Linking rare and common disease: mapping clinical disease-phenotypes to ontologies in therapeutic target validation

Linking common human diseases to their phenotypes; development of a resource for human phenomics

Background

In recent years a large volume of clinical genomics data has become available due to rapid advances in sequencing technologies. Efficient exploitation of this genomics data requires linkage to patient phenotype profiles. Current resources providing disease-phenotype associations are not comprehensive, and they often do not have broad coverage of the disease terminologies, particularly ICD-10, which is still the primary terminology used in clinical settings.

Methods

We developed two approaches to gather disease-phenotype associations. First, we used a text mining method that utilizes semantic relations in phenotype ontologies, and applies statistical methods to extract associations between diseases in ICD-10 and phenotype ontology classes from the literature. Second, we developed a semi-automatic way to collect ICD-10–phenotype associations from existing resources containing known relationships.

Results

We generated four datasets. Two of them are independent datasets linking diseases to their phenotypes based on text mining and semi-automatic strategies. The remaining two datasets are generated from these datasets and cover a subset of ICD-10 classes of common diseases contained in UK Biobank. We extensively validated our text mined and semi-automatically curated datasets by: comparing them against an expert-curated validation dataset containing disease–phenotype associations, measuring their similarity to disease–phenotype associations found in public databases, and assessing how well they could be used to recover gene–disease associations using phenotype similarity.

Conclusion

We find that our text mining method can produce phenotype annotations of diseases that are correct but often too general to have significant information content, or too specific to accurately reflect the typical manifestations of the sporadic disease. On the other hand, the datasets generated from integrating multiple knowledgebases are more complete (i.e., cover more of the required phenotype annotations for a given disease). We make all data freely available at 10.5281/zenodo.4726713.

Supplementary Information

The online version contains supplementary material available at (10.1186/s13326-021-00249-x).

Planarian Anatomy Ontology: a resource to connect data within and across experimental platforms

As the planarian research community expands, the need for an interoperable data organization framework for tool building has become increasingly apparent. Such software would streamline data annotation and enhance cross-platform and cross-species searchability. We created the Planarian Anatomy Ontology (PLANA), an extendable relational framework of defined Schmidtea mediterranea (Smed) anatomical terms used in the field. At publication, PLANA contains over 850 terms describing Smed anatomy from subcellular to system levels across all life cycle stages, in intact animals and regenerating body fragments. Terms from other anatomy ontologies were imported into PLANA to promote interoperability and comparative anatomy studies. To demonstrate the utility of PLANA as a tool for data curation, we created resources for planarian embryogenesis, including a staging series and molecular fate-mapping atlas, and the Planarian Anatomy Gene Expression database, which allows retrieval of a variety of published transcript/gene expression data associated with PLANA terms. As an open-source tool built using FAIR (findable, accessible, interoperable, reproducible) principles, our strategy for continued curation and versioning of PLANA also provides a platform for community-led growth and evolution of this resource.

Summary: Description of the construction of an anatomy ontology tool for planaria with examples of its potential use to curate and mine data across multiple experimental platforms.

DisMaNET: A network-based tool to cross map disease vocabularies

BACKGROUND AND OBJECTIVES

The growing integration of healthcare sources is improving our understanding of diseases. Cross-mapping resources such as UMLS play a very important role in this area, but their coverage is still incomplete. With the aim to facilitate the integration and interoperability of biological, clinical and literary sources in studies of diseases, we built DisMaNET, a system to cross-map terms from disease vocabularies by leveraging the power and interpretability of network analysis.

METHODS

First, we collected and normalized data from 8 disease vocabularies and mapping sources to generate our datasets. Next, we built DisMaNET by integrating the generated datasets into a Neo4j graph database. Then we exploited the query mechanisms of Neo4j to cross-map disease terms of different vocabularies with a relevance score metric and contrasted the results with some state-of-the-art solutions. Finally, we made our system publicly available for its exploitation and evaluation both through a graphical user interface and REST APIs.

RESULTS

DisMaNET contains almost half a million nodes and near nine hundred thousand edges, including hierarchical and mapping relationships. Its query capabilities enabled the detection of connections between disease vocabularies that are not present in major mapping sources such as UMLS and the Disease Ontology, even for rare diseases. Furthermore, DisMaNET was capable of obtaining more than 80% of the mappings with UMLS reported in MonDO and DisGeNET, and it was successfully exploited to resolve the missing mappings in the DISNET project.

