Gene ontology (GO) is a large public database which not only provides a set of controlled biological and biochemical vocabularies (terms) to describe gene products based upon their functions in the cell, but also contains gene annotation data from heterogeneous data sources. In this paper, using a new method to measure the semantic similarity of GO terms, an efficient algorithm is proposed to find genes that have similar biological functions with a given gene. An online tool is then implemented to search the top N genes having similar biological functions with a particular gene within the same or cross different species. Furthermore, various performance enhancement techniques are utilized to reduce the user query response time of the online tool. This tool is available at: http://bioinformatics.cletnson.edu/G-SESAME/Programs/geneTopl.php.

MOTIVATION: Although controlled biochemical or biological vocabularies, such as Gene Ontology (GO) (http://www.geneontology.org), address the need for consistent descriptions of genes in different data sources, there is still no effective method to determine the functional similarities of genes based on gene annotation information from heterogeneous data sources. RESULTS: To address this critical need, we proposed a novel method to encode a GO term's semantics (biological meanings) into a numeric value by aggregating the semantic contributions of their ancestor terms (including this specific term) in the GO graph and, in turn, designed an algorithm to measure the semantic similarity of GO terms. Based on the semantic similarities of GO terms used for gene annotation, we designed a new algorithm to measure the functional similarity of genes. The results of using our algorithm to measure the functional similarities of genes in pathways retrieved from the saccharomyces genome database (SGD), and the outcomes of clustering these genes based on the similarity values obtained by our algorithm are shown to be consistent with human perspectives. Furthermore, we developed a set of online tools for gene similarity measurement and knowledge discovery. AVAILABILITY: The online tools are available at: http://bioinformatics.clemson.edu/G-SESAME. SUPPLEMENTARY INFORMATION: http://bioinformatics.clemson.edu/Publication/Supplement/gsp.htm.

Recent studies have shown that a non-negligible number of packets face multiple congested links on Internet paths. We investigate the impact of using multiple active queue management (AQM) routers on paths that consist of multiple congested links. We present the results of an ns-2 evaluation study of various AQM techniques that, in contrast to previous studies of AQM, uses a complex network topology including up to three congested links, reverse path traffic, and realistic round-trip times. We consider the effects of multiple AQM routers coupled with explicit congestion notification (ECN) on the throughput of long-lived flows and response times of web traffic. We find that, especially for web traffic, performance is improved as the number of AQM routers is increased, but that significant improvements occur with even a single AQM router.

We propose a wireless network cache model to facilitate the cooperative proxy caching in wireless base stations. A self-configured and adaptive virtual proxy graph (VPG) is used to achieve efficient data search, data cache and data replication. Based on demand, the aggregate effect of data caching, searching and replicating actions by individual proxies automatically distributes cached Web documents closer to the interested mobile clients. Our performance study demonstrates that the proposed individual-based wireless proxy caching scheme outperforms existing caching schemes in terms of different performance metrics.

BACKGROUND: Peach is being developed as a model organism for Rosaceae, an economically important family that includes fruits and ornamental plants such as apple, pear, strawberry, cherry, almond and rose. The genomics and genetics data of peach can play a significant role in the gene discovery and the genetic understanding of related species. The effective utilization of these peach resources, however, requires the development of an integrated and centralized database with associated analysis tools. DESCRIPTION: The Genome Database for Rosaceae (GDR) is a curated and integrated web-based relational database. GDR contains comprehensive data of the genetically anchored peach physical map, an annotated peach EST database, Rosaceae maps and markers and all publicly available Rosaceae sequences. Annotations of ESTs include contig assembly, putative function, simple sequence repeats, and anchored position to the peach physical map where applicable. Our integrated map viewer provides graphical interface to the genetic, transcriptome and physical mapping information. ESTs, BACs and markers can be queried by various categories and the search result sites are linked to the integrated map viewer or to the WebFPC physical map sites. In addition to browsing and querying the database, users can compare their sequences with the annotated GDR sequences via a dedicated sequence similarity server running either the BLAST or FASTA algorithm. To demonstrate the utility of the integrated and fully annotated database and analysis tools, we describe a case study where we anchored Rosaceae sequences to the peach physical and genetic map by sequence similarity. CONCLUSIONS: The GDR has been initiated to meet the major deficiency in Rosaceae genomics and genetics research, namely a centralized web database and bioinformatics tools for data storage, analysis and exchange. GDR can be accessed at http://www.genome.clemson.edu/gdr/.