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

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Allen J, Salzberg S. JIGSAW: integration of multiple sources of evidence for gene prediction. [Bioinformatics 2005 21(18):3596-3603]: Describes JIGSAW, a new gene-finding system designed to automate the process of predicting gene structure from multiple sources of evidence, "with results that often match the performance of human curators," according to the authors. JIGSAW computes the relative weight of different lines of evidence using statistics generated from a training set, and then combines the evidence using dynamic programming. Availability: http://cbcb.umd.edu/software/jigsaw.


Antes I, Merkwirth C, Lengauer T. POEM: Parameter Optimization Using Ensemble Methods: Application to Target Specific Scoring Functions. [J Chem Inf Model. 2005 Sep-Oct;45(5):1291-302]: Describes a program called POEM (Parameter Optimization using Ensemble Methods) for aiding in the choice of parameters in computational biology processes such as docking, binding, and folding. POEM combines the DOE (Design of Experiment) procedure with ensembles of different regression methods. As test applications, the authors fitted the FlexX and Screenscore scoring functions to the kinase and ATPase protein classes, noting that "the results are promising: Starting from random parameters we are able to locate parameter sets which show superior performance compared to the original values."


Gott J, Parimi N, Bundschuh R. Discovery of new genes and deletion editing in Physarum mitochondria enabled by a novel algorithm for finding edited mRNAs. [Nucleic Acids Research 2005 33(16):5063-5072]: Introduces a new algorithm called Predictor of Insertional Editing (PIE), which can be used to locate genes whose mRNAs are subjected to multiple frame-shifting events. The authors have extended the algorithm to include probabilistic predictions for sites of nucleotide insertion, a feature that is "particularly useful when designing primers for sequencing edited RNAs," the authors write.


Liefeld T, Reich M, Gould J, Zhang P, Tamayo P, Mesirov J. GeneCruiser: a web service for the annotation of microarray data. [Bioinformatics 2005 21(18):3681-3682]: Describes GeneCruiser, a web service that allows users to annotate genomic data by mapping microarray feature identifiers to gene identifiers from databases. Genes are identified using the Life Sciences Identifier standard. Availability: as a web service and web application at http://www.genecruiser.org; and integrated into the GenePattern microarray analysis package at http://www.genepattern.org.


Novozhilov A, Karev G, Koonin E. Mathematical modeling of evolution of horizontally transferred genes [ArXiv pre-print archive: http://arXiv.org/abs/q-bio/0509010]: Describes a stochastic model of evolution of horizontally transferred genes in microbial populations that includes five parameters: the rate of mutational inactivation, selection coefficient, immigration rate (the rate of arrival of a novel sequence from outside of the recipient population), within-population horizontal transmission rate, and population size.


Pan X, Stein L, Brendel V. SynBrowse: a synteny browser for comparative sequence analysis [Bioinformatics 2005 21(17):3461-3468]: Describes SynBrowse, a synteny browser for visualizing and analyzing genome alignments both within and between species. SynBrowse is a module of the GBrowse (Generic Genome Browser). Availability: http://www.gmod.org.


Pizzi C, Bortoluzzi S, Bisognin A, Coppe S, Danieli G. Detecting seeded motifs in DNA sequences. [Nucleic Acids Research 2005 33(15):e135]: Presents a method for the detection of seeded DNA motifs, composed by regions with a different extent of variability. The method is based on a multi-step approach implemented in a motif searching web tool called MOST. "Experimental results on different yeast and human real datasets proved the methodology to be a promising solution for finding seeded motifs," the authors write. Availability: http://telethon.bio.unipd.it/bioinfo/MOST.


Riley R, Lee C, Sabatti C, Eisenberg D. Inferring protein domain interactions from databases of interacting proteins. [Genome Biology 2005, 6:R89]: Describes domain pair exclusion analysis (DPEA), a method for inferring domain interactions from databases of interacting proteins.


Scholtens D, Vidal M, Gentleman R. Local modeling of global interactome networks. [Bioinformatics 2005 21(17):3548-3557]: Discusses the application of local modeling to address four major issues pertaining to protein-protein networks. According to the authors, the method motivates "the need to move from static global interactome graphs to local protein complex models." Availability: The local modeling algorithm can be found in the R package apComplex at http://www.bioconductor.org.


Schreiber F, Schwöbbermeyer H. MAVisto: a tool for the exploration of network motifs. [Bioinformatics 2005 21(17):3572-3574]: Discussses MAVisto, a tool for analyzing and visualizing motifs in biological networks. Availability: http://mavisto.ipk-gatersleben.de/.


Shamir R, Maron-Katz A, Tanay A, Linhart C, Steinfeld I, Sharan R, Shiloh Y, Elkon R. EXPANDER — an integrative program suite for microarray data analysis. [BMC Bioinformatics 2005, 6:232]: Describes EXPANDER 2.0 (Expression Analyzer and Displayer), a software package for analyzing gene expression data, which implements various data analysis algorithms ranging from the initial steps of normalization and filtering, through clustering and biclustering, to high-level functional enrichment analysis that points to biological processes that are active in the examined conditions, and to promoter cis-regulatory elements analysis that elucidates transcription factors that control the observed transcriptional response. Availability: http://www.cs.tau.ac.il/~rshamir/expander.


Sharma-Oates A, Quirke P, Westhead D. TmaDB: a repository for tissue microarray data. [BMC Bioinformatics 2005, 6:218]: Describes a relational database (TmaDB) that has been developed to collate information relating to tissue microarrays, including microarray construction protocol, experimental protocol, and results from the various immunocytological and histochemical staining experiments including the scanned images for each of the tissue microarray cores. The database also contains pathological information associated with each of the specimens on the tissue microarray slide, the location of the various tissue microarrays, and the individual specimen blocks from which cores were taken in the laboratory and their current status.


Storey J, Xiao W, Leek J, Tompkins R, Davis R. Significance analysis of time course microarray experiments. [Proc Natl Acad Sci USA. 2005 Sep 6;102(36):12837-42]: Proposes a significance method for analyzing time course microarray studies that can be applied to "the typical types of comparisons and sampling schemes," according to the authors. Availability: http://faculty.washington.edu/jstorey/edge/index.php.


Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. [Proc Natl Acad Sci USA. 2005 Oct 25;102(43):15545-50]: Describes an analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method focuses on gene sets, or groups of genes that share common biological function, chromosomal location, or regulation. Availability: http://www.broad.mit.edu/gsea/.

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