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In Print: Bioinformatics Tool-Related Papers of Note, November 2005

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Bertsch B, Ogden CA, Sidhu K, Le-Niculescu H, Kuczenski R, Niculescu AB. Convergent functional genomics: A Bayesian candidate gene identification approach for complex disorders. [Methods. 2005 Nov;37(3):274-9]: Presents an approach called Convergent Functional Genomics, which cross-matches animal model microarray gene expression data with human genetic linkage data, human postmortem brain data, and biological role data. The approach produces a short list of high-probability candidate genes, according to the authors.


Chen J, Swamidass J, Dou Y, Bruand J, Baldi P. ChemDB: a public database of small molecules and related chemoinformatics resources. [Bioinformatics 2005 21(22):4133-4139]: Describes ChemDB, a public database of small molecules built using the digital catalogs of more than a hundred vendors and other public sources. The current version of the database contains around 4.1 million commercially available compounds and 8.2 million counting isomers. Availability: http://cdb.ics.uci.edu.


Guda C, Subramaniam S. pTARGET: a new method for predicting protein subcellular localization in eukaryotes. [Bioinformatics 2005 21(21):3963-3969]: Introduces pTARGET, a software package that can predict proteins targeted to nine different subcellular locations in the eukaryotic animal species, including cytoplasm, endoplasmic reticulum, extracellular/secretory, golgi, lysosomes, mitochondria, nucleus, plasma membrane, and peroxisomes. According to the authors, the method can predict 68 pecent to 87 percent of the true positives at accuracy rates of 96 percent to 99 percent. Availability: http://bioinformatics.albany.edu/~ptarget.


Hwang D, Rust AG, Ramsey S, Smith JJ, Leslie DM, Weston AD, de Atauri P, Aitchison JD, Hood L, Siegel AF, Bolouri H. A data integration methodology for systems biology. [Proc Natl Acad Sci USA. 2005 Nov 29;102(48):17296-301]: Describes data-integration methods "that can handle multiple data sets differing in statistical power, type, size, and network coverage without requiring a curated training data set," according to the authors. Availability: http://labs.systemsbiology.net/bolouri/software/Pointillist/.


Li X, Zhong S, Wong W. Reliable prediction of transcription factor binding sites by phylogenetic verification. [Proc Natl Acad Sci USA. 2005 Nov 22;102(47):16945-50]: Describes a statistical methodology for predicting transcription factor binding sites in eukaryote genomes. This method models the cross-species conservation of binding sites without relying on accurate sequence alignment, according to the authors, and can be combined with any motif-finding algorithm that searches for overrepresented sequence motifs in individual species.


Mayer K, McCorkle S, Shanklin J. Linking enzyme sequence to function using conserved property difference locator to identify and annotate positions likely to control specific functionality. [BMC Bioinformatics 2005, 6:284]: Describes a method for identifying residues likely to determine class-specific functionality in which multiple sequence alignments are converted to an annotated graphical form by the Conserved Property Difference Locator program. According to the authors, results suggest that CPDL "will have broad utility for the identification of potential class-determining residues based on multiple sequence analysis of groups of paralogous proteins" and that it is "well suited for designing structure-function experiments to investigate membrane and soluble proteins."


Ressom HW, Varghese RS, Abdel-Hamid M, Eissa SA, Saha D, Goldman L, Petricoin EF, Conrads TP, Veenstra TD, Loffredo CA, Goldman R. Analysis of mass spectral serum profiles for biomarker selection. [Bioinformatics 2005 21(21):4039-4045]: Presents low-level methods for the processing of mass spectral data and a machine learning method that combines support vector machines with particle swarm optimization for biomarker selection. Availability: Matlab scripts at http://lombardi.georgetown.edu/labpage.


Teusink B, van Enckevort FH, Francke C, Wiersma A, Wegkamp A, Smid EJ, Siezen RJ. In silico reconstruction of the metabolic pathways of Lactobacillus plantarum: comparing predictions of nutrient requirements with those from growth experiments. [Appl Environ Microbiol. 2005 Nov;71(11):7253-62]: Describes a project to reconstruct the metabolic pathways of the lactic acid bacterium Lactobacillus plantarum WCFS1 and to create LacplantCyc, a manually curated pathway-genome database. Availability: http://www.lacplantcyc.nl.


Yu X, Cao J, Cai Y, Shi T, Li Y. Predicting rRNA-, RNA-, and DNA-binding proteins from primary structure with support vector machines. [J Theor Biol. 2005 Nov 4 (e-pub ahead of print)]: Describes an effort to integrate support vector machines, protein sequence amino acid composition, and associated physicochemical properties to predict nucleic-acid-binding proteins. Test results show that the accuracies of rRNA-, RNA-, DNA-binding SVM predictions were approximately 84 percent, 78 percent, and 72 percent, respectively. Availability: from the author upon request.

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