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.

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.

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.

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.

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.

Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project.

The Minimum Information for Biological and Biomedical Investigations (MIBBI) project provides a resource for those exploring the range of extant minimum information checklists and fosters coordinated development of such checklists.