Primer on Ontologies

Chebifier: automating semantic classification in ChEBI to accelerate data-driven discovery

Connecting chemical structural representations with meaningful categories and semantic annotations representing existing knowledge enables data-driven digital discovery from chemistry data. Ontologies are semantic annotation resources that provide definitions and a classification hierarchy for a domain. They are widely used throughout the life sciences. ChEBI is a large-scale ontology for the domain of biologically interesting chemistry that connects representations of chemical structures with meaningful chemical and biological categories. Classifying novel molecular structures into ontologies such as ChEBI has been a longstanding objective for data scientific methods, but the approaches that have been developed to date are limited in several ways: they are not able to expand as the ontology expands without manual intervention, and they are not able to learn from continuously expanding data. We have developed an approach for automated classification of chemicals in the ChEBI ontology based on a neuro-symbolic AI technique that harnesses the ontology itself to create the learning system. We provide this system as a publicly available tool, Chebifier, and as an API, ChEB-AI. We here evaluate our approach and show how it constitutes an advance towards a continuously learning semantic system for chemical knowledge discovery.

The Behaviour Change Technique Ontology: Transforming the Behaviour Change Technique Taxonomy v1

Background

The Behaviour Change Technique Taxonomy v1 (BCTTv1) specifies the potentially active content of behaviour change interventions. Evaluation of BCTTv1 showed the need to extend it into a formal ontology, improve its labels and definitions, add BCTs and subdivide existing BCTs. We aimed to develop a Behaviour Change Technique Ontology (BCTO) that would meet these needs.

Methods

The BCTO was developed by: (1) collating and synthesising feedback from multiple sources; (2) extracting information from published studies and classification systems; (3) multiple iterations of reviewing and refining entities, and their labels, definitions and relationships; (4) refining the ontology via expert stakeholder review of its comprehensiveness and clarity; (5) testing whether researchers could reliably apply the ontology to identify BCTs in intervention reports; and (6) making it available online and creating a computer-readable version.

Results

Initially there were 282 proposed changes to BCTTv1. Following first-round review, 19 BCTs were split into two or more BCTs, 27 new BCTs were added and 26 BCTs were moved into a different group, giving 161 BCTs hierarchically organised into 12 logically defined higher-level groups in up to five hierarchical levels. Following expert stakeholder review, the refined ontology had 247 BCTs hierarchically organised into 20 higher-level groups. Independent annotations of intervention evaluation reports by researchers familiar and unfamiliar with the ontology resulted in good levels of inter-rater reliability (0.82 and 0.79, respectively). Following revision informed by this exercise, 34 BCTs were added, resulting in the first published version of the BCTO containing 281 BCTs organised into 20 higher-level groups over five hierarchical levels.

Discussion

The BCTO provides a standard terminology and comprehensive classification system for the content of behaviour change interventions that can be reliably used to describe interventions. The development and maintenance of an ontology is an iterative and ongoing process; no ontology is ever 'finished'. The BCTO will continue to evolve and grow (e.g. new BCTs or improved definitions) as a result of user feedback and new available evidence.

OMA orthology in 2024: improved prokaryote coverage, ancestral and extant GO enrichment, a revamped synteny viewer and more in the OMA Ecosystem

In this update paper, we present the latest developments in the OMA browser knowledgebase, which aims to provide high-quality orthology inferences and facilitate the study of gene families, genomes and their evolution. First, we discuss the addition of new species in the database, particularly an expanded representation of prokaryotic species. The OMA browser now offers Ancestral Genome pages and an Ancestral Gene Order viewer, allowing users to explore the evolutionary history and gene content of ancestral genomes. We also introduce a revamped Local Synteny Viewer to compare genomic neighborhoods across both extant and ancestral genomes. Hierarchical Orthologous Groups (HOGs) are now annotated with Gene Ontology annotations, and users can easily perform extant or ancestral GO enrichments. Finally, we recap new tools in the OMA Ecosystem, including OMAmer for proteome mapping, OMArk for proteome quality assessment, OMAMO for model organism selection and Read2Tree for phylogenetic species tree construction from reads. These new features provide exciting opportunities for orthology analysis and comparative genomics. OMA is accessible at https://omabrowser.org.

Toward an ontology of identity-related constructs in addiction, with examples from nicotine and tobacco research

BACKGROUND AND AIMS

We aimed to create a basic set of definitions and relationships for identity-related constructs, as part of the Addiction Ontology and E-Cigarette Ontology projects, that could be used by researchers with diverse theoretical positions and so facilitate evidence synthesis and interoperability.

METHODS

We reviewed the use of identity-related constructs in psychological and social sciences and how these have been applied to addiction with a focus on nicotine and tobacco research. We, then, used an iterative process of adaptation and review to arrive at a basic set of identity-related classes with labels, definitions and relationships that could provide a common framework for research.

