Capturing heart valve development with Gene Ontology.

Introduction: The normal development of all heart valves requires highly coordinated signaling pathways and downstream mediators. While genomic variants can be responsible for congenital valve disease, environmental factors can also play a role. Later in life valve calcification is a leading cause of aortic valve stenosis, a progressive disease that may lead to heart failure. Current research into the causes of both congenital valve diseases and valve calcification is using a variety of high-throughput methodologies, including transcriptomics, proteomics and genomics. High quality genetic data from biological knowledge bases are essential to facilitate analyses and interpretation of these high-throughput datasets. The Gene Ontology (GO, http://geneontology.org/) is a major bioinformatics resource used to interpret these datasets, as it provides structured, computable knowledge describing the role of gene products across all organisms. The UCL Functional Gene Annotation team focuses on GO annotation of human gene products. Having identified that the GO annotations included in transcriptomic, proteomic and genomic data did not provide sufficient descriptive information about heart valve development, we initiated a focused project to address this issue. Methods: This project prioritized 138 proteins for GO annotation, which led to the curation of 100 peer-reviewed articles and the creation of 400 heart valve development-relevant GO annotations. Results: While the focus of this project was heart valve development, around 600 of the 1000 annotations created described the broader cellular role of these proteins, including those describing aortic valve morphogenesis, BMP signaling and endocardial cushion development. Our functional enrichment analysis of the 28 proteins known to have a role in bicuspid aortic valve disease confirmed that this annotation project has led to an improved interpretation of a heart valve genetic dataset. Discussion: To address the needs of the heart valve research community this project has provided GO annotations to describe the specific roles of key proteins involved in heart valve development. The breadth of GO annotations created by this project will benefit many of those seeking to interpret a wide range of cardiovascular genomic, transcriptomic, proteomic and metabolomic datasets.

Insights into Online microRNA Bioinformatics Tools.

MicroRNAs (miRNAs) are members of the small non-coding RNA family regulating gene expression at the post-transcriptional level. MiRNAs have been found to have critical roles in various biological and pathological processes. Research in this field has significantly progressed, with increased recognition of the importance of miRNA regulation. As a result of the vast data and information available regarding miRNAs, numerous online tools have emerged to address various biological questions related to their function and influence across essential cellular processes. This review includes a brief introduction to available resources for an investigation covering aspects such as miRNA sequences, target prediction/validation, miRNAs associated with disease, pathway analysis and genetic variants within miRNAs.

Tissue specific LRRK2 interactomes reveal a distinct striatal functional unit.

Mutations in LRRK2 are the most common genetic cause of Parkinson's disease. Despite substantial research efforts, the physiological and pathological role of this multidomain protein remains poorly defined. In this study, we used a systematic approach to construct the general protein-protein interactome around LRRK2, which was then evaluated taking into consideration the differential expression patterns and the co-expression behaviours of the LRRK2 interactors in 15 different healthy tissue types. The LRRK2 interactors exhibited distinct expression features in the brain as compared to the peripheral tissues analysed. Moreover, a high degree of similarity was found for the LRRK2 interactors in putamen, caudate and nucleus accumbens, thus defining a potential LRRK2 functional cluster within the striatum. The general LRRK2 interactome paired with the expression profiles of its members constitutes a powerful tool to generate tissue-specific LRRK2 interactomes. We exemplified the generation of the tissue-specific LRRK2 interactomes and explored the functions highlighted by the "core LRRK2 interactors" in the striatum in comparison with the cerebellum. Finally, we illustrated how the LRRK2 general interactome reported in this manuscript paired with the expression profiles can be used to trace the relationship between LRRK2 and specific interactors of interest, here focusing on the LRRK2 interactors belonging to the Rab protein family.

GOing Forward With the Cardiac Conduction System Using Gene Ontology.