CONCLUSIONS

DisMaNET is a powerful, intuitive and publicly available system to cross-map terms from different disease vocabularies. Our study proves that it is a competitive alternative to existing mapping systems, incorporating the potential of network analysis and the interpretability of the results through a visual interface as its main advantages. Expansion with new sources, versioning and the improvement of the search and scoring algorithms are envisioned as future lines of work.

Artificial intelligence in gastrointestinal diseases

Artificial intelligence (AI) applications are growing in medicine. It is important to understand the current state of the AI applications prior to utilizing in disease research and treatment. In this review, AI application in the diagnosis and treatment of gastrointestinal diseases are studied and summarized. In most cases, AI studies had large amounts of data, including images, to learn to distinguish disease characteristics according to a human’s perspectives. The detailed pros and cons of utilizing AI approaches should be investigated in advance to ensure the safe application of AI in medicine. Evidence suggests that the collaborative usage of AI in both diagnosis and treatment of diseases will increase the precision and effectiveness of medicine. Recent progress in genome technology such as genome editing provides a specific example where AI has revealed the diagnostic and therapeutic possibilities of RNA detection and targeting.

Pathway Maps of Orphan and Complex Diseases Using an Integrative Computational Approach

Orphan diseases (ODs) are progressive genetic disorders, which affect a small number of people. The principal fundamental aspects related to these diseases include insufficient knowledge of mechanisms involved in the physiopathology necessary to access correct diagnosis and to develop appropriate healthcare. Unlike ODs, complex diseases (CDs) have been widely studied due to their high incidence and prevalence allowing to understand the underlying mechanisms controlling their physiopathology. Few studies have focused on the relationship between ODs and CDs to identify potential shared pathways and related molecular mechanisms which would allow improving disease diagnosis, prognosis, and treatment. We have performed a computational approach to studying CDs and ODs relationships through (1) connecting diseases to genes based on genes-diseases associations from public databases, (2) connecting ODs and CDs through binary associations based on common associated genes, and (3) linking ODs and CDs to common enriched pathways. Among the most shared significant pathways between ODs and CDs, we found pathways in cancer, p53 signaling, mismatch repair, mTOR signaling, B cell receptor signaling, and apoptosis pathways. Our findings represent a reliable resource that will contribute to identify the relationships between drugs and disease-pathway networks, enabling to optimise patient diagnosis and disease treatment.

[Evaluation of a future scenario concerning the use of big data applications to improve the care of people with rare diseases]

INTRODUCTION

In Germany there are about 4 million people living with a rare disease. Studies have shown that big data applications can improve diagnosis of and research on rare diseases more effectively. However, no concrete comprehensive concept for the use of big data in the care of people with rare diseases has so far been established in Germany. As part of the project "BIDA-SE", which is funded by the German Ministry of Health, a first scenario has been designed to show how big data applications can be usefully incorporated into the care of people with rare diseases.

METHODS

The aim of the present study was to evaluate this scenario with regard to acceptance, (clinical) benefits, economic aspects, and limitations and barriers to its implementation. To evaluate the scenario, an online survey was conducted in Germany in October/November 2019 amongst a total of N = 9 physicians, N = 69 patients with rare diseases/patient representatives, N = 14 IT experts and N = 21 health care researchers. The online questionnaire consisted of both standardized, validated questions taken from already tested survey instruments and additional questions which were constructed on the basis of a preceding literature analysis. The evaluation of the survey was primarily descriptive, with a calculation of frequencies, mean values and standard deviations.

RESULTS

The results of the evaluation show that the scenario has been accepted by a majority of all groups surveyed (physicians, patients/patient representatives, IT experts and health care researchers). From the point of view of physicians, patients/patient representatives and health care researchers, the scenario has the potential to accelerate the diagnosis and initiation of therapy and to improve cross-sectoral treatment. From the physician's and health care researcher's perspective, investments in the application presented in the scenario would be profitable. Financing the scenario would, however, require adjusting the reimbursement situation. The limitations and barriers identified by all groups for a medium-term implementation of the scenario can be grouped into seven thematic areas where action is needed: (1) financing and investment, (2) data protection and data security, (3) standards/data sources/data quality, (4) acceptance of technology, (5) integration into the daily work routine, (6) knowledge about availability as well as (7) habits and preferences/physician's role.

