Cagney G, et al. In silico proteome analysis to facilitate proteomics experiments using mass spectrometry. [Proteome Science 2003 1:5]: Describes an application, proteogest, that generates descriptive and statistical analyses of the biophysical properties of multiple protein sequences and carries out in silico proteolytic digestion of the submitted proteomes, and the distribution of biophysical properties of the resulting peptides.
Cannon S, et al. DiagHunter and GenoPix2D: programs for genomic comparisons, large-scale homology discovery and visualization. [Genome Biology 2003 4:R68]: Introduces DiagHunter, which identifies homologous regions within or between genomes, and GenoPix2D, which displays synteny blocks and other genomic features.
Del Val C, et al. cDNA2Genome: a tool for mapping and annotating cDNAs. [BMC Bioinformatics. 2003 4:39]: Describes an application that uses annotation data, EST and mRNA databases, and gene prediction approaches to assess cDNA exon-intron structure. Availability: http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar/.
Ding Y, et al. A simplified approach for analysis of SELDI-TOF mass spectrometry data. [Applied Genomics and Proteomics 2003: 2(2)71-77]: Demonstrates a simplified approach to evaluating surface-enhanced laser desorption ionization time of flight mass spectrometry data using three statistical tools and a non-iterative supervised algorithm for classification.
Dwyer M, et al. Computational design of a Zn2+ receptor that controls bacterial gene expression. [Proc. Natl. Acad. Sci. USA 100(20), 11255-11260]: Describes a synthetic bacterial signal transduction pathway in which gene expression is controlled by extracellular Zn2+.
Kelley B, et al. Conserved pathways within bacteria and yeast as revealed by global protein network alignment. [Proc. Natl. Acad. Sci.100(20), 11394-11399]: Describes an algorithm called PathBlast that can align two protein-protein interaction networks and use interaction topology and protein sequence similarity to identify conserved interaction pathways and complexes. Availability: http://www.pathblast.org/.
Kent J, et al. Evolution’s cauldron: Duplication, deletion, and rearrangement in the mouse and human genomes. [Proc. Natl. Acad. Sci. USA 100(20), 11484-11489]: A comparison of the mouse and human genomes that studies genomic duplications, deletions, and rearrangements. New alignment techniques that can handle large gaps and discriminate between orthologous and paralogous alignments were developed in order to conduct the analysis.
Lemon W, et al. A high performance test of differential gene expression for oligonucleotide arrays. [Genome Biology 2003 4:R67]: Describes Logit-t, a logit-transformation for normalization followed by statistical testing at the probe level for microarray data that shows improved positive-predictive values and sensitivity over Affymetrix Microarray Suite 5, dChip, and Robust Multi-array Analysis.
Lundgren DH, et al. PROTEOME-3D: An interactive bioinformatics tool for large-scale data exploration and knowledge discovery. [Mol Cell Proteomics. 2003 Sep 7 (epub ahead of print)]: Describes a software platform that provides a queryable database of identified proteins from published literature; graphical tools for displaying proteome landscapes and trends from multiple large-scale experiments; and interactive data analysis.
Tjandra D, et al. An XML message broker framework for exchange and integration of microarray data. [Bioinformatics 2003 19(14), 1844-1845]: Presents an information framework based on the Microarray Gene Expression Markup Language (MAGE-ML) specification to identify genomic and imaging markers for diagnosis of breast cancer. Availability: http://sourceforge.net/projects/microsoap/.
Wang J, et al. Soap-HT-BLAST: high throughput BLAST based on Web services. [Bioinformatics 2003 19(14), 1863-1864]: A system based on Web services that allows users to perform multiple Blast queries at one run in a distributed, parallel environment through the Internet. Availability: http://mammoth.bii.a-star.edu.sg/webservices/htblast/index.html.
Wang L, et al. Haplotype inference by maximum parsimony. [Bioinformatics 2003 19(14), 1773-1780]: Describes an algorithm for haplotype inference that finds a set of the minimum number of haplotypes that explains the genotype samples. Availability: upon request ([email protected]).