Blankenbecler R, et al. Matching protein structures with fuzzy alignments. [Proc. Natl. Acad. Sci. USA 100(21),11936¯11940]: Describes a structure-alignment method that uses a cost function containing both fuzzy (Potts) assignment variables and atomic coordinates. According to the authors, the approach performs well when compared with other methods and requires modest CPU consumption.
De Bono B, et al. Exegesis: a procedure to improve gene predictions and its use to find immunoglobulin superfamily proteins in the human and mouse genomes. [Nucleic Acids Research 31(21), 6096-6103]: Presents Exegesis, a procedure to refine gene predictions for complex genomes that uses the Genewise program along with experimentally derived sequences, experimental maps of gene segment libraries, and a new browser that allows the user to compare multiple gene maps to regions of genomic sequences.
Dror O, et al. Multiple structural alignment by secondary structures: Algorithm and applications. [Protein Sci. 12(11), 2492-2507]: Describes MASS (multiple alignment by secondary structures), a method for the structural alignment of multiple protein molecules and detection of common structural motifs that is based on a two-level alignment, using both secondary structure and atomic representation. Availability: http://bioinfo3d.cs.tau.ac.il/MASS/.
Jansen R, et al. A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data. [Science 302(17), 449-453]: Describes an approach using Bayesian networks to predict protein-protein interactions genome-wide in yeast that weights genomic features only weakly associated with interaction. The approach can also integrate noisy, experimental interaction data sets, according to the authors. Yeast interaction data available at http://genecensus.org/intint.
Kankainen M, et al. DANCER: a program for digital anatomical reconstruction of gene expression data. [Nucleic Acids Research 31(21) e132]: Discusses a digital anatomy construction (DANCER) program that was developed for gene expression data. DANCER can be used to reconstruct anatomical images from in situ hybridization images, microarray, or other gene expression data by filling in regions of a drawn figure with the corresponding values from a gene expression data set.
Keibler E, et al. Eval: A software package for analysis of genome annotations. [BMC Bioinformatics 2003 4:50]: Presents Eval, a tool for analyzing the performance of gene annotation systems that provides summaries and graphical distributions for many descriptive statistics about any set of annotations. Availability: http://genes.cse.wustl.edu/.
Meiler J, et al. Coupled prediction of protein secondary and tertiary structure. [Proc. Natl. Acad. Sci. USA 100 (21), 12105-12110]: An exploration of the extent to which non-local interactions in predicted tertiary structures can be used to improve secondary structure prediction. The authors extended the architecture of a neural network for secondary structure prediction that utilizes multiple sequence alignments to accept low-resolution non-local tertiary structure information as an additional input, and found that prediction accuracy was increased by 7-10 percent on average and by up to 35 percent in individual cases for independent test data.
Ortoleva P, et al. The Karyote Physico-Chemical Genomic, Proteomic, Metabolic Cell Modeling System. [Omics 7(3), 269-283]: Describes the Karyote cell-modeling system, which integrates three elements: a model-building and data archiving module; a genomic, proteomic, and metabolic cell simulator; and an information-theory module. Availability: http://lcg-ps.chem.indiana.edu/login.php.
Petrey D, et al. Using multiple structure alignments, fast model building, and energetic analysis in fold recognition and homology modeling. [Proteins 53(6), 430-5]: Describes a suite of protein structure prediction software tools including HMAP (Hybrid Multidimensional Alignment Profile), a profile-to-profile alignment method that is derived from sequence-enhanced multiple structure alignments in core regions; NEST, a fast model building program that applies an artificial-evolution algorithm to construct a model from a given template and alignment; and GRASP2, a structure and alignment visualization program.
Rocco D, et al. Automatic discovery and classification of bioinformatics Web sources. [Bioinformatics 19(15),1927-33]: Presents a system for finding classes of bioinformatics data sources and integrating them behind a unified interface. The system is based on a metadata description of the important features of an entire class of services without tying that description to any particular Web source.
Turner F, et al. POCUS: mining genomic sequence annotation to predict disease genes. [Genome Biology 2003 4:R75]: Describes POCUS (prioritization of candidate genes using statistics), a computational approach to prioritize candidate disease genes that is based on over-representation of functional annotation between loci for the same disease.
Vadigepalli R, et al. PAINT: A Promoter Analysis and Interaction Network Generation Tool for Gene Regulatory Network Identification. [Omics 7(3), 235-252]: Describes PAINT (Promoter Analysis and Interaction Network Tool), a software package that automates the promoter analysis of a given set of genes for the presence of transcription factor binding sites based on the coincidence of regulatory sites.
Vernikos G, et al. GeneViTo: Visualizing gene-product functional and structural features in genomic datasets. [BMC Bioinformatics 2003 4:53]: Presents a Java-based application that serves as a workbench for genome-wide analysis through visual interaction. Availability: http://bioinformatics.biol.uoa.gr/GENEVITO
Xu D, et al. EXCAVATOR: a computer program for efficiently mining gene expression data. [Nucleic Acids Res. 31(19):5582-9]: A computer package for clustering gene expression profiles based on a framework for representing gene expression data as a minimum spanning tree.
Zuyderduyn S, et al. A knowledge discovery object model API for Java. [BMC Bioinformatics 2003 4:51]: Presents KDOM (knowledge discovery object model), an API that provides a framework for developing a biological knowledge ontology for Java-based software projects. Availability: http://www.bcgsc.ca/software.