Sylva L. Donaldson

GeneMANIA (http://www.genemania.org) is a flexible, user-friendly web interface for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. Given a query list, GeneMANIA extends the list with functionally similar genes that it identifies using available genomics and proteomics data. GeneMANIA also reports weights that indicate the predictive value of each selected data set for the query. Six organisms are currently supported (Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus, Homo sapiens and Saccharomyces cerevisiae) and hundreds of data sets have been collected from GEO, BioGRID, Pathway Commons and I2D, as well as organism-specific functional genomics data sets. Users can select arbitrary subsets of the data sets associated with an organism to perform their analyses and can upload their own data sets to analyze. The GeneMANIA algorithm performs as well or better than other gene function prediction methods on yeast and mouse benchmarks. The high accuracy of the GeneMANIA prediction algorithm, an intuitive user interface and large database make GeneMANIA a useful tool for any biologist.

Abstract Summary: Cytoscape Web is a web-based network visualization tool–modeled after Cytoscape–which is open source, interactive, customizable and easily integrated into web sites. Multiple file exchange formats can be used to load data into Cytoscape Web, including GraphML, XGMML and SIF. Availability and Implementation: Cytoscape Web is implemented in Flex/ActionScript with a JavaScript API and is freely available at http://cytoscapeweb.cytoscape.org/ Contact: gary.bader@utoronto.ca Supplementary information: Supplementary data are available at Bioinformatics online.

UNLABELLED: The GeneMANIA Cytoscape plugin brings fast gene function prediction capabilities to the desktop. GeneMANIA identifies the most related genes to a query gene set using a guilt-by-association approach. The plugin uses over 800 networks from six organisms and each related gene is traceable to the source network used to make the prediction. Users may add their own interaction networks and expression profile data to complement or override the default data. AVAILABILITY AND IMPLEMENTATION: The GeneMANIA Cytoscape plugin is implemented in Java and is freely available at http://www.genemania.org/plugin/.

Understanding the significance of bacterial species that colonize and persist in cystic fibrosis (CF) airways requires a detailed examination of bacterial community structure across a broad range of age and disease stage. We used 16S ribosomal RNA sequencing to characterize the lung microbiota in 269 CF patients spanning a 60 year age range, including 76 pediatric samples from patients of age 4-17, and a broad cross-section of disease status to identify features of bacterial community structure and their relationship to disease stage and age. The CF lung microbiota shows significant inter-individual variability in community structure, composition and diversity. The core microbiota consists of five genera - Streptococcus, Prevotella, Rothia, Veillonella and Actinomyces. CF-associated pathogens such as Pseudomonas, Burkholderia, Stenotrophomonas and Achromobacter are less prevalent than core genera, but have a strong tendency to dominate the bacterial community when present. Community diversity and lung function are greatest in patients less than 10 years of age and lower in older age groups, plateauing at approximately age 25. Lower community diversity correlates with worse lung function in a multivariate regression model. Infection by Pseudomonas correlates with age-associated trends in community diversity and lung function.