The cardiac conduction system (CCS) comprises critical components responsible for the initiation, propagation, and coordination of the action potential. Aberrant CCS development can cause conduction abnormalities, including sick sinus syndrome, accessory pathways, and atrioventricular and bundle branch blocks. Gene Ontology (GO; http://geneontology.org/) is an invaluable global bioinformatics resource which provides structured, computable knowledge describing the functions of gene products. Many gene products are known to be involved in CCS development; however, this information is not comprehensively captured by GO. To address the needs of the heart development research community, this study aimed to describe the specific roles of proteins reported in the literature to be involved with CCS development and/or function. 14 proteins were prioritized for GO annotation which led to the curation of 15 peer-reviewed primary experimental articles using carefully selected GO terms. 152 descriptive GO annotations, including those describing sinoatrial node and atrioventricular node development were created and submitted to the GO Consortium database. A functional enrichment analysis of 35 key CCS development proteins confirmed that this work has improved the in-silico interpretation of this CCS dataset. This work may improve future investigations of the CCS with application of high-throughput methods such as genome-wide association studies analysis, proteomics, and transcriptomics.

Plasma proteins, cognitive decline, and 20-year risk of dementia in the Whitehall II and Atherosclerosis Risk in Communities studies.

INTRODUCTION: Plasma proteins affect biological processes and are common drug targets but their role in the development of Alzheimer's disease and related dementias remains unclear. We examined associations between 4953 plasma proteins and cognitive decline and risk of dementia in two cohort studies with 20-year follow-ups. METHODS: In the Whitehall II prospective cohort study proteins were measured using SOMAscan technology. Cognitive performance was tested five times over 20 years. Linkage to electronic health records identified incident dementia. The results were replicated in the Atherosclerosis Risk in Communities (ARIC) study. RESULTS: Fifteen non-amyloid/non-tau-related proteins were associated with cognitive decline and dementia, were consistently identified in both cohorts, and were not explained by known dementia risk factors. Levels of six of the proteins are modifiable by currently approved medications for other conditions. DISCUSSION: This study identified several plasma proteins in dementia-free people that are associated with long-term risk of cognitive decline and dementia.

The gene regulation knowledge commons: the action area of GREEKC.

As computational modeling becomes more essential to analyze and understand biological regulatory mechanisms, governance of the many databases and knowledge bases that support this domain is crucial to guarantee reliability and interoperability of resources. To address this, 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 steps in the knowledge management process that focuses on understanding gene regulatory mechanisms. The discussions between ontologists, curators, text miners, biologists, bioinformaticians, philosophers and computational scientists spawned a host of activities aimed to standardize and update existing knowledge management workflows and involve end-users in the process of designing the Gene Regulation Knowledge Commons (GRKC). Here the GREEKC consortium describes its main achievements in improving this GRKC.

Gene Ontology curation of the blood-brain barrier to improve the analysis of Alzheimer's and other neurological diseases.

The role of the blood-brain barrier (BBB) in Alzheimer's and other neurodegenerative diseases is still the subject of many studies. However, those studies using high-throughput methods have been compromised by the lack of Gene Ontology (GO) annotations describing the role of proteins in the normal function of the BBB. The GO Consortium provides a gold-standard bioinformatics resource used for analysis and interpretation of large biomedical data sets. However, the GO is also used by other research communities and, therefore, must meet a variety of demands on the breadth and depth of information that is provided. To meet the needs of the Alzheimer's research community we have focused on the GO annotation of the BBB, with over 100 transport or junctional proteins prioritized for annotation. This project has led to a substantial increase in the number of human proteins associated with BBB-relevant GO terms as well as more comprehensive annotation of these proteins in many other processes. Furthermore, data describing the microRNAs that regulate the expression of these priority proteins have also been curated. Thus, this project has increased both the breadth and depth of annotation for these prioritized BBB proteins. Database URLhttps://www.ebi.ac.uk/QuickGO/.

A GO catalogue of human DNA-binding transcription factors.

To control gene transcription, DNA-binding transcription factors recognise specific sequence motifs in gene regulatory regions. A complete and reliable GO annotation of all DNA-binding transcription factors is key to investigating the delicate balance of gene regulation in response to environmental and developmental stimuli. The need for such information is demonstrated by the many lists of transcription factors that have been produced over the past decade. The COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC) Consortium brought together experts in the field of transcription with the aim of providing high quality and interoperable gene regulatory data. The Gene Ontology (GO) Consortium provides strict definitions for gene product function, including factors that regulate transcription. The collaboration between the GREEKC and GO Consortia has enabled the application of those definitions to produce a new curated catalogue of over 1400 human DNA-binding transcription factors, that can be accessed at https://www.ebi.ac.uk/QuickGO/targetset/dbTF. This catalogue has facilitated an improvement in the GO annotation of human DNA-binding transcription factors and led to the GO annotation of almost sixty thousand DNA-binding transcription factors in over a hundred species. Thus, this work will aid researchers investigating the regulation of transcription in both biomedical and basic science.

