Mouse model phenotypes provide information about human drug targets.

MOTIVATION: Methods for computational drug target identification use information from diverse information sources to predict or prioritize drug targets for known drugs. One set of resources that has been relatively neglected for drug repurposing is animal model phenotype. RESULTS: We investigate the use of mouse model phenotypes for drug target identification. To achieve this goal, we first integrate mouse model phenotypes and drug effects, and then systematically compare the phenotypic similarity between mouse models and drug effect profiles. We find a high similarity between phenotypes resulting from loss-of-function mutations and drug effects resulting from the inhibition of a protein through a drug action, and demonstrate how this approach can be used to suggest candidate drug targets. AVAILABILITY AND IMPLEMENTATION: Analysis code and supplementary data files are available on the project Web site at https://drugeffects.googlecode.com.

Analyzing gene expression data in mice with the Neuro Behavior Ontology.

We have applied the Neuro Behavior Ontology (NBO), an ontology for the annotation of behavioral gene functions and behavioral phenotypes, to the annotation of more than 1,000 genes in the mouse that are known to play a role in behavior. These annotations can be explored by researchers interested in genes involved in particular behaviors and used computationally to provide insights into the behavioral phenotypes resulting from differences in gene expression. We developed the OntoFUNC tool and have applied it to enrichment analyses over the NBO to provide high-level behavioral interpretations of gene expression datasets. The resulting increase in the number of gene annotations facilitates the identification of behavioral or neurologic processes by assisting the formulation of hypotheses about the relationships between gene, processes, and phenotypic manifestations resulting from behavioral observations.

Systematic analysis of experimental phenotype data reveals gene functions.

High-throughput phenotyping projects in model organisms have the potential to improve our understanding of gene functions and their role in living organisms. We have developed a computational, knowledge-based approach to automatically infer gene functions from phenotypic manifestations and applied this approach to yeast (Saccharomyces cerevisiae), nematode worm (Caenorhabditis elegans), zebrafish (Danio rerio), fruitfly (Drosophila melanogaster) and mouse (Mus musculus) phenotypes. Our approach is based on the assumption that, if a mutation in a gene [Formula: see text] leads to a phenotypic abnormality in a process [Formula: see text], then [Formula: see text] must have been involved in [Formula: see text], either directly or indirectly. We systematically analyze recorded phenotypes in animal models using the formal definitions created for phenotype ontologies. We evaluate the validity of the inferred functions manually and by demonstrating a significant improvement in predicting genetic interactions and protein-protein interactions based on functional similarity. Our knowledge-based approach is generally applicable to phenotypes recorded in model organism databases, including phenotypes from large-scale, high throughput community projects whose primary mode of dissemination is direct publication on-line rather than in the literature.

Practical applications of metabolomics in plant biology.

The technologies being developed for the large-scale, essentially unbiased analysis of the small molecules present in organic extracts made from plant materials are greatly changing our way of thinking about what is possible in plant biology. A range of different separation and detection techniques are being refined and expanded and their combination with advanced data management and data analysis approaches is already giving plant scientists far deeper insights into the complexity of plant metabolism and plant metabolic composition than was imaginable just a few years ago. This field of "metabolomics", while still in its infancy, has nevertheless already been welcomed with open arms by the plant science community, partly because of these said advantages but also because of the broad potential applicability of the approaches in both fundamental and applied science. The diversity in application already ranges from understanding the considerable complexity of primary metabolic networks in Arabidopsis, to the changes which occur in the biochemical composition of foods occurring, for example, during the Pasteurization of tomato purée for long-term storage or the boiling of Basmati rice for direct consumption. The insights being gained are revealing valuable information on the strict control yet flexible nature of plant metabolic networks in many different systems. This volume aims to give a comprehensive overview of the approaches available for the performance of a "typical" plant metabolomics experiment, the choice of analytical techniques and to offer warnings on the potential pitfalls in experimental design and execution.

A strategy for selecting data mining techniques in metabolomics.

There is a general agreement that the development of metabolomics depends not only on advances in chemical analysis techniques but also on advances in computing and data analysis methods. Metabolomics data usually requires intensive pre-processing, analysis, and mining procedures. Selecting and applying such procedures requires attention to issues including justification, traceability, and reproducibility. We describe a strategy for selecting data mining techniques which takes into consideration the goals of data mining techniques on the one hand, and the goals of metabolomics investigations and the nature of the data on the other. The strategy aims to ensure the validity and soundness of results and promote the achievement of the investigation goals.