dcGOR: An R Package for Analysing Ontologies and Protein Domain Annotations

I introduce an open-source R package ‘dcGOR’ to provide the bioinformatics community with the ease to analyse ontologies and protein domain annotations, particularly those in the dcGO database. The dcGO is a comprehensive resource for protein domain annotations using a panel of ontologies including Gene Ontology. Although increasing in popularity, this database needs statistical and graphical support to meet its full potential. Moreover, there are no bioinformatics tools specifically designed for domain ontology analysis. As an add-on package built in the R software environment, dcGOR offers a basic infrastructure with great flexibility and functionality. It implements new data structure to represent domains, ontologies, annotations, and all analytical outputs as well. For each ontology, it provides various mining facilities, including: (i) domain-based enrichment analysis and visualisation; (ii) construction of a domain (semantic similarity) network according to ontology annotations; and (iii) significance analysis for estimating a contact (statistical significance) network. To reduce runtime, most analyses support high-performance parallel computing. Taking as inputs a list of protein domains of interest, the package is able to easily carry out in-depth analyses in terms of functional, phenotypic and diseased relevance, and network-level understanding. More importantly, dcGOR is designed to allow users to import and analyse their own ontologies and annotations on domains (taken from SCOP, Pfam and InterPro) and RNAs (from Rfam) as well. The package is freely available at CRAN for easy installation, and also at GitHub for version control. The dedicated website with reproducible demos can be found at http://supfam.org/dcGOR.

This is a PLOS Computational Biology Software Article

     

OntoBlast function: From sequence similarities directly to potential functional annotations by ontology terms

OntoBlast allows one to find information about potential functions of proteins by presenting a weighted list of ontology entries associated with similar sequences from completely sequenced genomes identified in a BLAST search. It combines, in a single analysis step, the search for sequence similarities in several species with the association of information stored in ontologies. From each identified ontology term a list of genes, which share the functional annotation, can be retrieved. The OntoBlast function is an integral part of the 'Ontologies TO GenomeMatrix' tool which provides an alternative entry point from ontology terms to the Genome-Matrix database. OntoBlast's web interface is accessible on the 'Ontologies TO GenomeMatrix Gate' page at http://functionalgenomics.de/ontogate/.     

Representing genetic sequence data for pharmacogenomics: an evolutionary approach using ontological and relational models

MOTIVATION: The information model chosen to store biological data affects the types of queries possible, database performance, and difficulty in updating that information model. Genetic sequence data for pharmacogenetics studies can be complex, and the best information model to use may change over time. As experimental and analytical methods change, and as biological knowledge advances, the data storage requirements and types of queries needed may also change. RESULTS: We developed a model for genetic sequence and polymorphism data, and used XML Schema to specify the elements and attributes required for this model. We implemented this model as an ontology in a frame-based representation and as a relational model in a database system. We collected genetic data from two pharmacogenetics resequencing studies, and formulated queries useful for analysing these data. We compared the ontology and relational models in terms of query complexity, performance, and difficulty in changing the information model. Our results demonstrate benefits of evolving the schema for storing pharmacogenetics data: ontologies perform well in early design stages as the information model changes rapidly and simplify query formulation, while relational models offer improved query speed once the information model and types of queries needed stabilize.     

Ontologies: Formalising biological knowledge for bioinformatics

An ontology is a domain of knowledge structured through formal rules so that it can be interpreted and used by computers. Ontologies are becoming increasingly important in bioinformatics because they can be linked to the information in databases and their knowledge then used to query the databases. Typical examples in current use are the Gene Ontology, which incorporates much of our knowledge about gene products, and ontologies of developmental anatomy, which, for example, facilitate tissue-based queries to gene expression databases both textually and spatially. This article considers the production, formulation and types of bio-ontologies together with the reasons why they are so useful.     

Data exploration tools for the Gene Ontology database

MOTIVATION: To improve the ability of biologists (both researchers and students) to ask biologically interesting questions of the Gene Ontology (GO) database and to explore the ontologies by seeing large portions of the ontology graphs in context, along with details of individual terms in the ontologies. RESULTS: GoGet and GoView are two new tools built as part of an extensible web application system based on Java 2 Enterprise Edition technology. GoGet has a user interface that enables users to ask biologically interesting questions, such as (1) What are the DNA binding proteins involved in DNA repair, but not in DNA replication? and (2) Of the terms containing the word triphosphatase, which have associated gene products from mouse, but not fruit fly? The results of such queries can be viewed in a collapsed tabular format that eases the burden of getting through large tables of data. GoView enables users to explore the large directed acyclic graph structure of the ontologies in the GO database. The two tools are coordinated, so that results from queries in GoGet can be visualized in GoView in the ontology in which they appear, and explorations started from GoView can request details of gene product associations to appear in a result table in GoGet. AVAILABILITY: Free access to the GoGet query tool and free download of the GoView ontology viewer are provided to all users at http://db.math.macalester.edu/goproject. In addition, source code for the GoView tool is also available from this site, along with a user manual for both tools.     

