SUMMARY: Computational gene function prediction can serve to focus experimental resources on high-priority experimental tasks. FuncBase is a web resource for viewing quantitative machine learning-based gene function annotations. Quantitative annotations of genes, including fungal and mammalian genes, with Gene Ontology terms are accompanied by a community feedback system. Evidence underlying function annotations is shown. For example, a custom Cytoscape viewer shows functional linkage graphs relevant to the gene or function of interest. FuncBase provides links to external resources, and may be accessed directly or via links from species-specific databases. AVAILABILITY: FuncBase as well as all underlying data and annotations are freely available via http://func.med.harvard.edu/

UNLABELLED: FuncAssociate is a web application that discovers properties enriched in lists of genes or proteins that emerge from large-scale experimentation. Here we describe an updated application with a new interface and several new features. For example, enrichment analysis can now be performed within multiple gene- and protein-naming systems. This feature avoids potentially serious translation artifacts to which other enrichment analysis strategies are subject. AVAILABILITY: The FuncAssociate web application is freely available to all users at http://llama.med.harvard.edu/funcassociate.

In many database applications involving string data, it is common to have near neighbor queries (asking for strings that are similar to a query string) or nearest neighbor queries (asking for strings that are most similar to a query string). The similarity between strings is defined in terms of a distance function determined by the application domain. The most popular string distance measures are based on (a weighted) count of (i) character edit or (ii) block edit operations to transform one string into the other. Examples include the Levenshtein edit distance and the recently introduced compression distance. The main goal is to develop efficient near(est) neighbor search tools that work for both character and block edit distances. Our premise is that distance-based indexing methods, which are originally designed for metric distances can be modified for string distance measures, provided that they form almost metrics. We show that several distance measures, such as the compression distance and weighted character edit distance are almost metrics. In order to analyze the performance of distance based indexing methods (in particular VP trees) for strings, we then develop a model based on distribution of pairwise distances. Based on this model we show how to modify VP trees to improve their performance on string data, providing tradeoffs between search time and space. We test our theoretical results on synthetic data sets and protein strings.

MOTIVATION: During the next phase of the Human Genome Project, research will focus on functional studies of attributing functions to genes, their regulatory elements, and other DNA sequences. To facilitate the use of genomic information in such studies, a new modeling perspective is needed to examine and study genome sequences in the context of many kinds of biological information. Pathways are the logical format for modeling and presenting such information in a manner that is familiar to biological researchers. RESULTS: In this paper we present an integrated system, called Pathways Database System, with a set of software tools for modeling, storing, analyzing, visualizing, and querying biological pathways data at different levels of genetic, molecular, biochemical and organismal detail. The novel features of the system include: (a) genomic information integrated with other biological data and presented from a pathway, rather than from the DNA sequence, perspective; (b) design for biologists who are possibly unfamiliar with genomics, but whose research is essential for annotating gene and genome sequences with biological functions; (c) database design, implementation and graphical tools which enable users to visualize pathways data in multiple abstraction levels, and to pose predetermined queries; and (d) an implementation that allows for web(XML)-based dissemination of query outputs (i.e. pathways data) to researchers in the community, giving them control on the use of pathways data. AVAILABILITY: Available on request from the authors.