DISCUSSION

With the present study, a first interdisciplinary, practical scenario using big data applications was evaluated with regard to acceptance, benefits and limitations/barriers. The scenario is widely accepted among the majority of all surveyed target groups and is considered (clinically) useful, although legal, organisational and technical barriers still need to be overcome for its medium-term implementation. The evaluation results contribute to the derivation of recommendations for action to ensure the medium-term implementation of the scenario and to channel access to the Centres for Rare Diseases in the future.

CONCLUSION

Many activities have been initiated at a national level to improve the health care situation of people with rare diseases. The scenario developed in the "BIDA-SE" project complements these research activities and illustrates how big data applications can be usefully implemented into practice to improve the diagnosis and therapy of people with rare diseases in a sustainable way.

Biomedical Holistic Ontology for People with Rare Diseases

This research provides a biomedical ontology to adequately represent the information necessary to manage a person with a disease in the context of a specific patient. A bottom-up approach was used to build the ontology, best ontology practices described in the literature were followed and the minimum information to reference an external ontology term (MIREOT) methodology was used to add external terms of other ontologies when possible. Public data of rare diseases from rare associations were used to build the ontology. In addition, sentiment analysis was performed in the standardized data using the Python library Textblob. A new holistic ontology was built, which models 25 real scenarios of people with rare diseases. We conclude that a comprehensive profile of patients is needed in biomedical ontologies. The generated code is openly available, so this research is partially reproducible. Depending on the knowledge needed, several views of the ontology should be generated. Links to other ontologies should be used more often to model the knowledge more precisely and improve flexibility. The proposed holistic ontology has many benefits, such as a more standardized computation of sentiment analysis between attributes.

BioHackathon 2015: Semantics of data for life sciences and reproducible research

We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.

Annotating and detecting phenotypic information for chronic obstructive pulmonary disease

Objectives

Chronic obstructive pulmonary disease (COPD) phenotypes cover a range of lung abnormalities. To allow text mining methods to identify pertinent and potentially complex information about these phenotypes from textual data, we have developed a novel annotated corpus, which we use to train a neural network-based named entity recognizer to detect fine-grained COPD phenotypic information.

Materials and methods

Since COPD phenotype descriptions often mention other concepts within them (proteins, treatments, etc.), our corpus annotations include both outermost phenotype descriptions and concepts nested within them. Our neural layered bidirectional long short-term memory conditional random field (BiLSTM-CRF) network firstly recognizes nested mentions, which are fed into subsequent BiLSTM-CRF layers, to help to recognize enclosing phenotype mentions.

Results

Our corpus of 30 full papers (available at: http://www.nactem.ac.uk/COPD) is annotated by experts with 27 030 phenotype-related concept mentions, most of which are automatically linked to UMLS Metathesaurus concepts. When trained using the corpus, our BiLSTM-CRF network outperforms other popular approaches in recognizing detailed phenotypic information.

Discussion

Information extracted by our method can facilitate efficient location and exploration of detailed information about phenotypes, for example, those specifically concerning reactions to treatments.

Conclusion

The importance of our corpus for developing methods to extract fine-grained information about COPD phenotypes is demonstrated through its successful use to train a layered BiLSTM-CRF network to extract phenotypic information at various levels of granularity. The minimal human intervention needed for training should permit ready adaption to extracting phenotypic information about other diseases.

Exploring semantic deep learning for building reliable and reusable one health knowledge from PubMed systematic reviews and veterinary clinical notes

Background

Deep Learning opens up opportunities for routinely scanning large bodies of biomedical literature and clinical narratives to represent the meaning of biomedical and clinical terms. However, the validation and integration of this knowledge on a scale requires cross checking with ground truths (i.e. evidence-based resources) that are unavailable in an actionable or computable form. In this paper we explore how to turn information about diagnoses, prognoses, therapies and other clinical concepts into computable knowledge using free-text data about human and animal health. We used a Semantic Deep Learning approach that combines the Semantic Web technologies and Deep Learning to acquire and validate knowledge about 11 well-known medical conditions mined from two sets of unstructured free-text data: 300 K PubMed Systematic Review articles (the PMSB dataset) and 2.5 M veterinary clinical notes (the VetCN dataset). For each target condition we obtained 20 related clinical concepts using two deep learning methods applied separately on the two datasets, resulting in 880 term pairs (target term, candidate term). Each concept, represented by an n-gram, is mapped to UMLS using MetaMap; we also developed a bespoke method for mapping short forms (e.g. abbreviations and acronyms). Existing ontologies were used to formally represent associations. We also create ontological modules and illustrate how the extracted knowledge can be queried. The evaluation was performed using the content within BMJ Best Practice.