Methodology for the inference of gene function from phenotype data

Background

Biomedical ontologies are increasingly instrumental in the advancement of biological research primarily through their use to efficiently consolidate large amounts of data into structured, accessible sets. However, ontology development and usage can be hampered by the segregation of knowledge by domain that occurs due to independent development and use of the ontologies. The ability to infer data associated with one ontology to data associated with another ontology would prove useful in expanding information content and scope. We here focus on relating two ontologies: the Gene Ontology (GO), which encodes canonical gene function, and the Mammalian Phenotype Ontology (MP), which describes non-canonical phenotypes, using statistical methods to suggest GO functional annotations from existing MP phenotype annotations. This work is in contrast to previous studies that have focused on inferring gene function from phenotype primarily through lexical or semantic similarity measures.

Results

We have designed and tested a set of algorithms that represents a novel methodology to define rules for predicting gene function by examining the emergent structure and relationships between the gene functions and phenotypes rather than inspecting the terms semantically. The algorithms inspect relationships among multiple phenotype terms to deduce if there are cases where they all arise from a single gene function.We apply this methodology to data about genes in the laboratory mouse that are formally represented in the Mouse Genome Informatics (MGI) resource. From the data, 7444 rule instances were generated from five generalized rules, resulting in 4818 unique GO functional predictions for 1796 genes.

Conclusions

We show that our method is capable of inferring high-quality functional annotations from curated phenotype data. As well as creating inferred annotations, our method has the potential to allow for the elucidation of unforeseen, biologically significant associations between gene function and phenotypes that would be overlooked by a semantics-based approach. Future work will include the implementation of the described algorithms for a variety of other model organism databases, taking full advantage of the abundance of available high quality curated data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0405-z) contains supplementary material, which is available to authorized users.     

A top-level ontology of functions and its application in the Open Biomedical Ontologies

MOTIVATION: A clear understanding of functions in biology is a key component in accurate modelling of molecular, cellular and organismal biology. Using the existing biomedical ontologies it has been impossible to capture the complexity of the community's knowledge about biological functions. RESULTS: We present here a top-level ontological framework for representing knowledge about biological functions. This framework lends greater accuracy, power and expressiveness to biomedical ontologies by providing a means to capture existing functional knowledge in a more formal manner. An initial major application of the ontology of functions is the provision of a principled way in which to curate functional knowledge and annotations in biomedical ontologies. Further potential applications include the facilitation of ontology interoperability and automated reasoning. A major advantage of the proposed implementation is that it is an extension to existing biomedical ontologies, and can be applied without substantial changes to these domain ontologies. AVAILABILITY: The Ontology of Functions (OF) can be downloaded in OWL format from http://onto.eva.mpg.de/. Additionally, a UML profile and supplementary information and guides for using the OF can be accessed from the same website.     

CRAVE: a database, middleware and visualization system for phenotype ontologies

MOTIVATION: A major challenge in modern biology is to link genome sequence information to organismal function. In many organisms this is being done by characterizing phenotypes resulting from mutations. Efficiently expressing phenotypic information requires combinatorial use of ontologies. However tools are not currently available to visualize combinations of ontologies. Here we describe CRAVE (Concept Relation Assay Value Explorer), a package allowing storage, active updating and visualization of multiple ontologies. RESULTS: CRAVE is a web-accessible JAVA application that accesses an underlying MySQL database of ontologies via a JAVA persistent middleware layer (Chameleon). This maps the database tables into discrete JAVA classes and creates memory resident, interlinked objects corresponding to the ontology data. These JAVA objects are accessed via calls through the middleware's application programming interface. CRAVE allows simultaneous display and linking of multiple ontologies and searching using Boolean and advanced searches.     

Ontology annotation: mapping genomic regions to biological function

With numerous whole genomes now in hand, and experimental data about genes and biological pathways on the increase, a systems approach to biological research is becoming essential. Ontologies provide a formal representation of knowledge that is amenable to computational as well as human analysis, an obvious underpinning of systems biology. Mapping function to gene products in the genome consists of two, somewhat intertwined enterprises: ontology building and ontology annotation. Ontology building is the formal representation of a domain of knowledge; ontology annotation is association of specific genomic regions (which we refer to simply as 'genes', including genes and their regulatory elements and products such as proteins and functional RNAs) to parts of the ontology. We consider two complementary representations of gene function: the Gene Ontology (GO) and pathway ontologies. GO represents function from the gene's eye view, in relation to a large and growing context of biological knowledge at all levels. Pathway ontologies represent function from the point of view of biochemical reactions and interactions, which are ordered into networks and causal cascades. The more mature GO provides an example of ontology annotation: how conclusions from the scientific literature and from evolutionary relationships are converted into formal statements about gene function. Annotations are made using a variety of different types of evidence, which can be used to estimate the relative reliability of different annotations.     