RESULTS

We propose that 'identity' be used to refer to 'a cognitive representation by a person or group of themselves', with 'self-identity' referring to an individual's identity and 'group identity' referring to an identity held by a social group. Identities can then be classified at any level of granularity based on the content of the representations (e.g. 'tobacco smoker identity', 'cigarette smoker identity' and 'vaper identity'). We propose distinguishing identity from 'self-appraisal' to capture the distinction between the representation of oneself (e.g. as an 'ex-smoker') and (i) the importance and (ii) the positive or negative evaluation that we attach to what is represented. We label an identity that is appraised as enduring as a 'core identity', related to 'strong identity' because of the appraisal as important. Identities that are appraised positively or negatively involve 'positive self-appraisal' and 'negative self-appraisal' respectively. This allows us to create 'logically defined classes' of identity by combining them (e.g. 'positive core cigarette smoker identity' to refer to a cigarette smoker self-identity that is both positive and important). We refer to the totality of self-identities of a person as a 'composite self-identity'.

CONCLUSIONS

An ontology of identity constructs may assist in improving clarity when discussing theories and evidence relating to this construct in addiction research.

Behaviour change techniques taxonomy v1: Feedback to inform the development of an ontology

Background: To build cumulative evidence about what works in behaviour change interventions, efforts have been made to develop classification systems for specifying the content of interventions. The Behaviour Change Techniques (BCT) Taxonomy v1 (BCTTv1) is one of the most widely used classifications of behaviour change techniques across a variety of behaviours. The BCTTv1 was intentionally named version 1 to allow for further revisions to the taxonomy. This study aimed to gather data to improve the BCTTv1 and provide recommendations for developing it into a more elaborated knowledge structure, an ontology.  Methods: Feedback from users of BCTTv1 about limitations and proposed improvements was collected through the BCT website, user survey, researchers and experts involved in the Human Behaviour-Change Project, and a consultation. In addition, relevant published research reports and other classification systems of BCTs were analysed. These data were synthesised to produce recommendations to inform the development of an ontology of BCTs. Results: A total of 282 comments from six sources were reviewed and synthesised into four categories of suggestions: additional BCTs, amendments to labels and definitions of specific BCTs, amendments to the groupings, and general improvements. Feedback suggested some lack of clarity regarding understanding and identifying techniques from labels, definitions, and examples; distinctions and relations between BCTs; and knowing what they would look like in practice. Three recommendations to improve the BCTTv1 resulted from this analysis: to review the label and definition of each BCT, the 16 groupings of BCTs, and the examples illustrating BCTs. Conclusions : This review of feedback about BCTTv1 identified the need to improve the precision and knowledge structure of the current taxonomy. A BCT ontology would enable the specification of relationships between BCTs, more precise definitions, and allow better interoperability with other ontologies. This ontology will be developed as part of the Human Behaviour-Change Project.

Behaviour change techniques taxonomy v1: Feedback to inform the development of an ontology

Background: To build cumulative evidence about what works in behaviour change interventions, efforts have been made to develop classification systems for specifying the content of interventions. The Behaviour Change Techniques (BCT) Taxonomy v1 (BCTTv1) is one of the most widely used classifications of behaviour change techniques across a variety of behaviours. The BCTTv1 was intentionally named version 1 to allow for further revisions to the taxonomy. This study aimed to gather data to improve the BCTTv1 and provide recommendations for developing it into a more elaborated knowledge structure, an ontology.  Methods: Feedback from users of BCTTv1 about limitations and proposed improvements was collected through the BCT website, user survey, researchers and experts involved in the Human Behaviour-Change Project, and a consultation. In addition, relevant published research reports and other classification systems of BCTs were analysed. These data were synthesised to produce recommendations to inform the development of an ontology of BCTs. Results: A total of 282 comments from six sources were reviewed and synthesised into four categories of suggestions: additional BCTs, amendments to labels and definitions of specific BCTs, amendments to the groupings, and general improvements. Feedback suggested some lack of clarity regarding understanding and identifying techniques from labels, definitions, and examples; distinctions and relations between BCTs; and knowing what they would look like in practice. Three recommendations to improve the BCTTv1 resulted from this analysis: to review the label and definition of each BCT, the 16 groupings of BCTs, and the examples illustrating BCTs. Conclusions : This review of feedback about BCTTv1 identified the need to improve the precision and knowledge structure of the current taxonomy. A BCT ontology would enable the specification of relationships between BCTs, more precise definitions, and allow better interoperability with other ontologies. This ontology will be developed as part of the Human Behaviour-Change Project.

A Framework for Supporting Well-being using the Character Computing Ontology - Anxiety and Sleep Quality during COVID-19

The COVID-19 pandemic is affecting human behavior, increasing the demand for the cooperation between psychologists and computer scientists to develop technology solutions that can help people in order to promote well-being and behavior change. According to the conceptual Character-Behavior-Situation (CBS) triad of Character Computing, behavior is driven by an individual’s character (trait and state markers) and the situation. In previous work, a computational ontology for Character Computing (CCOnto) has been introduced. The ontology can be extended with domain-specific knowledge for developing applications for inferring certain human behaviors to be leveraged for different purposes. In this paper, we present a framework for developing applications for dealing with changes in well-being during the COVID-19 pandemic. The framework can be used by psychology domain experts and application developers. The proposed model allows the input of heuristic rules as well as data-based rule extraction for inferring behavior. In this paper, we present how CCOnto is extended with components of physical and mental well-being and how the framework uses the extended domain ontologies in applications for evaluating sleep habits, anxiety, and depression predisposition during the COVID-19 pandemic based on user-input data.