Results

MetaMap achieves an F measure of 88% (precision 85%, recall 91%) when applied directly to the total of 613 unique candidate terms for the 880 term pairs. When the processing of short forms is included, MetaMap achieves an F measure of 94% (precision 92%, recall 96%). Validation of the term pairs with BMJ Best Practice yields precision between 98 and 99%.

Conclusions

The Semantic Deep Learning approach can transform neural embeddings built from unstructured free-text data into reliable and reusable One Health knowledge using ontologies and content from BMJ Best Practice.

Ontology mapping for semantically enabled applications

In this review, we provide a summary of recent progress in ontology mapping (OM) at a crucial time when biomedical research is under a deluge of an increasing amount and variety of data. This is particularly important for realising the full potential of semantically enabled or enriched applications and for meaningful insights, such as drug discovery, using machine-learning technologies. We discuss challenges and solutions for better ontology mappings, as well as how to select ontologies before their application. In addition, we describe tools and algorithms for ontology mapping, including evaluation of tool capability and quality of mappings. Finally, we outline the requirements for an ontology mapping service (OMS) and the progress being made towards implementation of such sustainable services.

The anatomy of phenotype ontologies: principles, properties and applications

The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of dealing with the related phenotype data, which is not only large but also highly multidimensional. Computational analysis of phenotypes has therefore become critical for our ability to understand the biological meaning of genomic data in the biological sciences. At the heart of computational phenotype analysis are the phenotype ontologies. A large number of these ontologies have been developed across many domains, and we are now at a point where the knowledge captured in the structure of these ontologies can be used for the integration and analysis of large interrelated data sets. The Phenotype And Trait Ontology framework provides a method for formal definitions of phenotypes and associated data sets and has proved to be key to our ability to develop methods for the integration and analysis of phenotype data. Here, we describe the development and products of the ontological approach to phenotype capture, the formal content of phenotype ontologies and how their content can be used computationally.

Disease Specific Ontology of Adverse Events: Ontology extension and adaptation for Chronic Kidney Disease

BACKGROUND

Adverse Event (AE) ontology can be used to support interoperability and computer-assisted reasoning of AEs. Despite significant progress in developing biomedical ontologies, they are facing the obstacle of adoption partly because those ontologies are too general to meet the requirements of a specific domain. Understanding and representing of AEs for a specific domain such as Chronic Kidney Disease (CKD) has both theoretical and clinical significance. CKD patients are at a high risk for an array of disease-intervention specific AEs, and these in turn can contribute to disease progression unlike other diseases. This study proposes Disease Specific Ontology of Adverse Events (DSOAE) to address specific requirements of CKD, and applies it to different usage scenarios with real data.

METHODS

We introduce a method for developing DSOAE through the extension and adaption of general ontologies by incorporating domain-specific information and usage requirements. It starts with specifying the goal and scope of a target domain (i.e. selecting seed ontologies), followed by identifying main AE classes and relations, extracting and creating classes and relations, aligning and identifying upper-level classes and lower-level classes, and finally populating the ontology with instances. Any of these steps may be repeated to refine the ontology.

RESULTS

DSOAE contains 22 CKD-specific AE classes, which are grouped into two general categories: patient-reported AEs and biochemical/laboratory-related AEs. In addition, disease history and comorbidity classes as introduced in this study help model patient-related risk factors for AEs. With the support of DSOAE, we build a knowledge base of CKD-specific AEs using data from different sources (e.g. patient cohort data and social media), and apply the knowledge base to data analysis and data integration.

CONCLUSIONS

DSOAE enables the interoperability of AEs across different sources and supports the development of a knowledge base of domain-specific AEs. DSOAE can also meet the needs of different usage scenarios. The approach to constructing DSOAE is generalizable and can be used to develop AE ontology in other domains.

A Census of Disease Ontologies

For centuries, humans have sought to classify diseases based on phenotypic presentation and available treatments. Today, a wide landscape of strategies, resources, and tools exist to classify patients and diseases. Ontologies can provide a robust foundation of logic for precise stratification and classification along diverse axes such as etiology, development, treatment, and genetics. Disease and phenotype ontologies are used in four primary ways: ( a) search, retrieval, and annotation of knowledge; ( b) data integration and analysis; ( c) clinical decision support; and ( d) knowledge discovery. Computational inference can connect existing knowledge and generate new insights and hypotheses about drug targets, prognosis prediction, or diagnosis. In this review, we examine the rise of disease and phenotype ontologies and the diverse ways they are represented and applied in biomedicine.

