Bioinformatics Tool-Related Papers of Note, September 2007

Beauheim CC, Wymore F, Nitzberg M, Zachariah ZK, Jin H, Skene JP, Ball CA, Sherlock G. OntologyWidget — a reusable, embeddable widget for easily locating ontology terms. [BMC Bioinformatics 2007, 8:338]: Introduces OntologyWidget, a tool that allows users to search for and browse biological ontology terms. OntologyWidget is written using AJAX (asynchronous JavaScript and XML) and includes two elements: a dynamic auto-complete ontology search feature and a generic and dynamic ontology browser. Availability: http://smd.stanford.edu/ontologyWidget

 

 

Chen JH, Linstead E, Swamidass SJ, Wang D, Baldi P. ChemDB update — full-text search and virtual chemical space. [Bioinformatics 2007 23(17):2348-2351]: Presents ChemDB, a database containing nearly 5 million commercially available small molecules. Recent improvements include optimization of chemical structure retrieval algorithms, which enable full database searches in less than a second, according to the paper’s abstract. Availability: http://cdb.ics.uci.edu 

 

 

Faith JJ, Olson AJ, Gardner TS, Sachidanandam R. Lightweight Genome Viewer: portable software for browsing genomics data in its chromosomal context. [BMC Bioinformatics 2007, 8:344]: Introduces lwgv, or lightweight genome viewer, a web-based tool for visualizing sequence annotations in their chromosomal context that “simplifies the visualization of user-generated results on a local computer” and provides a “lightweight alternative to large genome browsers.” Availability: http://lwgv.sourceforge.net/

 

 

Gao Y, Douguet D, Tovchigrechko A, Vakser IA. DOCKGROUND system of databases for protein recognition studies: Unbound structures for docking. [Proteins. 2007 Sep 5; (e-pub ahead of print)]: Describes an improved version of the DOCKGROUND database of co-crystallized protein-protein complexes. The current release includes new datasets: an automatically generated nonredundant set, and a manually curated set that includes only biological nonobligate complexes along with a number of additional useful characteristics. Availability: http://dockground.bioinformatics.ku.edu/

 

 

Huang J, Gusnanto A, O'Sullivan K, Staaf J, Borg A, Pawitan Y. Robust smooth segmentation approach for array CGH data analysis. [Bioinformatics 2007 23(18):2463-2469]: Describes a “smooth segmentation” approach for analyzing array comparative genomic hybridization data. According to the paper’s abstract, “existing segmentation approaches for analyzing aCGH data are based on modeling data as a series of discrete segments with unknown boundaries and unknown heights,” but “in reality a variety of biological and experimental factors can cause the signal to deviate from a stepwise function.” The so-called “smoothseg” approach uses a doubly heavy-tailed random-effect model to first deal with outliers in the observations, and then to deal with possible jumps in the underlying pattern associated with different segments. Availability: http://www.meb.ki.se/~yudpaw 

 

 

Morris JH, Huang CC, Babbitt PC, Ferrin TE. structureViz: linking Cytoscape and UCSF Chimera. [Bioinformatics 2007 23(17):2345-2347]: Introduces structureViz, a Cytoscape plug-in that links the visualization of biological networks with the visualization and analysis of macromolecular structures and sequences provided by UCSF Chimera. According to the paper’s abstract, when combined with Cytoscape and Chimera, structureViz is “the first tool that links these two critical aspects of computational analysis in a straightforward manner.” Availability: http://www.cgl.ucsf.edu/Research/cytoscape/structureViz/

 

 

Post LJ, Roos M, Marshall MS, Driel RV, Breit TM. A semantic web approach applied to integrative bioinformatics experimentation: a biological use case with genomics data. [Bioinformatics. 2007 Sep 19; (e-pub ahead of print)]: Presents SWEDI, or semantic web-enabled data integration, an approach that aims to “formalize biological domains by capturing the knowledge in semantic models using ontologies as controlled vocabularies,” according to the paper’s abstract. The paper outlines a biological use case that shows “high startup costs for the SWEDI approach, but straightforward extension with similar data.” Availability: http://www.integrativebioinformatics.nl/swedi/index.html

 

 

Prliæ A, Down TA, Kulesha E, Finn RD, Kähäri A, Hubbard TJ. Integrating sequence and structural biology with DAS. [BMC Bioinformatics 2007, 8:333]: Describes several extensions to the Distributed Annotation System, a network protocol for sharing annotations of genomes and protein sequence. The extensions provide new commands to share alignments and three dimensional molecular structure data, add the possibility for registration and discovery of DAS servers, and provide a convention for providing different types of data plots. Availability: http://www.dasregistry.org