The Gene Regulation Knowledge Commons: The action area of GREEKC

The COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC, CA15205, www.greekc.org) organized nine workshops in a four-year period, starting September 2016. The workshops brought together a wide range of experts from all over the world working on various parts of the knowledge cycle that is central to understanding gene regulatory mechanisms. The discussions between ontologists, curators, text miners, biologists, bioinformaticians, philosophers and computational scientists spawned a host of activities aimed to update and standardise existing knowledge management workflows, encourage new experimental approaches and thoroughly involve end-users in the process to design the Gene Regulation Knowledge Commons (GRKC). The GREEKC consortium describes its main achievements, contextualised in a state-of-the-art of current tools and resources that today represent the GRKC.

Transcriptome repository of North-Western Himalayan endangered medicinal herbs: a paramount approach illuminating molecular perspective of phytoactive molecules and secondary metabolism

Medicinal plants of the North-Western Himalayan region are known for their unprecedented biodiversity and valuable secondary metabolites that are unique to this dynamic geo-climatic region. From ancient times these medicinal herbs have been used traditionally for their therapeutic potentials. But from the last 2 decades increasing pharmaceutical demand, illegal and unorganized trade of these medicinal plants have accelerated the rate of over-exploitation in a non-scientific manner. In addition, climate change and anthropogenic activities also affected their natural habitat and driving most of these endemic plant species to critically endangered that foresee peril of mass extinction from this eco-region. Hence there is an urgent need for developing alternative sustainable approaches and policies to utilize this natural bioresource ensuring simultaneous conservation. Hither, arise the advent of sequencing-based transcriptomic studies significantly contributes to better understand the background of important metabolic pathways and related genes/enzymes of high-value medicinal herbs, in the absence of genomic information. The use of comparative transcriptomics in conjunction with biochemical techniques in North-Western Himalayan medicinal plants has resulted in significant advances in the identification of the molecular players involved in the production of secondary metabolic pathways over the last decade. This information could be used to further engineer metabolic pathways and breeding programs, ultimately leading to the development of in vitro systems dedicated to the production of pharmaceutically important secondary metabolites at the industrial level. Collectively, successful adoption of these approaches can certainly ensure the sustainable utilization of Himalayan bioresource by reducing the pressure on the wild population of these critically endangered medicinal herbs. This review provides novel insight as a transcriptome-based bioresource repository for the understanding of important secondary metabolic pathways genes/enzymes and metabolism of endangered high-value North-Western Himalayan medicinal herbs, so that researchers across the globe can effectively utilize this information for devising effective strategies for the production of pharmaceutically important compounds and their scale-up for sustainable usage and take a step forward in omics-based conservation genetics.

A Birds-Eye (Re)View of Acid-Suppression Drugs, COVID-19, and the Highly Variable Literature

This Perspective examines a recent surge of information regarding the potential benefits of acid-suppression drugs in the context of COVID-19, with a particular eye on the great variability (and, thus, confusion) that has arisen across the reported findings, at least as regards the popular antacid famotidine. The degree of inconsistency and discordance reflects contradictory conclusions from independent, clinical-based studies that took roughly similar approaches, in terms of both experimental design (retrospective, observational, cohort-based, etc.) and statistical analysis workflows (propensity-score matching and stratification into sub-cohorts, etc.). The contradictions and potential confusion have ramifications for clinicians faced with choosing therapeutically optimal courses of intervention: e.g., do any potential benefits of famotidine suggest its use in a particular COVID-19 case? (If so, what administration route, dosage regimen, duration, etc. are likely optimal?) As succinctly put this March in Freedberg et al. (2021), "…several retrospective studies show relationships between famotidine and outcomes in COVID-19 and several do not." Beyond the pressing issue of possible therapeutic indications, the conflicting data and conclusions related to famotidine must be resolved before its inclusion/integration in ontological and knowledge graph (KG)-based frameworks, which in turn are useful for drug discovery and repurposing. As a broader methodological issue, note that reconciling inconsistencies would bolster the validity of meta-analyses which draw upon the relevant data-sources. And, perhaps most broadly, developing a system for treating inconsistencies would stand to improve the qualities of both 1) real world evidence-based studies (retrospective), on the one hand, and 2) placebo-controlled, randomized multi-center clinical trials (prospective), on the other hand. In other words, a systematic approach to reconciling the two types of studies would inherently improve the quality and utility of each type of study individually.