Cells in experimental life sciences - challenges and solution to the rapid evolution of knowledge

Cell cultures used in biomedical experiments come in the form of both sample biopsy primary cells, and maintainable immortalised cell lineages. The rise of bioinformatics and high-throughput technologies has led us to the requirement of ontology representation of cell types and cell lines. The Cell Ontology (CL) and Cell Line Ontology (CLO) have long been established as reference ontologies in the OBO framework. We have compiled a series of the challenges and the proposals of solutions in this CELLS (Cells in ExperimentaL Life Sciences) thematic series that cover the grounds of standing issues and the directions, which were discussed in the First International Workshop on CELLS at the the International Conference on Biomedical Ontology (ICBO). This workshop focused on the extension of the current CL and CLO to cover a wider set of biological questions and challenges needing semantic infrastructure for information modeling. We discussed data-driven use cases that leverage linkage of CL, CLO and other bio-ontologies. This is an established approach in data-driven ontologies such as the Experimental Factor Ontology (EFO), and the Ontology for Biomedical Investigation (OBI). The First International Workshop on CELLS at the International Conference on Biomedical Ontology has brought together experimental biologists and biomedical ontologists to discuss solutions to organizing and representing the rapidly evolving knowledge gained from experimental cells. The workshop has successfully identified the areas of challenge, and the gap in connecting the two domains of knowledge. The outcome of this workshop yielded practical implementation plans to filled in this gap.This CELLS workshop also provided a venue for panel discussions of innovative solutions as well as challenges in the development and applications of biomedical ontologies to represent and analyze experimental cell data.

Integrating phenotype ontologies with PhenomeNET

BACKGROUND

Integration and analysis of phenotype data from humans and model organisms is a key challenge in building our understanding of normal biology and pathophysiology. However, the range of phenotypes and anatomical details being captured in clinical and model organism databases presents complex problems when attempting to match classes across species and across phenotypes as diverse as behaviour and neoplasia. We have previously developed PhenomeNET, a system for disease gene prioritization that includes as one of its components an ontology designed to integrate phenotype ontologies. While not applicable to matching arbitrary ontologies, PhenomeNET can be used to identify related phenotypes in different species, including human, mouse, zebrafish, nematode worm, fruit fly, and yeast.

RESULTS

Here, we apply the PhenomeNET to identify related classes from two phenotype and two disease ontologies using automated reasoning. We demonstrate that we can identify a large number of mappings, some of which require automated reasoning and cannot easily be identified through lexical approaches alone. Combining automated reasoning with lexical matching further improves results in aligning ontologies.

CONCLUSIONS

PhenomeNET can be used to align and integrate phenotype ontologies. The results can be utilized for biomedical analyses in which phenomena observed in model organisms are used to identify causative genes and mutations underlying human disease.

Knowledge Representation and Management, It's Time to Integrate!

Objectives: To select, present, and summarize the best papers published in 2016 in the field of Knowledge Representation and Management (KRM). Methods: A comprehensive and standardized review of the medical informatics literature was performed based on a PubMed query. Results: Among the 1,421 retrieved papers, the review process resulted in the selection of four best papers focused on the integration of heterogeneous data via the development and the alignment of terminological resources. In the first article, the authors provide a curated and standardized version of the publicly available US FDA Adverse Event Reporting System. Such a resource will improve the quality of the underlying data, and enable standardized analyses using common vocabularies. The second article describes a project developed in order to facilitate heterogeneous data integration in the i2b2 framework. The originality is to allow users integrate the data described in different terminologies and to build a new repository, with a unique model able to support the representation of the various data. The third paper is dedicated to model the association between multiple phenotypic traits described within the Human Phenotype Ontology (HPO) and the corresponding genotype in the specific context of rare diseases (rare variants). Finally, the fourth paper presents solutions to annotation-ontology mapping in genome-scale data. Of particular interest in this work is the Experimental Factor Ontology (EFO) and its generic association model, the Ontology of Biomedical AssociatioN (OBAN). Conclusion: Ontologies have started to show their efficiency to integrate medical data for various tasks in medical informatics: electronic health records data management, clinical research, and knowledge-based systems development.