Crowdsourcing biocuration: The Community Assessment of Community Annotation with Ontologies (CACAO).

Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5,000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a 10-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills.

Formalization of gene regulation knowledge using ontologies and gene ontology causal activity models.

Gene regulation computational research requires handling and integrating large amounts of heterogeneous data. The Gene Ontology has demonstrated that ontologies play a fundamental role in biological data interoperability and integration. Ontologies help to express data and knowledge in a machine processable way, which enables complex querying and advanced exploitation of distributed data. Contributing to improve data interoperability in gene regulation is a major objective of the GREEKC Consortium, which aims to develop a standardized gene regulation knowledge commons. GREEKC proposes the use of ontologies and semantic tools for developing interoperable gene regulation knowledge models, which should support data annotation. In this work, we study how such knowledge models can be generated from cartoons of gene regulation scenarios. The proposed method consists of generating descriptions in natural language of the cartoons; extracting the entities from the texts; finding those entities in existing ontologies to reuse as much content as possible, especially from well known and maintained ontologies such as the Gene Ontology, the Sequence Ontology, the Relations Ontology and ChEBI; and implementation of the knowledge models. The models have been implemented using Protégé, a general ontology editor, and Noctua, the tool developed by the Gene Ontology Consortium for the development of causal activity models to capture more comprehensive annotations of genes and link their activities in a causal framework for Gene Ontology Annotations. We applied the method to two gene regulation scenarios and illustrate how to apply the models generated to support the annotation of data from research articles.

Sequence Ontology terminology for gene regulation.

The Sequence Ontology (SO) is a structured, controlled vocabulary that provides terms and definitions for genomic annotation. The Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC) initiative has gathered input from many groups of researchers, including the SO, the Gene Ontology (GO), and gene regulation experts, with the goal of curating information about how gene expression is regulated at the molecular level. Here we discuss recent updates to the SO reflecting current knowledge. We have developed more accurate human-readable terms (also known as classes), including new definitions, and relationships related to the expression of genes. New findings continue to give us insight into the biology of gene regulation, including the order of events, and participants in those events. These updates to the SO support logical reasoning with the current understanding of gene expression regulation at the molecular level.

Gene Ontology representation for transcription factor functions.

Transcription plays a central role in defining the identity and functionalities of cells, as well as in their responses to changes in the cellular environment. The Gene Ontology (GO) provides a rigorously defined set of concepts that describe the functions of gene products. A GO annotation is a statement about the function of a particular gene product, represented as an association between a gene product and the biological concept a GO term defines. Critically, each GO annotation is based on traceable scientific evidence. Here, we describe the different GO terms that are associated with proteins involved in transcription and its regulation, focusing on the standard of evidence required to support these associations. This article is intended to help users of GO annotations understand how to interpret the annotations and can contribute to the consistency of GO annotations. We distinguish between three classes of activities involved in transcription or directly regulating it - general transcription factors, DNA-binding transcription factors, and transcription co-regulators.

Student biocuration projects as a learning environment.

Background: Bioinformatics is becoming an essential tool for the majority of biological and biomedical researchers. Although bioinformatics data is exploited by academic and industrial researchers, limited focus is on teaching this area to undergraduates, postgraduates and senior scientists. Many scientists are developing their own expertise without formal training and often without appreciating the source of the data they are reliant upon. Some universities do provide courses on a variety of bioinformatics resources and tools, a few also provide biocuration projects, during which students submit data to annotation resources. Methods: To assess the usefulness and enjoyability of annotation projects a survey was sent to University College London (UCL) students who have undertaken Gene Ontology biocuration projects. Results: Analysis of survey responses suggest that these projects provide students with an opportunity not only to learn about bioinformatics resources but also to improve their literature analysis, presentation and writing skills. Conclusion: Biocuration student projects provide valuable annotations as well as enabling students to develop a variety of skills relevant to their future careers. It is also hoped that, as future scientists, these students will critically assess their own manuscripts and ensure that these are written with the biocurators of the future in mind.

The Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST).

MOTIVATION: A large variety of molecular interactions occurs between biomolecular components in cells. When a molecular interaction results in a regulatory effect, exerted by one component onto a downstream component, a so-called 'causal interaction' takes place. Causal interactions constitute the building blocks in our understanding of larger regulatory networks in cells. These causal interactions and the biological processes they enable (e.g. gene regulation) need to be described with a careful appreciation of the underlying molecular reactions. A proper description of this information enables archiving, sharing and reuse by humans and for automated computational processing. Various representations of causal relationships between biological components are currently used in a variety of resources. RESULTS: Here, we propose a checklist that accommodates current representations, called the Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST). This checklist defines both the required core information, as well as a comprehensive set of other contextual details valuable to the end user and relevant for reusing and reproducing causal molecular interaction information. The MI2CAST checklist can be used as reporting guidelines when annotating and curating causal statements, while fostering uniformity and interoperability of the data across resources. AVAILABILITY AND IMPLEMENTATION: The checklist together with examples is accessible at https://github.com/MI2CAST/MI2CAST. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Towards a unified open access dataset of molecular interactions.

The International Molecular Exchange (IMEx) Consortium provides scientists with a single body of experimentally verified protein interactions curated in rich contextual detail to an internationally agreed standard. In this update to the work of the IMEx Consortium, we discuss how this initiative has been working in practice, how it has ensured database sustainability, and how it is meeting emerging annotation challenges through the introduction of new interactor types and data formats. Additionally, we provide examples of how IMEx data are being used by biomedical researchers and integrated in other bioinformatic tools and resources.

Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns.

Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes probably reflects errors in literature curation, ontology structure or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotation errors, based on and validated by case studies including the three we present here: fission yeast protein-coding gene annotations over time; annotations for cohesin complex subunits in human and model species; and annotations using a selected set of GO biological process terms in human and five model species. For each case study, we reviewed available GO annotations, identified pairs of biological processes which are unlikely to be correctly co-annotated to the same gene products (e.g. amino acid metabolism and cytokinesis), and traced erroneous annotations to their sources. To date we have generated 107 quality control rules, and corrected 289 manual annotations in eukaryotes and over 52 700 automatically propagated annotations across all taxa.

A Coordinated Approach by Public Domain Bioinformatics Resources to Aid the Fight Against Alzheimer's Disease Through Expert Curation of Key Protein Targets.

BACKGROUND: The analysis and interpretation of data generated from patient-derived clinical samples relies on access to high-quality bioinformatics resources. These are maintained and updated by expert curators extracting knowledge from unstructured biological data described in free-text journal articles and converting this into more structured, computationally-accessible forms. This enables analyses such as functional enrichment of sets of genes/proteins using the Gene Ontology, and makes the searching of data more productive by managing issues such as gene/protein name synonyms, identifier mapping, and data quality. OBJECTIVE: To undertake a coordinated annotation update of key public-domain resources to better support Alzheimer's disease research. METHODS: We have systematically identified target proteins critical to disease process, in part by accessing informed input from the clinical research community. RESULTS: Data from 954 papers have been added to the UniProtKB, Gene Ontology, and the International Molecular Exchange Consortium (IMEx) databases, with 299 human proteins and 279 orthologs updated in UniProtKB. 745 binary interactions were added to the IMEx human molecular interaction dataset. CONCLUSION: This represents a significant enhancement in the expert curated data pertinent to Alzheimer's disease available in a number of biomedical databases. Relevant protein entries have been updated in UniProtKB and concomitantly in the Gene Ontology. Molecular interaction networks have been significantly extended in the IMEx Consortium dataset and a set of reference protein complexes created. All the resources described are open-source and freely available to the research community and we provide examples of how these data could be exploited by researchers.

PINOT: an intuitive resource for integrating protein-protein interactions.