Specifying who delivers behaviour change interventions: development of an Intervention Source Ontology

Background:  Identifying how behaviour change interventions are delivered, including by whom, is key to understanding intervention effectiveness. However, information about who delivers interventions is reported inconsistently in intervention evaluations, limiting communication and knowledge accumulation. This paper reports a method for consistent reporting: The Intervention Source Ontology. This forms one part of the Behaviour Change Intervention Ontology, which aims to cover all aspects of behaviour change interventions . Methods: The Intervention Source Ontology was developed following methods for ontology development and maintenance used in the Human Behaviour-Change Project, with seven key steps: 1) define the scope of the ontology, 2) identify key entities and develop their preliminary definitions by reviewing existing classification systems (top-down) and reviewing 100 behaviour change intervention reports (bottom-up), 3) refine the ontology by piloting the preliminary ontology on 100 reports, 4) stakeholder review by 34 behavioural science and public health experts, 5) inter-rater reliability testing of annotating intervention reports using the ontology, 6) specify ontological relationships between entities and 7) disseminate and maintain the Intervention Source Ontology. Results: The Intervention Source Ontology consists of 140 entities. Key areas of the ontology include Occupational Role of Source, Relatedness between Person Source and the Target Population, Sociodemographic attributes and Expertise. Inter-rater reliability was found to be 0.60 for those familiar with the ontology and 0.59 for those unfamiliar with it, levels of agreement considered 'acceptable'. Conclusions: Information about who delivers behaviour change interventions can be reliably specified using the Intervention Source Ontology. For human-delivered interventions, the ontology can be used to classify source characteristics in existing behaviour change reports and enable clearer specification of intervention sources in reporting.

Delivering Behaviour Change Interventions: Development of a Mode of Delivery Ontology

Background: Investigating and improving the effects of behaviour change interventions requires detailed and consistent specification of all aspects of interventions. An important feature of interventions is the way in which these are delivered, i.e. their mode of delivery. This paper describes an ontology for specifying the mode of delivery of interventions, which forms part of the Behaviour Change Intervention Ontology, currently being developed in the Wellcome Trust funded Human Behaviour-Change Project. Methods: The Mode of Delivery Ontology was developed in an iterative process of annotating behaviour change interventions evaluation reports, and consulting with expert stakeholders. It consisted of seven steps: 1) annotation of 110 intervention reports to develop a preliminary classification of modes of delivery; 2) open review from international experts (n=25); 3) second round of annotations with 55 reports to test inter-rater reliability and identify limitations; 4) second round of expert review feedback (n=16); 5) final round of testing of the refined ontology by two annotators familiar and two annotators unfamiliar with the ontology; 6) specification of ontological relationships between entities; and 7) transformation into a machine-readable format using the Web Ontology Language (OWL) and publishing online. Results: The resulting ontology is a four-level hierarchical structure comprising 65 unique modes of delivery, organised by 15 upper-level classes: Informational , Environmental change, Somatic, Somatic alteration, Individual-based/ Pair-based /Group-based, Uni-directional/Interactional, Synchronous/ Asynchronous, Push/ Pull, Gamification, Arts feature. Relationships between entities consist of is_a. Inter-rater reliability of the Mode of Delivery Ontology for annotating intervention evaluation reports was a=0.80 (very good) for those familiar with the ontology and a= 0.58 (acceptable) for those unfamiliar with it. Conclusion: The ontology can be used for both annotating and writing behaviour change intervention evaluation reports in a consistent and coherent manner, thereby improving evidence comparison, synthesis, replication, and implementation of effective interventions.

Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application

BACKGROUND

Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices.

PURPOSES

By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP).

METHODS

The project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists.

RESULTS

Intervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data.

CONCLUSIONS

AI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms.

Representation of behaviour change interventions and their evaluation: Development of the Upper Level of the Behaviour Change Intervention Ontology

Background: Behaviour change interventions (BCI), their contexts and evaluation methods are heterogeneous, making it difficult to synthesise evidence and make recommendations for real-world policy and practice. Ontologies provide a means for addressing this. They represent knowledge formally as entities and relationships using a common language able to cross disciplinary boundaries and topic domains. This paper reports the development of the upper level of the Behaviour Change Intervention Ontology (BCIO), which provides a systematic way to characterise BCIs, their contexts and their evaluations. Methods: Development took place in four steps. (1) Entities and relationships were identified by behavioural and social science experts, based on their knowledge of evidence and theory, and their practical experience of behaviour change interventions and evaluations. (2) The outputs of the first step were critically examined by a wider group of experts, including the study ontology expert and those experienced in annotating relevant literature using the initial ontology entities. The outputs of the second step were tested by (3) feedback from three external international experts in ontologies and (4) application of the prototype upper-level BCIO to annotating published reports; this informed the final development of the upper-level BCIO. Results: The final upper-level BCIO specifies 42 entities, including the BCI scenario, elaborated across 21 entities and 7 relationship types, and the BCI evaluation study comprising 10 entities and 9 relationship types. BCI scenario entities include the behaviour change intervention (content and delivery), outcome behaviour, mechanism of action, and its context, which includes population and setting. These entities have corresponding entities relating to the planning and reporting of interventions and their evaluations. Conclusions: The upper level of the BCIO provides a comprehensive and systematic framework for representing BCIs, their contexts and their evaluations.

