Systems developmental biology: the use of ontologies in annotating models and in identifying gene function within and across species

Systems developmental biology is an approach to the study of embryogenesis that attempts to analyze complex developmental processes through integrating the roles of their molecular, cellular, and tissue participants within a computational framework. This article discusses ways of annotating these participants using standard terms and IDs now available in public ontologies (these are areas of hierarchical knowledge formalized to be computationally accessible) for tissues, cells, and processes. Such annotations bring two types of benefit. The first comes from using standard terms: This allows linkage to other resources that use them (e.g., GXD, the gene-expression [G-E] database for mouse development). The second comes from the annotation procedure itself: This can lead to the identification of common processes that are used in very different and apparently unrelated events, even in other organisms. One implication of this is the potential for identifying the genes underpinning common developmental processes in different tissues through Boolean analysis of their G-E profiles. While it is easiest to do this for single organisms, the approach is extendable to analyzing similar processes in different organisms. Although the full computational infrastructure for such an analysis has yet to be put in place, two examples are briefly considered as illustration. First, the early development of the mouse urogenital system shows how a line of development can be graphically formalized using ontologies. Second, Boolean analysis of the G-E profiles of the mesenchyme-to-epithelium transitions that take place during mouse development suggest Lhx1, Foxc1, and Meox1 as candidate transcription factors for mediating this process

Mouse anatomy ontologies:enhancements and tools for exploring and integrating biomedical data

Mouse anatomy ontologies provide standard nomenclature for describing normal and mutant mouse anatomy, and are essential for the description and integration of data directly related to anatomy such as gene expression patterns. Building on our previous work on anatomical ontologies for the embryonic and adult mouse, we have recently developed a new and substantially revised anatomical ontology covering all life stages of the mouse. Anatomical terms are organized in complex hierarchies enabling multiple relationships between terms. Tissue classification as well as partonomic, developmental, and other types of relationships can be represented. Hierarchies for specific developmental stages can also be derived. The ontology forms the core of the eMouse Atlas Project (EMAP) and is used extensively for annotating and integrating gene expression patterns and other data by the Gene Expression Database (GXD), the eMouse Atlas of Gene Expression (EMAGE) and other database resources. Here we illustrate the evolution of the developmental and adult mouse anatomical ontologies toward one combined system. We report on recent ontology enhancements, describe the current status, and discuss future plans for mouse anatomy ontology development and application in integrating data resources. Mamm Genome 2015 Oct; 26(9-10):422-3

Genome Profiling (GP) Method Based Classification of Insects: Congruence with That of Classical Phenotype-Based One

Ribosomal RNAs have been widely used for identification and classification of species, and have produced data giving new insights into phylogenetic relationships. Recently, multilocus genotyping and even whole genome sequencing-based technologies have been adopted in ambitious comparative biology studies. However, such technologies are still far from routine-use in species classification studies due to their high costs in terms of labor, equipment and consumables.Here, we describe a simple and powerful approach for species classification called genome profiling (GP). The GP method composed of random PCR, temperature gradient gel electrophoresis (TGGE) and computer-aided gel image processing is highly informative and less laborious. For demonstration, we classified 26 species of insects using GP and 18S rDNA-sequencing approaches. The GP method was found to give a better correspondence to the classical phenotype-based approach than did 18S rDNA sequencing employing a congruence value. To our surprise, use of a single probe in GP was sufficient to identify the relationships between the insect species, making this approach more straightforward.The data gathered here, together with those of previous studies show that GP is a simple and powerful method that can be applied for actually universally identifying and classifying species. The current success supported our previous proposal that GP-based web database can be constructible and effective for the global identification/classification of species

Accommodating Ontologies to Biological Reality—Top-Level Categories of Cumulative-Constitutively Organized Material Entities