BACKGROUND: The past decade has seen the rise of omics data for the understanding of biological systems in health and disease. This wealth of information includes protein-protein interaction (PPI) data derived from both low- and high-throughput assays, which are curated into multiple databases that capture the extent of available information from the peer-reviewed literature. Although these curation efforts are extremely useful, reliably downloading and integrating PPI data from the variety of available repositories is challenging and time consuming. METHODS: We here present a novel user-friendly web-resource called PINOT (Protein Interaction Network Online Tool; available at http://www.reading.ac.uk/bioinf/PINOT/PINOT_form.html) to optimise the collection and processing of PPI data from IMEx consortium associated repositories (members and observers) and WormBase, for constructing, respectively, human and Caenorhabditis elegans PPI networks. RESULTS: Users submit a query containing a list of proteins of interest for which PINOT extracts data describing PPIs. At every query submission PPI data are downloaded, merged and quality assessed. Then each PPI is confidence scored based on the number of distinct methods used for interaction detection and the number of publications that report the specific interaction. Examples of how PINOT can be applied are provided to highlight the performance, ease of use and potential utility of this tool. CONCLUSIONS: PINOT is a tool that allows users to survey the curated literature, extracting PPI data in relation to a list of proteins of interest. PINOT extracts a similar numbers of PPIs as other, analogous, tools and incorporates a set of innovative features. PINOT is able to process large queries, it downloads human PPIs live through PSICQUIC and it applies quality control filters on the downloaded PPI data (i.e. removing the need for manual inspection by the user). PINOT provides the user with information on detection methods and publication history for each downloaded interaction data entry and outputs the results in a table format that can be straightforwardly further customised and/or directly uploaded into network visualization software. Video abstract.

Gene Ontology Curation of Neuroinflammation Biology Improves the Interpretation of Alzheimer's Disease Gene Expression Data.

BACKGROUND: Gene Ontology (GO) is a major bioinformatic resource used for analysis of large biomedical datasets, for example from genome-wide association studies, applied universally across biological fields, including Alzheimer's disease (AD) research. OBJECTIVE: We aim to demonstrate the applicability of GO for interpretation of AD datasets to improve the understanding of the underlying molecular disease mechanisms, including the involvement of inflammatory pathways and dysregulated microRNAs (miRs). METHODS: We have undertaken a systematic full article GO annotation approach focused on microglial proteins implicated in AD and the miRs regulating their expression. PANTHER was used for enrichment analysis of previously published AD data. Cytoscape was used for visualizing and analyzing miR-target interactions captured from published experimental evidence. RESULTS: We contributed 3,084 new annotations for 494 entities, i.e., on average six new annotations per entity. This included a total of 1,352 annotations for 40 prioritized microglial proteins implicated in AD and 66 miRs regulating their expression, yielding an average of twelve annotations per prioritized entity. The updated GO resource was then used to re-analyze previously published data. The re-analysis showed novel processes associated with AD-related genes, not identified in the original study, such as 'gliogenesis', 'regulation of neuron projection development', or 'response to cytokine', demonstrating enhanced applicability of GO for neuroscience research. CONCLUSIONS: This study highlights ongoing development of the neurobiological aspects of GO and demonstrates the value of biocuration activities in the area, thus helping to delineate the molecular bases of AD to aid the development of diagnostic tools and treatments.

Non-coding RNA regulatory networks.

It is well established that the vast majority of human RNA transcripts do not encode for proteins and that non-coding RNAs regulate cell physiology and shape cellular functions. A subset of them is involved in gene regulation at different levels, from epigenetic gene silencing to post-transcriptional regulation of mRNA stability. Notably, the aberrant expression of many non-coding RNAs has been associated with aggressive pathologies. Rapid advances in network biology indicates that the robustness of cellular processes is the result of specific properties of biological networks such as scale-free degree distribution and hierarchical modularity, suggesting that regulatory network analyses could provide new insights on gene regulation and dysfunction mechanisms. In this study we present an overview of public repositories where non-coding RNA-regulatory interactions are collected and annotated, we discuss unresolved questions for data integration and we recall existing resources to build and analyse networks.