Ontologies relevant to behaviour change interventions: a method for their development

Background: Behaviour and behaviour change are integral to many aspects of wellbeing and sustainability. However, reporting behaviour change interventions accurately and synthesising evidence about effective interventions is hindered by lacking a shared, scientific terminology to describe intervention characteristics. Ontologies are standardised frameworks that provide controlled vocabularies to help unify and connect scientific fields. To date, there is no published guidance on the specific methods required to develop ontologies relevant to behaviour change. We report the creation and refinement of a method for developing ontologies that make up the Behaviour Change Intervention Ontology (BCIO). Aims: (1) To describe the development method of the BCIO and explain its rationale; (2) To provide guidance on implementing the activities within the development method. Method and results: The method for developing ontologies relevant to behaviour change interventions was constructed by considering principles of good practice in ontology development and identifying key activities required to follow those principles. The method's details were refined through application to developing two ontologies. The resulting ontology development method involved: (1) defining the ontology's scope; (2) identifying key entities; (3) refining the ontology through an iterative process of literature annotation, discussion and revision; (4) expert stakeholder review; (5) testing inter-rater reliability; (6) specifying relationships between entities, and; (7) disseminating and maintaining the ontology. Guidance is provided for conducting relevant activities for each step.  Conclusions: We have developed a detailed method for creating ontologies relevant to behaviour change interventions, together with practical guidance for each step, reflecting principles of good practice in ontology development. The most novel aspects of the method are the use of formal mechanisms for literature annotation and expert stakeholder review to develop and improve the ontology content. We suggest the mnemonic SELAR3, representing the method's first six steps as Scope, Entities, Literature Annotation, Review, Reliability, Relationships.

TOA: A software package for automated functional annotation in non-model plant species

The increase of sequencing capacity provided by high-throughput platforms has made it possible to routinely obtain large sets of genomic and transcriptomic sequences from model and non-model organisms. Subsequent genomic analysis and gene discovery in next-generation sequencing experiments are, however, bottlenecked by functional annotation. One common way to perform functional annotation of sets of sequences obtained from next-generation sequencing experiments, is by searching for homologous sequences and accessing the related functional information deposited in genomic databases. Functional annotation is especially challenging for non-model organisms, like many plant species. In such cases, existing free and commercial general-purpose applications may not offer complete and accurate results. We present TOA (Taxonomy-oriented annotation), a Python-based user-friendly open source application designed to establish functional annotation pipelines geared towards non-model plant species that can run in Linux/Mac computers, HPCs and cloud servers. TOA performs homology searches against proteins stored in the PLAZA databases, NCBI RefSeq Plant, Nucleotide Database and Non-Redundant Protein Sequence Database, and outputs functional information from several ontology systems: Gene Ontology, InterPro, EC, KEGG, Mapman and MetaCyc. The software performance was validated by comparing the runtimes, total number of annotated sequences and accuracy of the functional information obtained for several plant benchmark data sets with TOA and other functional annotation solutions. TOA outperformed the other software in terms of number of annotated sequences and accuracy of the annotation and constitutes a good alternative to improve functional annotation in plants. TOA is especially recommended for gymnosperms or for low quality sequence data sets of non-model plants.

Using Drosophila melanogaster To Discover Human Disease Genes: An Educational Primer for Use with "Amyotrophic Lateral Sclerosis Modifiers in Drosophila Reveal the Phospholipase D Pathway as a Potential Therapeutic Target"

Since the dawn of the 20th century, the fruit fly Drosophila melanogaster has been used as a model organism to understand the nature of genes and how they control development, behavior, and physiology. One of the most powerful experimental approaches employed in Drosophila is the forward genetic screen. In the 21st century, genome-wide screens have become popular tools for identifying evolutionarily conserved genes involved in complex human diseases. In the accompanying article "Amyotrophic Lateral Sclerosis Modifiers in Drosophila Reveal thePhospholipase DPathway as a Potential Therapeutic Target," Kankel and colleagues describe a forward genetic modifier screen to discover factors that contribute to the severe neurodegenerative disease amyotrophic lateral sclerosis (ALS). This primer briefly traces the history of genetic screens in Drosophila and introduces students to ALS. We then provide a set of guided reading questions to help students work through the data presented in the research article. Finally, several ideas for literature-based research projects are offered as opportunities for students to expand their appreciation of the potential scope of genetic screens. The primer is intended to help students and instructors thoroughly examine a current study that uses forward genetics in Drosophila to identify human disease genes.

