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Bioinformatics Tool-Related Papers of Note, July 2006

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Babnigg G, Giometti CS. A database of unique protein sequence identifiers for proteome studies. [Proteomics. 2006 Jul 21; (e-pub ahead of print)]: Presents a database of unique protein sequence identifiers called Sequence Globally Unique Identifiers (SEGUID) derived from primary protein sequences. The identifiers “serve as a common link between multiple sequence databases and are resilient to annotation changes in either public or private databases throughout the lifetime of a given protein sequence,” according to the authors. Availability: http://bioinformatics.anl.gov/SEGUID/.
 

 
Das R, Dimitrova N, Xuan Z, et al. Computational prediction of methylation status in human genomic sequences. [Proc Natl Acad Sci USA. 2006 Jul 11;103(28):10713-6]: Introduces a computational pattern recognition method, called HDFinder, for predicting the methylation landscape of human brain DNA. The method can be applied both to CpG islands and to non-CpG island regions and has a prediction accuracy of 86 percent, according to the authors, who used the program to predict the genomic methylation patterns for all 22 human autosomes.
 

 
Herrgard MJ, Fong SS, Palsson BO. Identification of genome-scale metabolic network models using experimentally measured flux profiles: Introduces a method, called OMNI (optimal metabolic network identification), for determining the active reactions in a genome-scale metabolic network based on a limited number of experimentally measured fluxes. The method “results in the best agreement between in silico predicted and experimentally measured flux distributions” and has applications in metabolic engineering as well as the reconstruction of metabolic, according to the authors.
 

 
Jaffe JD, Mani DR, Leptos KC, Church GM, Gillette MA, Carr SA. PEPPeR: A platform for experimental proteomic pattern recognition. [Mol Cell Proteomics. 2006 Jul 19; (e-pub ahead of print)]: Describes new algorithms for quantitative proteomics. One, called Landmark Matching, performs time base-independent propagation of peptide identities onto accurate mass LC-MS features based on historical data from disparate data acquisition strategies. Another algorithm, called Peak Matching, builds upon Landmark Matching by recognizing identical molecular species across multiple LC-MS experiments by clustering. The authors have bundled these algorithms together with other algorithms, data acquisition strategies, and experimental designs to create PEPPer (platform for experimental proteomic pattern recognition).
 

 
Klekota J, Roth FP, Schreiber SL. Query Chem: a Google-powered web search combining text and chemical structures. [Bioinformatics 2006 22(13):1670-1673]: Describes Query Chem, a program that integrates chemical structure and text-based searching using publicly available chemical databases and Google's API. According to the authors, Query Chem enables users “to search the Web for information about chemical structures without knowing their common names or identifiers.” Availability: http://www.QueryChem.com.
 

 
Kolodny R, Honig B. VISTAL — a new 2D visualization tool of protein 3D structural alignments. [Bioinformatics. 2006 Jul 12; (e-pub ahead of print)]: Describes VISTAL, a tool for two-dimensional visualization of protein structural alignments. Availability: http://trantor.bioc.columbia.edu/~kolodny/software.html.
 

 
Ohlson T, Aggarwal V, Elofsson A, Maccallum RM. Improved alignment quality by combining evolutionary Information, predicted secondary structure and self-organizing maps: Describes the use of a self-organizing map to assign sequence profile windows to "structural states" of proteins and assess their use in sequence alignment.
 

 
Pelizzola M, Pavelka N, Foti M, Ricciardi-Castagnoli P. AMDA: an R package for the automated microarray data analysis. [BMC Bioinformatics. 2006 Jul 6;7(1):335]: Describes AMDA (automated microarray data analysis), an R software package for analyzing Affymetrix microarray experiments. AMDA integrates different functions available in the R and Bioconductor projects with newly developed functions. Availability: http://www.genopolis.it/.
 

 
Schmidt T, Frishman D. PROMPT: A protein mapping and comparison tool. [BMC Bioinformatics. 2006 Jul 4;7(1):331]: Discusses, PROMPT, a software package that “enables the user to compare arbitrary protein sequence sets, revealing statistically significant differences in their annotation features,” according to the paper’s abstract. PROMPT offers statistical procedures that enable users to compare the frequencies of categorical annotations between two sets; enrich nominal features in one set with respect to another one; compare numeric distributions; and correlate numeric variables. Availability: http://webclu.bio.wzw.tum.de/prompt/.
 

 
Steuer R, Gross T, Selbig J, Blasius B. Structural kinetic modeling of metabolic networks. [Proc Natl Acad Sci USA. 2006 Aug 8;103(32):11868-73]: Describes a method for providing a quantitative account of the dynamical capabilities of a metabolic system, without requiring any explicit information about the functional form of the rate equations. The approach constructs a local linear model at each point in parameter space, such that each element of the model is either directly experimentally accessible or amenable to a straightforward biochemical interpretation. “This ensemble of local linear models, encompassing all possible explicit kinetic models, then allows for a statistical exploration of the comprehensive parameter space,” the authors note.
 

 
van Dijk M, van Dijk AD, Hsu V, Boelens R, Bonvin AM. Information-driven protein–DNA docking using HADDOCK: it is a matter of flexibility. [Nucleic Acids Research 2006 34(11):3317-3325]: Describes an extension of HADDOCK (high ambiguity driven docking) to explicitly deal with DNA flexibility. HADDOCK “uses non-structural experimental data to drive the docking during a rigid-body energy minimization, and semi-flexible and water refinement stages,” the authors write in the paper’s abstract.
 

 
Wei C, Brent M. Using ESTs to improve the accuracy of de novo gene prediction. [BMC Bioinformatics 2006, 7:327]: Presents TWINSCAN_EST, a new system that combines EST alignments with the TWINSCAN gene-prediction program. For the C. elegans genome, TWINSCAN_EST showed a 14 percent improvement in sensitivity and a 13 percent improvement in specificity in predicting exact gene structures compared to TWINSCAN without EST alignments, according to the authors. Availability: http://genes.cse.wustl.edu/distribution/download_TS.html.

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