Interoperability between biomedical ontologies through relation expansion, upper-level ontologies and automatic reasoning

Researchers design ontologies as a means to accurately annotate and integrate experimental data across heterogeneous and disparate data- and knowledge bases. Formal ontologies make the semantics of terms and relations explicit such that automated reasoning can be used to verify the consistency of knowledge. However, many biomedical ontologies do not sufficiently formalize the semantics of their relations and are therefore limited with respect to automated reasoning for large scale data integration and knowledge discovery. We describe a method to improve automated reasoning over biomedical ontologies and identify several thousand contradictory class definitions. Our approach aligns terms in biomedical ontologies with foundational classes in a top-level ontology and formalizes composite relations as class expressions. We describe the semi-automated repair of contradictions and demonstrate expressive queries over interoperable ontologies. Our work forms an important cornerstone for data integration, automatic inference and knowledge discovery based on formal representations of knowledge. Our results and analysis software are available at http://bioonto.de/pmwiki.php/Main/ReasonableOntologies.     

Large-scale biomedical ontology matching with ServOMap

The proliferation of biomedical applications, which rely on different knowledge organization systems, such as ontologies and thesauri raises the issue of the automated identification of the correspondences between these models, in particular for the data integration need. A significant effort has been conducted for tackling this issue of ontology alignment. However, few systems are able to deal with ontologies containing tens of thousands of entities, as it may be the case in the biomedical domain where resources such as SNOMED-CT, the FMA or the NCI thesaurus are commonly used. We present in this paper ServOMap, an efficient system for large-scale ontology alignment. It relies on an Ontology Server (ServO) and uses Information Retrieval techniques for computing similarity between entities. The system participated with two configurations in the 2012 Ontology Alignment Evaluation Initiative campaign. We report the very promising results obtained by the system for large biomedical ontologies alignment. ServOMap is freely available for download at http://code.google.com/p/servo/.     

An integrated approach to inferring gene-disease associations in humans

One of the most important tasks of modern bioinformatics is the development of computational tools that can be used to understand and treat human disease. To date, a variety of methods have been explored and algorithms for candidate gene prioritization are gaining in their usefulness. Here, we propose an algorithm for detecting gene-disease associations based on the human protein-protein interaction network, known gene-disease associations, protein sequence, and protein functional information at the molecular level. Our method, PhenoPred, is supervised: first, we mapped each gene/protein onto the spaces of disease and functional terms based on distance to all annotated proteins in the protein interaction network. We also encoded sequence, function, physicochemical, and predicted structural properties, such as secondary structure and flexibility. We then trained support vector machines to detect gene-disease associations for a number of terms in Disease Ontology and provided evidence that, despite the noise/incompleteness of experimental data and unfinished ontology of diseases, identification of candidate genes can be successful even when a large number of candidate disease terms are predicted on simultaneously. Availability: www.phenopred.org.     

The Ontology Lookup Service, a lightweight cross-platform tool for controlled vocabulary queries

Background  

With the vast amounts of biomedical data being generated by high-throughput analysis methods, controlled vocabularies and ontologies are becoming increasingly important to annotate units of information for ease of search and retrieval. Each scientific community tends to create its own locally available ontology. The interfaces to query these ontologies tend to vary from group to group. We saw the need for a centralized location to perform controlled vocabulary queries that would offer both a lightweight web-accessible user interface as well as a consistent, unified SOAP interface for automated queries.     

Bio-ontologies: current trends and future directions

In recent years, as a knowledge-based discipline, bioinformatics has been made more computationally amenable. After its beginnings as a technology advocated by computer scientists to overcome problems of heterogeneity, ontology has been taken up by biologists themselves as a means to consistently annotate features from genotype to phenotype. In medical informatics, artifacts called ontologies have been used for a longer period of time to produce controlled lexicons for coding schemes. In this article, we review the current position in ontologies and how they have become institutionalized within biomedicine. As the field has matured, the much older philosophical aspects of ontology have come into play. With this and the institutionalization of ontology has come greater formality. We review this trend and what benefits it might bring to ontologies and their use within biomedicine.     

EcoCyc: an encyclopedia of Escherichia coli genes and metabolism.

The encyclopedia of Escherichia coli genes and metabolism (EcoCyc) is a database that combines information about the genome and the intermediary metabolism of E.coli. It describes 2034 genes, 306 enzymes encoded by these genes, 580 metabolic reactions that occur in E.coli and the organization of these reactions into 100 metabolic pathways. The EcoCyc graphical user interface allows query and exploration of the EcoCyc database using visualization tools such as genomic map browsers and automatic layouts of metabolic pathways. EcoCyc spans the space from sequence to function to allow investigation of an unusually broad range of questions. EcoCyc can be thought of as both an electronic review article, because of its copious references to the primary literature, and as an in silico model of E.coli that can be probed and analyzed through computational means.