level resolution from ultrahigh resolution mass spectrometry (UHRMS), trends of mass peak intensities are currently the only way to follow photochemically induced molecular changes in DOM. Many real-world relationships and temporal processes can be intuitively modeled using graph data structures (networks). Graphs enhance the potential and value of AI applications by adding context and interconnections allowing the uncovering of hidden or unknown relationships in data sets. We use a temporal graph model and link prediction to identify transformations of DOM molecules in a photo-oxidation experiment. Our link prediction algorithm simultaneously considers educt removal and product formation for molecules linked by predefined transformation units (oxidation, decarboxylation, etc.). The transformations are further weighted by the extent of intensity change and clustered on the graph structure to identify groups of similar reactivity. The temporal graph is capable of identifying relevant molecules subject to similar reactions and enabling to study their time course. Our approach overcomes previous data evaluation limitations for mechanistic studies of DOM and leverages the potential of temporal graphs to study DOM reactivity by UHRMS.

Evolution of biomedical ontologies and mappings: Overview of recent approaches.

Biomedical ontologies are heavily used to annotate data, and different ontologies are often interlinked by ontology mappings. These ontology-based mappings and annotations are used in many applications and analysis tasks. Since biomedical ontologies are continuously updated dependent artifacts can become outdated and need to undergo evolution as well. Hence there is a need for largely automated approaches to keep ontology-based mappings up-to-date in the presence of evolving ontologies. In this article, we survey current approaches and novel directions in the context of ontology and mapping evolution. We will discuss requirements for mapping adaptation and provide a comprehensive overview on existing approaches. We will further identify open challenges and outline ideas for future developments.

Region Evolution eXplorer - A tool for discovering evolution trends in ontology regions.

BACKGROUND: A large number of life science ontologies has been developed to support different application scenarios such as gene annotation or functional analysis. The continuous accumulation of new insights and knowledge affects specific portions in ontologies and thus leads to their adaptation. Therefore, it is valuable to study which ontology parts have been extensively modified or remained unchanged. Users can monitor the evolution of an ontology to improve its further development or apply the knowledge in their applications. RESULTS: Here we present REX (Region Evolution eXplorer) a web-based system for exploring the evolution of ontology parts (regions). REX provides an analysis platform for currently about 1,000 versions of 16 well-known life science ontologies. Interactive workflows allow an explorative analysis of changing ontology regions and can be used to study evolution trends for long-term periods. CONCLUSION: REX is a web application providing an interactive and user-friendly interface to identify (un)stable regions in large life science ontologies. It is available at http://www.izbi.de/rex.

A multi-part matching strategy for mapping LOINC with laboratory terminologies.

OBJECTIVE: To address the problem of mapping local laboratory terminologies to Logical Observation Identifiers Names and Codes (LOINC). To study different ontology matching algorithms and investigate how the probability of term combinations in LOINC helps to increase match quality and reduce manual effort. MATERIALS AND METHODS: We proposed two matching strategies: full name and multi-part. The multi-part approach also considers the occurrence probability of combined concept parts. It can further recommend possible combinations of concept parts to allow more local terms to be mapped. Three real-world laboratory databases from Taiwanese hospitals were used to validate the proposed strategies with respect to different quality measures and execution run time. A comparison with the commonly used tool, Regenstrief LOINC Mapping Assistant (RELMA) Lab Auto Mapper (LAM), was also carried out. RESULTS: The new multi-part strategy yields the best match quality, with F-measure values between 89% and 96%. It can automatically match 70-85% of the laboratory terminologies to LOINC. The recommendation step can further propose mapping to (proposed) LOINC concepts for 9-20% of the local terminology concepts. On average, 91% of the local terminology concepts can be correctly mapped to existing or newly proposed LOINC concepts. CONCLUSIONS: The mapping quality of the multi-part strategy is significantly better than that of LAM. It enables domain experts to perform LOINC matching with little manual work. The probability of term combinations proved to be a valuable strategy for increasing the quality of match results, providing recommendations for proposed LOINC conepts, and decreasing the run time for match processing.

COnto-Diff: generation of complex evolution mappings for life science ontologies.

