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Bioinformatics Tool-Related Papers of Note, October 2006

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Alibes A, Morrissey ER, Canada A, Rueda OM, Casado D, Yankilevich P, Diaz-Uriarte R. Asterias: a parallelized web-based suite for the analysis of expression and aCGH data. [ArXiv preprint archive: http://arXiv.org/abs/q-bio/0610039]: Discusses Asterias, a collection of freely accessible web tools for analyzing gene expression and array-CGH data. Availability: http://www.asterias.info/.
 

 
Cheung TH, Kwan YL, Hamady M, Liu X. Unraveling transcriptional control and cis-regulatory codes using the software suite GENEACT. [Genome Biol. 2006 Oct 25;7(10):R97]: Presents a suite of web-based bioinformatics tools, called GeneACT, that can rapidly detect evolutionarily conserved transcription factor binding sites or microRNA target sites that are either unique or overrepresented in differentially expressed genes from DNA microarray data. Availability: http://promoter.colorado.edu/.
 

 
Chivian D, Baker D. Homology modeling using parametric alignment ensemble generation with consensus and energy-based model selection. [Nucleic Acids Research 2006 34(17):e112]: Introduces an approach for sequence-to-structure alignment called K*Sync, in which alignments are generated by dynamic programming using a scoring function that combines information on many protein features, including “a novel measure of how obligate a sequence region is to the protein fold,” the authors wrote. According to performance benchmarks, the approach, which is the foundation for the homology modeling module in the Rosetta server, is “effective at both generating and selecting accurate alignments.”
 

 
Descorps-Declere S, Ziebelin D, Rechenmann F, Viari A. Genepi: a blackboard framework for genome annotation. [BMC Bioinformatics. 2006 Oct 12;7(1):450]: Discusses Genepi, a “blackboard framework” for developing automatic annotation systems that is not bound to any specific annotation strategy. The user specifies a blackboard structure in a configuration file and the system will run this particular annotation strategy, according to the authors.
 

 
Goh CS, Gianoulis TA, Liu Y, Li J, Paccanaro A, Lussier YA, Gerstein M. Integration of curated databases to identify genotype-phenotype associations. [BMC Genomics. 2006 Oct 12;7(1):257]: Describes an approach to discover genotype-phenotype associations that combines phenotypic information from the GIDEON biomedical informatics database with the molecular information contained in NCBI’s COGs database.
 

 
Guo J, Lin Y, Liu X. GNBSL: A new integrative system to predict the subcellular location for Gram-negative bacteria proteins. [Proteomics. 2006 Oct;6(19):5099-105]: Presents a system called GNBSL (gram-negative bacteria subcellular localization) that generates a position-specific frequency matrix and a position-specific scoring matrix for protein sequences in Swiss-Prot. Availability: http://166.111.24.5/webtools/GNBSL/index.htm.
 

 
Hu G, Wang HY, Greenawalt DM, Azaro MA, Luo M, Tereshchenko IV, Cui X, Yang Q, Gao R, Shen L, Li H. AccuTyping: new algorithms for automated analysis of data from high-throughput genotyping with oligonucleotide microarrays. [Nucleic Acids Research 2006 34(17):e116]: Presents new algorithms for SNP microarray data analysis and a software package based on the algorithms called AccuTyping. The algorithms take advantage of the fact that the top and bottom 20 percent of SNPs can be “safely treated as homozygous after sorting based on their ratios between the signal intensities,” the authors wrote. These SNPs are then used as controls for color channel normalization and background subtraction. Availability: http://www2.umdnj.edu/lilabweb/publications/AccuTyping.html.
 

 
Jensen ST, Chen G, Stoeckert CJ. Bayesian Variable Selection and Data Integration for Biological Regulatory Networks. [ArXiv pre-print archive: http://arXiv.org/abs/math/0610034]: Describes a Bayesian hierarchical model that integrates gene expression data, chromatin immunoprecipitation binding data, and promoter sequence data in a principled variable selection framework. According to the authors, when the method was used to infer regulatory relationships in yeast, it resulted in “greater biological relevance on the external validation measures than previous data integration methods.”
 