Ontologies relevant to behaviour change interventions: a method for their development

Background: Behaviour and behaviour change are integral to many aspects of wellbeing and sustainability. However, reporting behaviour change interventions accurately and synthesising evidence about effective interventions is hindered by lacking a shared, scientific terminology to describe intervention characteristics. Ontologies are knowledge structures that provide controlled vocabularies to help unify and connect scientific fields. To date, there is no published guidance on the specific methods required to develop ontologies relevant to behaviour change. We report the creation and refinement of a method for developing ontologies that make up the Behaviour Change Intervention Ontology (BCIO). Aims: (1) To describe the development method of the BCIO and explain its rationale; (2) To provide guidance on implementing the activities within the development method. Method and results: The method for developing ontologies relevant to behaviour change interventions was constructed by considering principles of good practice in ontology development and identifying key activities required to follow those principles. The method's details were refined through application to developing two ontologies. The resulting ontology development method involved: (1) defining the ontology's scope; (2) identifying key entities; (3) refining the ontology through an iterative process of literature annotation, discussion and revision; (4) expert stakeholder review; (5) testing inter-rater reliability; (6) specifying relationships between entities, and; (7) disseminating and maintaining the ontology. Guidance is provided for conducting relevant activities for each step.  Conclusions: We have developed a detailed method for creating ontologies relevant to behaviour change interventions, together with practical guidance for each step, reflecting principles of good practice in ontology development. The most novel aspects of the method are the use of formal mechanisms for literature annotation and expert stakeholder review to develop and improve the ontology content. We suggest the mnemonic SELAR3, representing the method's first six steps as Scope, Entities, Literature Annotation, Review, Reliability, Relationships.

Delivering Behaviour Change Interventions: Development of a Mode of Delivery Ontology

Background: Investigating and improving the effects of behaviour change interventions requires detailed and consistent specification of all aspects of interventions. An important feature of interventions is the way in which these are delivered, i.e. their mode of delivery. This paper describes an ontology for specifying the mode of delivery of interventions, which forms part of the Behaviour Change Intervention Ontology, currently being developed in the Wellcome Trust funded Human Behaviour-Change Project. Methods: The Mode of Delivery Ontology was developed in an iterative process of annotating behaviour change interventions evaluation reports, and consulting with expert stakeholders. It consisted of seven steps: 1) annotation of 110 intervention reports to develop a preliminary classification of modes of delivery; 2) open review from international experts (n=25); 3) second round of annotations with 55 reports to test inter-rater reliability and identify limitations; 4) second round of expert review feedback (n=16); 5) final round of testing of the refined ontology by two annotators familiar and two annotators unfamiliar with the ontology; 6) specification of ontological relationships between entities; and 7) transformation into a machine-readable format using the Web Ontology Language (OWL) language and publishing online. Results: The resulting ontology is a four-level hierarchical structure comprising 65 unique modes of delivery, organised by 15 upper-level classes: Informational , Environmental change, Somatic, Somatic alteration, Individual-based/ Pair-based /Group-based, Uni-directional/Interactional, Synchronous/ Asynchronous, Push/ Pull, Gamification, Arts feature. Relationships between entities consist of is_a. Inter-rater reliability of the Mode of Delivery Ontology for annotating intervention evaluation reports was a=0.80 (very good) for those familiar with the ontology and a= 0.58 (acceptable) for those unfamiliar with it. Conclusion: The ontology can be used for both annotating and writing behaviour change intervention evaluation reports in a consistent and coherent manner, thereby improving evidence comparison, synthesis, replication, and implementation of effective interventions.

Ontologies relevant to behaviour change interventions: a method for their development

Background: Behaviour and behaviour change are integral to many aspects of wellbeing and sustainability. However, reporting behaviour change interventions accurately and synthesising evidence about effective interventions is hindered by lacking a shared, scientific terminology to describe intervention characteristics. Ontologies are knowledge structures that provide controlled vocabularies to help unify and connect scientific fields. To date, there is no published guidance on the specific methods required to develop ontologies relevant to behaviour change. We report the creation and refinement of a method for developing ontologies that make up the Behaviour Change Intervention Ontology (BCIO). Aims: (1) To describe the development method of the BCIO and explain its rationale; (2) To provide guidance on implementing the activities within the development method. Method and results: The method for developing ontologies relevant to behaviour change interventions was constructed by considering principles of good practice in ontology development and identifying key activities required to follow those principles. The method's details were refined through application to developing two ontologies. The resulting ontology development method involved: (1) defining the ontology's scope; (2) identifying key entities; (3) refining the ontology through an iterative process of literature annotation, discussion and revision; (4) expert stakeholder review; (5) testing inter-rater reliability; (6) specifying relationships between entities, and; (7) disseminating and maintaining the ontology. Guidance is provided for conducting relevant activities for each step.  Conclusions: We have developed a detailed method for creating ontologies relevant to behaviour change interventions, together with practical guidance for each step, reflecting principles of good practice in ontology development. The most novel aspects of the method are the use of formal mechanisms for literature annotation and expert stakeholder review to develop and improve the ontology content. We suggest the mnemonic SELAR3, representing the method's first six steps as Scope, Entities, Literature Annotation, Review, Reliability, Relationships.