Life science ontologies evolve frequently to meet new requirements or to better reflect the current domain knowledge. The development and adaptation of large and complex ontologies is typically performed collaboratively by several curators. To effectively manage the evolution of ontologies it is essential to identify the difference (Diff) between ontology versions. Such a Diff supports the synchronization of changes in collaborative curation, the adaptation of dependent data such as annotations, and ontology version management. We propose a novel approach COnto-Diff to determine an expressive and invertible diff evolution mapping between given versions of an ontology. Our approach first matches the ontology versions and determines an initial evolution mapping consisting of basic change operations (insert/update/delete). To semantically enrich the evolution mapping we adopt a rule-based approach to transform the basic change operations into a smaller set of more complex change operations, such as merge, split, or changes of entire subgraphs. The proposed algorithm is customizable in different ways to meet the requirements of diverse ontologies and application scenarios. We evaluate the proposed approach for large life science ontologies including the Gene Ontology and the NCI Thesaurus and compare it with PromptDiff. We further show how the Diff results can be used for version management and annotation migration in collaborative curation.

Impact of ontology evolution on functional analyses.

MOTIVATION: Ontologies are used in the annotation and analysis of biological data. As knowledge accumulates, ontologies and annotation undergo constant modifications to reflect this new knowledge. These modifications may influence the results of statistical applications such as functional enrichment analyses that describe experimental data in terms of ontological groupings. Here, we investigate to what degree modifications of the Gene Ontology (GO) impact these statistical analyses for both experimental and simulated data. The analysis is based on new measures for the stability of result sets and considers different ontology and annotation changes. RESULTS: Our results show that past changes in the GO are non-uniformly distributed over different branches of the ontology. Considering the semantic relatedness of significant categories in analysis results allows a more realistic stability assessment for functional enrichment studies. We observe that the results of term-enrichment analyses tend to be surprisingly stable despite changes in ontology and annotation.

CODEX: exploration of semantic changes between ontology versions.

Life science ontologies substantially change over time to meet the requirements of their users and to include the newest domain knowledge. Thus, an important task is to know what has been modified between two versions of an ontology (diff). This diff should contain all performed changes as compact and understandable as possible. We present CODEX (Complex Ontology Diff Explorer), a tool that allows determining semantic changes between two versions of an ontology, which users can interactively analyze in multiple ways.

GOMMA: a component-based infrastructure for managing and analyzing life science ontologies and their evolution.

BACKGROUND: Ontologies are increasingly used to structure and semantically describe entities of domains, such as genes and proteins in life sciences. Their increasing size and the high frequency of updates resulting in a large set of ontology versions necessitates efficient management and analysis of this data. RESULTS: We present GOMMA, a generic infrastructure for managing and analyzing life science ontologies and their evolution. GOMMA utilizes a generic repository to uniformly and efficiently manage ontology versions and different kinds of mappings. Furthermore, it provides components for ontology matching, and determining evolutionary ontology changes. These components are used by analysis tools, such as the Ontology Evolution Explorer (OnEX) and the detection of unstable ontology regions. We introduce the component-based infrastructure and show analysis results for selected components and life science applications. GOMMA is available at http://dbs.uni-leipzig.de/GOMMA. CONCLUSIONS: GOMMA provides a comprehensive and scalable infrastructure to manage large life science ontologies and analyze their evolution. Key functions include a generic storage of ontology versions and mappings, support for ontology matching and determining ontology changes. The supported features for analyzing ontology changes are helpful to assess their impact on ontology-dependent applications such as for term enrichment. GOMMA complements OnEX by providing functionalities to manage various versions of mappings between two ontologies and allows combining different match approaches.

OnEX: Exploring changes in life science ontologies.

BACKGROUND: Numerous ontologies have recently been developed in life sciences to support a consistent annotation of biological objects, such as genes or proteins. These ontologies underlie continuous changes which can impact existing annotations. Therefore, it is valuable for users of ontologies to study the stability of ontologies and to see how many and what kind of ontology changes occurred. RESULTS: We present OnEX (Ontology Evolution EXplorer) a system for exploring ontology changes. Currently, OnEX provides access to about 560 versions of 16 well-known life science ontologies. The system is based on a three-tier architecture including an ontology version repository, a middleware component and the OnEX web application. Interactive workflows allow a systematic and explorative change analysis of ontologies and their concepts as well as the semi-automatic migration of out-dated annotations to the current version of an ontology. CONCLUSION: OnEX provides a user-friendly web interface to explore information about changes in current life science ontologies. It is available at http://www.izbi.de/onex.