 
Morisawa H, Hirota M, Toda T. Development of an open source laboratory information management system for 2-D gel electrophoresis-based proteomics workflow. [BMC Bioinformatics. 2006 Oct 4;7(1):430]: Describes an open source laboratory information management system that is customized for 2D gel electrophoresis-based proteomics. The LIMS is equipped with the same input interface for 2D gel information as a clickable map on public 2DPAGE databases.
 

 
Noguchi H, Park J, Takagi T. MetaGene: prokaryotic gene finding from environmental genome shotgun sequences. [Nucleic Acids Res. 2006;34(19):5623-30]: Describes a prokaryotic gene-finding program for metagenomics data called MetaGene, which uses di-codon frequencies estimated by the GC content of a given sequence with other various measures. According to the authors, MetaGene can predict a whole range of prokaryotic genes based on the anonymous genomic sequences of a few hundred bases, with a sensitivity of 95 percent and a specificity of 90 percent for artificial shotgun sequences.
 

 
Robertson TA, Varani G. An all-atom, distance-dependent scoring function for the prediction of protein-DNA interactions from structure. [Proteins. 2006 Oct 31 (e-pub ahead of print)]: Describes an all-atom statistical potential function for the prediction of protein-DNA interactions from their structures. The authors claim that this method outperforms similar, lower-resolution statistical potentials in a series of decoy discrimination experiments. On average, the method is able to identify 90 percent of near-native docking decoys within the best-scoring 10 percent of structures in a given decoy set.
 

 
Sealfon RS, Hibbs MA, Huttenhower C, Myers CL, Troyanskaya OG. GOLEM: an interactive graph-based gene-ontology navigation and analysis tool. [BMC Bioinformatics 2006, 7:443]: Introduces GOLEM (Gene Ontology Local Exploration Map), a visualization and analysis tool for exploring the Gene Ontology graph. GOLEM allows users to dynamically expand and focus the local graph structure of the gene ontology hierarchy in the neighborhood of any chosen term. Availability: http://function.princeton.edu/GOLEM.
 

 
Wang Y, Xue Z, Xu J. Better prediction of the location of alpha-turns in proteins with support vector machine. [Proteins. 2006 Oct 1;65(1):49-54]: Introduces AlphaTurn, a method that uses a support vector machine to predict alpha-turns in proteins. Availability: http://bmc.hust.edu.cn/bioinformatics/.
 

 
Weise S, Grosse I, Klukas C, Koschutzki D, Scholz U, Schreiber F, Junker BH. Meta-All: a system for managing metabolic pathway information. [BMC Bioinformatics 2006, 7:465]: Introduces Meta-All, an information system for managing metabolic pathways, including reaction kinetics, detailed locations, environmental factors and taxonomic information. Data can be stored together with quality tags and in different parallel versions. Availability: http://bic-gh.de/meta-all.
 

 
Yu H,Gerstein M. Genomic analysis of the hierarchical structure of regulatory networks. [Proc Natl Acad Sci USA. 2006 Oct 3;103(40):14724-31]:Describes algorithms for identifying generalized hierarchies in the regulatory networks of Escherichia coli and Saccharomyces cerevisiae. Analysis reveals a few “master” transcription factors that receive most of the input for the whole regulatory hierarchy through protein interactions. While these transcription factors have “maximal influence” over other genes, in terms of affecting expression-level changes, those at the bottom of the regulatory hierarchy were found to be “more essential to the viability of the cell,” according to the authors.
 

 
Zhao Q, Stoyanova R, Du S, Sajda P, Brown TR. HiRes—a tool for comprehensive assessment and interpretation of metabolomic data. [Bioinformatics 2006 22(20):2562-2564]: Describes HiRes, a metabolomics software package that combines standard NMR spectral processing functionalities with techniques for multi-spectral dataset analysis, such as principal component analysis and non-negative matrix factorization. Availability: http://hatch.cpmc.columbia.edu/highresmrs.html.

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