Representation of behaviour change interventions and their evaluation: Development of the Upper Level of the Behaviour Change Intervention Ontology

Background: Behaviour change interventions (BCI), their contexts and evaluation methods are heterogeneous, making it difficult to synthesise evidence and make recommendations for real-world policy and practice. Ontologies provide a means for addressing this. They represent knowledge formally as entities and relationships using a common language able to cross disciplinary boundaries and topic domains. This paper reports the development of the upper level of the Behaviour Change Intervention Ontology (BCIO), which provides a systematic way to characterise BCIs, their contexts and their evaluations. Methods: Development took place in four steps. (1) Entities and relationships were identified by behavioural and social science experts, based on their knowledge of evidence and theory, and their practical experience of behaviour change interventions and evaluations. (2) The outputs of the first step were critically examined by a wider group of experts, including the study ontology expert and those experienced in annotating relevant literature using the initial ontology entities. The outputs of the second step were tested by (3) feedback from three external international experts in ontologies and (4) application of the prototype upper-level BCIO to annotating published reports; this informed the final development of the upper-level BCIO. Results: The final upper-level BCIO specifies 42 entities, including the BCI scenario, elaborated across 21 entities and 7 relationship types, and the BCI evaluation study comprising 10 entities and 9 relationship types. BCI scenario entities include the behaviour change intervention (content and delivery), outcome behaviour, mechanism of action, and its context, which includes population and setting. These entities have corresponding entities relating to the planning and reporting of interventions and their evaluations. Conclusions: The upper level of the BCIO provides a comprehensive and systematic framework for representing BCIs, their contexts and their evaluations.

Development of an Intervention Setting Ontology for behaviour change: Specifying where interventions take place

Background: Contextual factors such as an intervention's setting are key to understanding how interventions to change behaviour have their effects and patterns of generalisation across contexts. The intervention's setting is not consistently reported in published reports of evaluations. Using ontologies to specify and classify intervention setting characteristics enables clear and reproducible reporting, thus aiding replication, implementation and evidence synthesis. This paper reports the development of a Setting Ontology for behaviour change interventions as part of a Behaviour Change Intervention Ontology, currently being developed in the Wellcome Trust funded Human Behaviour-Change Project. Methods: The Intervention Setting Ontology was developed following methods for ontology development used in the Human Behaviour-Change Project: 1) Defining the ontology's scope, 2) Identifying key entities by reviewing existing classification systems (top-down) and 100 published behaviour change intervention reports (bottom-up), 3) Refining the preliminary ontology by literature annotation of 100 reports, 4) Stakeholder reviewing by 23 behavioural science and public health experts to refine the ontology, 5) Assessing inter-rater reliability of using the ontology by two annotators familiar with the ontology and two annotators unfamiliar with it, 6) Specifying ontological relationships between setting entities and 7) Making the Intervention Setting Ontology machine-readable using Web Ontology Language (OWL) and publishing online. Re sults: The Intervention Setting Ontology consists of 72 entities structured hierarchically with two upper-level classes: Physical setting including Geographic location, Attribute of location (including Area social and economic condition, Population and resource density sub-levels) and Intervention site (including Facility, Transportation and Outdoor environment sub-levels), as well as Social setting. Inter-rater reliability was found to be 0.73 (good) for those familiar with the ontology and 0.61 (acceptable) for those unfamiliar with it. Conclusion: The Intervention Setting Ontology can be used to code information from diverse sources, annotate the setting characteristics of existing intervention evaluation reports and guide future reporting.

Development of a formal system for representing behaviour-change theories

Use of natural language to represent behaviour-change theories has resulted in lack of clarity and consistency, hindering comparison, integration, development and use. This paper describes development of a formal system for representing behaviour-change theories that aims to improve clarity and consistency. A given theory is represented in terms of (1) its component constructs (for example, 'self-efficacy', 'perceived threat' or 'subjective norm'), which are labelled and defined, and (2) relationships between pairs of constructs, which may be causal, structural or semantic. This formalism appears adequate to represent five commonly used theories (health belief model, information-motivation-behavioural skill model, social cognitive theory, theory of planned behaviour and the trans-theoretical model). Theory authors and experts judged that the system was able to capture the main propositions of the theories. Following this proof of concept, the next step is to assess how far the system can be applied to other theories of behaviour change.

Introducing high school students to the Gene Ontology classification system

We present a tutorial that introduces high school students to the Gene Ontology classification system which is widely used in genomics and systems biology studies to characterize large sets of genes based on functional and structural information. This classification system is a valuable and standardized method used to identify genes that act in similar processes and pathways and also provides insight into the overall architecture and distribution of genes and gene families associated with a particular tissue or disease. By means of this tutorial, students learn how the classification system works through analyzing a gene set using DAVID the Database for Annotation, Visualization and Integrated Discovery that incorporates the Gene Ontology system into its suite of analysis tools. This method of analyzing genes is used by our high school student interns to categorize gene expression data related to behavioral neuroscience. Students will get a feel for working with genes and gene sets, acquire vocabulary, obtain an understanding of how a database is structured and gain an awareness of the vast amount of information that is known about genes as well as the online analysis tools to manage this information that is nowadays available. Based on survey responses, students intellectually benefit from learning about the Gene Ontology System and using the DAVID tools, they are better prepared for future database use and they also find it enjoyable.

Structural biology meets data science: does anything change?

