Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

Solving functional reliability issue for an optical electrostatic switch

In this paper, we report the advantage of using AC actuating signal for driving MEMS actuators instead of DC voltages. The study is based upon micro mirror devices used in digital mode for optical switching operation. When the pull-in effect is used, charge injection occurs when the micro mirror is maintained in the deflected position. To avoid this effect, a geometrical solution is to realize grounded landing electrodes which are electro-statically separated from the control electrodes. Another solution is the use of AC signal which eliminates charge injection particularly if a bipolar signal is used. Long term experiments have demonstrated the reliability of such a signal command to avoid injection of electric charges.Comment: Submitted on behalf of EDA Publishing Association (http://irevues.inist.fr/EDA-Publishing

The evolutionary signal in metagenome phyletic profiles predicts many gene functions

Background. The function of many genes is still not known even in model organisms. An increasing availability of microbiome DNA sequencing data provides an opportunity to infer gene function in a systematic manner. Results. We evaluated if the evolutionary signal contained in metagenome phyletic profiles (MPP) is predictive of a broad array of gene functions. The MPPs are an encoding of environmental DNA sequencing data that consists of relative abundances of gene families across metagenomes. We find that such MPPs can accurately predict 826 Gene Ontology functional categories, while drawing on human gut microbiomes, ocean metagenomes, and DNA sequences from various other engineered and natural environments. Overall, in this task, the MPPs are highly accurate, and moreover they provide coverage for a set of Gene Ontology terms largely complementary to standard phylogenetic profiles, derived from fully sequenced genomes. We also find that metagenomes approximated from taxon relative abundance obtained via 16S rRNA gene sequencing may provide surprisingly useful predictive models. Crucially, the MPPs derived from different types of environments can infer distinct, non-overlapping sets of gene functions and therefore complement each other. Consistently, simulations on > 5000 metagenomes indicate that the amount of data is not in itself critical for maximizing predictive accuracy, while the diversity of sampled environments appears to be the critical factor for obtaining robust models. Conclusions. In past work, metagenomics has provided invaluable insight into ecology of various habitats, into diversity of microbial life and also into human health and disease mechanisms. We propose that environmental DNA sequencing additionally constitutes a useful tool to predict biological roles of genes, yielding inferences out of reach for existing comparative genomics approaches

Development of Cryogenic Current Comparators with DC Squid Readout for the Calibration of Electrical Standards

For the realization of the electrical quantum metrology triangle (V-A-Ω) a device to amplify very small currents with high precision is needed. The cryogenic current comparator (CCC) is by far the best instrument to do this. In order to make a very current sensitive CCC for calibration of electrical standards, we have developed optimum dc Superconducting QUantum Interference Devices (SQUIDs). The design, fabrication and characterisation of these devices is presented. The measurements concern the flux-to-voltage transfer and the noise properties, especially the input current noise. The optimisation of the flux transformer circuit that links the CCC with the SQUID will be treated. In addition, typical fabrication aspects of the CCC as the wires and tube assembly, the shields and the support system will be addressed

Quality of Computationally Inferred Gene Ontology Annotations

Gene Ontology (GO) has established itself as the undisputed standard for protein function annotation. Most annotations are inferred electronically, i.e. without individual curator supervision, but they are widely considered unreliable. At the same time, we crucially depend on those automated annotations, as most newly sequenced genomes are non-model organisms. Here, we introduce a methodology to systematically and quantitatively evaluate electronic annotations. By exploiting changes in successive releases of the UniProt Gene Ontology Annotation database, we assessed the quality of electronic annotations in terms of specificity, reliability, and coverage. Overall, we not only found that electronic annotations have significantly improved in recent years, but also that their reliability now rivals that of annotations inferred by curators when they use evidence other than experiments from primary literature. This work provides the means to identify the subset of electronic annotations that can be relied upon—an important outcome given that >98% of all annotations are inferred without direct curation

