KCaM (KEGG Carbohydrate Matcher) is a tool for the analysis of carbohydrate sugar chains, or glycans. It consists of a web-based graphical user interface that allows users to enter glycans easily with the mouse. The glycan structure is then transformed into our KCF (KEGG Chemical Function) file format and sent to our program which implements an efficient tree-structure alignment algorithm, similar to sequence alignment algorithms but for branched tree structures. Users can also retrieve glycan tree structures in KCF format from their local computers for visualization over the web. The tree-matching algorithm provides several options for performing different types of tree-matching procedures on glycans. These options consist of whether to incorporate gaps in a match, whether to take the linkage information into consideration and local versus global alignment. The results of this program are returned as a list of glycan structures in order of similarity based on these options. The actual alignment can be viewed graphically, and the annotation information can also be viewed easily since all this information is linked with KEGG's comprehensive suite of genomic data. Analogously to BLAST, users are thus able to compare glycan structures of interest with glycans from different glycan databases using a variety of tree-alignment options. KCaM is currently available at http://glycan.genome.ad.jp.
BackgroundBiological databases vary enormously in size and data complexity, from small databases that contain a few million Resource Description Framework (RDF) triples to large databases that contain billions of triples. In this paper, we evaluate whether RDF native stores can be used to meet the needs of a biological database provider. Prior evaluations have used synthetic data with a limited database size. For example, the largest BSBM benchmark uses 1 billion synthetic e-commerce knowledge RDF triples on a single node. However, real world biological data differs from the simple synthetic data much. It is difficult to determine whether the synthetic e-commerce data is efficient enough to represent biological databases. Therefore, for this evaluation, we used five real data sets from biological databases.ResultsWe evaluated five triple stores, 4store, Bigdata, Mulgara, Virtuoso, and OWLIM-SE, with five biological data sets, Cell Cycle Ontology, Allie, PDBj, UniProt, and DDBJ, ranging in size from approximately 10 million to 8 billion triples.For each database, we loaded all the data into our single node and prepared the database for use in a classical data warehouse scenario. Then, we ran a series of SPARQL queries against each endpoint and recorded the execution time and the accuracy of the query response.ConclusionsOur paper shows that with appropriate configuration Virtuoso and OWLIM-SE can satisfy the basic requirements to load and query biological data less than 8 billion or so on a single node, for the simultaneous access of 64 clients.OWLIM-SE performs best for databases with approximately 11 million triples; For data sets that contain 94 million and 590 million triples, OWLIM-SE and Virtuoso perform best. They do not show overwhelming advantage over each other; For data over 4 billion Virtuoso works best.4store performs well on small data sets with limited features when the number of triples is less than 100 million, and our test shows its scalability is poor; Bigdata demonstrates average performance and is a good open source triple store for middle-sized (500 million or so) data set; Mulgara shows a little of fragility.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.