Data science has emerged from the proliferation of digital data, coupled with advances in algorithms, software and hardware (e.g., GPU computing). Innovations in structural biology have been driven by similar factors, spurring us to ask: can these two fields impact one another in deep and hitherto unforeseen ways? We posit that the answer is yes. New biological knowledge lies in the relationships between sequence, structure, function and disease, all of which play out on the stage of evolution, and data science enables us to elucidate these relationships at scale. Here, we consider the above question from the five key pillars of data science: acquisition, engineering, analytics, visualization and policy, with an emphasis on machine learning as the premier analytics approach.

From Affective Science to Psychiatric Disorder: Ontology as a Semantic Bridge

Advances in emotion and affective science have yet to translate routinely into psychiatric research and practice. This is unfortunate since emotion and affect are fundamental components of many psychiatric conditions. Rectifying this lack of interdisciplinary integration could thus be a potential avenue for improving psychiatric diagnosis and treatment. In this contribution, we propose and discuss an ontological framework for explicitly capturing the complex interrelations between affective entities and psychiatric disorders, in order to facilitate mapping and integration between affective science and psychiatric diagnostics. We build on and enhance the categorisation of emotion, affect and mood within the previously developed Emotion Ontology, and that of psychiatric disorders in the Mental Disease Ontology. This effort further draws on developments in formal ontology regarding the distinction between normal and abnormal in order to formalize the interconnections. This operational semantic framework is relevant for applications including clarifying psychiatric diagnostic categories, clinical information systems, and the integration and translation of research results across disciplines.

Get GO! Retrieving GO Data Using AmiGO, QuickGO, API, Files, and Tools

The Gene Ontology Consortium (GOC) produces a wealth of resources widely used throughout the scientific community. In this chapter, we discuss the different ways in which researchers can access the resources of the GOC. We here share details about the mechanics of obtaining GO annotations, both by manually browsing, querying, and downloading data from the GO website, as well as computationally accessing the resources from the command line, including the ability to restrict the data being retrieved to subsets with only certain attributes.

Gene-Category Analysis

Gene-category analysis is one important knowledge integration approach in biomedical sciences that combines knowledge bases such as Gene Ontology with lists of genes or their products, which are often the result of high-throughput experiments, gained from either wet-lab or synthetic experiments. In this chapter, we will motivate this class of analyses and describe an often used variant that is based on Fisher's exact test. We show that this approach has some problems in the context of Gene Ontology of which users should be aware. We then describe some more recent algorithms that try to address some of the shortcomings of the standard approach.

Community-Wide Evaluation of Computational Function Prediction

A biological experiment is the most reliable way of assigning function to a protein. However, in the era of high-throughput sequencing, scientists are unable to carry out experiments to determine the function of every single gene product. Therefore, to gain insights into the activity of these molecules and guide experiments, we must rely on computational means to functionally annotate the majority of sequence data. To understand how well these algorithms perform, we have established a challenge involving a broad scientific community in which we evaluate different annotation methods according to their ability to predict the associations between previously unannotated protein sequences and Gene Ontology terms. Here we discuss the rationale, benefits, and issues associated with evaluating computational methods in an ongoing community-wide challenge.

The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations

The Evidence and Conclusion Ontology (ECO) is a community resource for describing the various types of evidence that are generated during the course of a scientific study and which are typically used to support assertions made by researchers. ECO describes multiple evidence types, including evidence resulting from experimental (i.e., wet lab) techniques, evidence arising from computational methods, statements made by authors (whether or not supported by evidence), and inferences drawn by researchers curating the literature. In addition to summarizing the evidence that supports a particular assertion, ECO also offers a means to document whether a computer or a human performed the process of making the annotation. Incorporating ECO into an annotation system makes it possible to leverage the structure of the ontology such that associated data can be grouped hierarchically, users can select data associated with particular evidence types, and quality control pipelines can be optimized. Today, over 30 resources, including the Gene Ontology, use the Evidence and Conclusion Ontology to represent both evidence and how annotations are made.

Primer on the Gene Ontology

The Gene Ontology (GO) project is the largest resource for cataloguing gene function. The combination of solid conceptual underpinnings and a practical set of features have made the GO a widely adopted resource in the research community and an essential resource for data analysis. In this chapter, we provide a concise primer for all users of the GO. We briefly introduce the structure of the ontology and explain how to interpret annotations associated with the GO.

Gene Ontology: Pitfalls, Biases, and Remedies

The Gene Ontology (GO) is a formidable resource, but there are several considerations about it that are essential to understand the data and interpret it correctly. The GO is sufficiently simple that it can be used without deep understanding of its structure or how it is developed, which is both a strength and a weakness. In this chapter, we discuss some common misinterpretations of the ontology and the annotations. A better understanding of the pitfalls and the biases in the GO should help users make the most of this very rich resource. We also review some of the misconceptions and misleading assumptions commonly made about GO, including the effect of data incompleteness, the importance of annotation qualifiers, and the transitivity or lack thereof associated with different ontology relations. We also discuss several biases that can confound aggregate analyses such as gene enrichment analyses. For each of these pitfalls and biases, we suggest remedies and best practices.