GenoWatch: a disease gene mining browser for association study

A human gene association study often involves several genomic markers such as single nucleotide polymorphisms (SNPs) or short tandem repeat polymorphisms, and many statistically significant markers may be identified during the study. GenoWatch can efficiently extract up-to-date information about multiple markers and their associated genes in batch mode from many relevant biological databases in real-time. The comprehensive gene information retrieved includes gene ontology, function, pathway, disease, related articles in PubMed and so on. Subsequent SNP functional impact analysis and primer design of a target gene for re-sequencing can also be done in a few clicks. The presentation of results has been carefully designed to be as intuitive as possible to all users

Mining the Gene Wiki for functional genomic knowledge

<p>Abstract</p> <p>Background</p> <p>Ontology-based gene annotations are important tools for organizing and analyzing genome-scale biological data. Collecting these annotations is a valuable but costly endeavor. The Gene Wiki makes use of Wikipedia as a low-cost, mass-collaborative platform for assembling text-based gene annotations. The Gene Wiki is comprised of more than 10,000 review articles, each describing one human gene. The goal of this study is to define and assess a computational strategy for translating the text of Gene Wiki articles into ontology-based gene annotations. We specifically explore the generation of structured annotations using the Gene Ontology and the Human Disease Ontology.</p> <p>Results</p> <p>Our system produced 2,983 candidate gene annotations using the Disease Ontology and 11,022 candidate annotations using the Gene Ontology from the text of the Gene Wiki. Based on manual evaluations and comparisons to reference annotation sets, we estimate a precision of 90-93% for the Disease Ontology annotations and 48-64% for the Gene Ontology annotations. We further demonstrate that this data set can systematically improve the results from gene set enrichment analyses.</p> <p>Conclusions</p> <p>The Gene Wiki is a rapidly growing corpus of text focused on human gene function. Here, we demonstrate that the Gene Wiki can be a powerful resource for generating ontology-based gene annotations. These annotations can be used immediately to improve workflows for building curated gene annotation databases and knowledge-based statistical analyses.</p

A transversal approach to predict gene product networks from ontology-based similarity

<p>Abstract</p> <p>Background</p> <p>Interpretation of transcriptomic data is usually made through a "standard" approach which consists in clustering the genes according to their expression patterns and exploiting Gene Ontology (GO) annotations within each expression cluster. This approach makes it difficult to underline functional relationships between gene products that belong to different expression clusters. To address this issue, we propose a transversal analysis that aims to predict functional networks based on a combination of GO processes and data expression.</p> <p>Results</p> <p>The transversal approach presented in this paper consists in computing the semantic similarity between gene products in a Vector Space Model. Through a weighting scheme over the annotations, we take into account the representativity of the terms that annotate a gene product. Comparing annotation vectors results in a matrix of gene product similarities. Combined with expression data, the matrix is displayed as a set of functional gene networks. The transversal approach was applied to 186 genes related to the enterocyte differentiation stages. This approach resulted in 18 functional networks proved to be biologically relevant. These results were compared with those obtained through a standard approach and with an approach based on information content similarity.</p> <p>Conclusion</p> <p>Complementary to the standard approach, the transversal approach offers new insight into the cellular mechanisms and reveals new research hypotheses by combining gene product networks based on semantic similarity, and data expression.</p

Tomato Functional Genomics Database: a comprehensive resource and analysis package for tomato functional genomics

Tomato Functional Genomics Database (TFGD) provides a comprehensive resource to store, query, mine, analyze, visualize and integrate large-scale tomato functional genomics data sets. The database is functionally expanded from the previously described Tomato Expression Database by including metabolite profiles as well as large-scale tomato small RNA (sRNA) data sets. Computational pipelines have been developed to process microarray, metabolite and sRNA data sets archived in the database, respectively, and TFGD provides downloads of all the analyzed results. TFGD is also designed to enable users to easily retrieve biologically important information through a set of efficient query interfaces and analysis tools, including improved array probe annotations as well as tools to identify co-expressed genes, significantly affected biological processes and biochemical pathways from gene expression data sets and miRNA targets, and to integrate transcript and metabolite profiles, and sRNA and mRNA sequences. The suite of tools and interfaces in TFGD allow intelligent data mining of recently released and continually expanding large-scale tomato functional genomics data sets. TFGD is available at http://ted.bti.cornell.edu