Computational drug repositioning for rare diseases in the era of precision medicine

There are tremendous unmet needs in drug development for rare diseases. Computational drug repositioning is a promising approach and has been successfully applied to the development of treatments for diseases. However, how to utilize this knowledge and effectively conduct and implement computational drug repositioning approaches for rare disease therapies is still an open issue. Here, we focus on the means of utilizing accumulated genomic data for accelerating and facilitating drug repositioning for rare diseases. First, we summarize the current genome landscape of rare diseases. Second, we propose several promising bioinformatics approaches and pipelines for computational drug repositioning for rare diseases. Finally, we discuss recent regulatory incentives and other enablers in rare disease drug development and outline the remaining challenges.

Use of Biomedical Ontologies for Integration of Biological Knowledge for Learning and Prediction of Adverse Drug Reactions

Drug-induced toxicity is a major public health concern that leads to patient morbidity and mortality. To address this problem, the Food and Drug Administration is working on the PredicTox initiative, a pilot research program on tyrosine kinase inhibitors, to build mechanistic and predictive models for drug-induced toxicity. This program involves integrating data acquired during preclinical studies and clinical trials within pharmaceutical company development programs that they have agreed to put in the public domain and in publicly available biological, pharmacological, and chemical databases. The integration process is accommodated by biomedical ontologies, a set of standardized vocabularies that define terms and logical relationships between them in each vocabulary. We describe a few programs that have used ontologies to address biomedical questions. The PredicTox effort is leveraging the experience gathered from these early initiatives to develop an infrastructure that allows evaluation of the hypothesis that having a mechanistic understanding underlying adverse drug reactions will improve the capacity to understand drug-induced clinical adverse drug reactions.

Rapid establishment of the European Bank for induced Pluripotent Stem Cells (EBiSC) - the Hot Start experience

A fast track "Hot Start" process was implemented to launch the European Bank for Induced Pluripotent Stem Cells (EBiSC) to provide early release of a range of established control and disease linked human induced pluripotent stem cell (hiPSC) lines. Established practice amongst consortium members was surveyed to arrive at harmonised and publically accessible Standard Operations Procedures (SOPs) for tissue procurement, bio-sample tracking, iPSC expansion, cryopreservation, qualification and distribution to the research community. These were implemented to create a quality managed foundational collection of lines and associated data made available for distribution. Here we report on the successful outcome of this experience and work flow for banking and facilitating access to an otherwise disparate European resource, with lessons to benefit the international research community. ETOC: The report focuses on the EBiSC experience of rapidly establishing an operational capacity to procure, bank and distribute a foundational collection of established hiPSC lines. It validates the feasibility and defines the challenges of harnessing and integrating the capability and productivity of centres across Europe using commonly available resources currently in the field.

Thematic issue of the Second combined Bio-ontologies and Phenotypes Workshop

This special issue covers selected papers from the 18th Bio-Ontologies Special Interest Group meeting and Phenotype Day, which took place at the Intelligent Systems for Molecular Biology (ISMB) conference in Dublin in 2015. The papers presented in this collection range from descriptions of software tools supporting ontology development and annotation of objects with ontology terms, to applications of text mining for structured relation extraction involving diseases and phenotypes, to detailed proposals for new ontologies and mapping of existing ontologies. Together, the papers consider a range of representational issues in bio-ontology development, and demonstrate the applicability of bio-ontologies to support biological and clinical knowledge-based decision making and analysis.

The full set of papers in the Thematic Issue is available at http://www.biomedcentral.com/collections/sig.

Open Targets: a platform for therapeutic target identification and validation

We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.

Reporting phenotypes in mouse models when considering body size as a potential confounder

Genotype-phenotype studies aim to identify causative relationships between genes and phenotypes. The International Mouse Phenotyping Consortium is a high throughput phenotyping program whose goal is to collect phenotype data for a knockout mouse strain of every protein coding gene. The scale of the project requires an automatic analysis pipeline to detect abnormal phenotypes, and disseminate the resulting gene-phenotype annotation data into public resources. A body weight phenotype is a common result of knockout studies. As body weight correlates with many other biological traits, this challenges the interpretation of related gene-phenotype associations. Co-correlation can lead to gene-phenotype associations that are potentially misleading. Here we use statistical modelling to account for body weight as a potential confounder to assess the impact. We find that there is a considerable impact on previously established gene-phenotype associations due to an increase in sensitivity as well as the confounding effect. We investigated the existing ontologies to represent this phenotypic information and we explored ways to ontologically represent the results of the influence of confounders on gene-phenotype associations. With the scale of data being disseminated within the high throughput programs and the range of downstream studies that utilise these data, it is critical to consider how we improve the quality of the disseminated data and provide a robust ontological representation.