BACKGROUND: The Basic Formal Ontology (BFO) is a top-level formal foundational ontology for the biomedical domain. It has been developed with the purpose to serve as an ontologically consistent template for top-level categories of application oriented and domain reference ontologies within the Open Biological and Biomedical Ontologies Foundry (OBO). BFO is important for enabling OBO ontologies to facilitate in reliably communicating and managing data and metadata within and across biomedical databases. Following its intended single inheritance policy, BFO's three top-level categories of material entity (i.e. ‘object’, ‘fiat object part’, ‘object aggregate’) must be exhaustive and mutually disjoint. We have shown elsewhere that for accommodating all types of constitutively organized material entities, BFO must be extended by additional categories of material entity. METHODOLOGY/PRINCIPAL FINDINGS: Unfortunately, most biomedical material entities are cumulative-constitutively organized. We show that even the extended BFO does not exhaustively cover cumulative-constitutively organized material entities. We provide examples from biology and everyday life that demonstrate the necessity for ‘portion of matter’ as another material building block. This implies the necessity for further extending BFO by ‘portion of matter’ as well as three additional categories that possess portions of matter as aggregate components. These extensions are necessary if the basic assumption that all parts that share the same granularity level exhaustively sum to the whole should also apply to cumulative-constitutively organized material entities. By suggesting a notion of granular representation we provide a way to maintain the single inheritance principle when dealing with cumulative-constitutively organized material entities. CONCLUSIONS/SIGNIFICANCE: We suggest to extend BFO to incorporate additional categories of material entity and to rearrange its top-level material entity taxonomy. With these additions and the notion of granular representation, BFO would exhaustively cover all top-level types of material entities that application oriented ontologies may use as templates, while still maintaining the single inheritance principle

A probabilistic framework to predict protein function from interaction data integrated with semantic knowledge

<p>Abstract</p> <p>Background</p> <p>The functional characterization of newly discovered proteins has been a challenge in the post-genomic era. Protein-protein interactions provide insights into the functional analysis because the function of unknown proteins can be postulated on the basis of their interaction evidence with known proteins. The protein-protein interaction data sets have been enriched by high-throughput experimental methods. However, the functional analysis using the interaction data has a limitation in accuracy because of the presence of the false positive data experimentally generated and the interactions that are a lack of functional linkage.</p> <p>Results</p> <p>Protein-protein interaction data can be integrated with the functional knowledge existing in the Gene Ontology (GO) database. We apply similarity measures to assess the functional similarity between interacting proteins. We present a probabilistic framework for predicting functions of unknown proteins based on the functional similarity. We use the leave-one-out cross validation to compare the performance. The experimental results demonstrate that our algorithm performs better than other competing methods in terms of prediction accuracy. In particular, it handles the high false positive rates of current interaction data well.</p> <p>Conclusion</p> <p>The experimentally determined protein-protein interactions are erroneous to uncover the functional associations among proteins. The performance of function prediction for uncharacterized proteins can be enhanced by the integration of multiple data sources available.</p

False positive reduction in protein-protein interaction predictions using gene ontology annotations

<p>Abstract</p> <p>Background</p> <p>Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated.</p> <p>Results</p> <p>Gene Ontology (GO) annotations were used to reduce false positive protein-protein interactions (PPI) pairs resulting from computational predictions. Using experimentally obtained PPI pairs as a training dataset, eight top-ranking keywords were extracted from GO molecular function annotations. The sensitivity of these keywords is 64.21% in the yeast experimental dataset and 80.83% in the worm experimental dataset. The specificities, a measure of recovery power, of these keywords applied to four predicted PPI datasets for each studied organisms, are 48.32% and 46.49% (by average of four datasets) in yeast and worm, respectively. Based on eight top-ranking keywords and co-localization of interacting proteins a set of two knowledge rules were deduced and applied to remove false positive protein pairs. The '<it>strength</it>', a measure of improvement provided by the rules was defined based on the signal-to-noise ratio and implemented to measure the applicability of knowledge rules applying to the predicted PPI datasets. Depending on the employed PPI-predicting methods, the <it>strength </it>varies between two and ten-fold of randomly removing protein pairs from the datasets.</p> <p>Conclusion</p> <p>Gene Ontology annotations along with the deduced knowledge rules could be implemented to partially remove false predicted PPI pairs. Removal of false positives from predicted datasets increases the true positive fractions of the datasets and improves the robustness of predicted pairs as compared to random protein pairing, and eventually results in better overlap with experimental results.</p