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In Print: Bioinformatics Tool-Related Papers of Note, November 2003

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Bray N, et al. MAVID: Constrained ancestral alignment of multiple sequences. [arXiv pre-print archive (http://arXiv.org/abs/q-bio/0311018)]: Describes a global multiple alignment program that incorporates maximum-likelihood inference of ancestral sequences; automatic guide-tree construction; protein-based anchoring of ab initio gene predictions; and constraints derived from a global homology map of the sequences. According to the authors, MAVID is able to accurately align multiple genomic regions up to several megabases long.


Claudel-Renard C, et al. Enzyme-specific profiles for genome annotation: PRIAM. [Nucleic Acids Research 31(22): 6633-6639]: Describes PRIAM (Profils pour l’Identification Automatique du Métabolisme), a method for automated enzyme detection and metabolic pathway reconstruction in fully sequenced genomes, based on the classification of enzymes in the Enzyme database. Availability: http://genopole.toulouse.inra.fr/bioinfo/priam/.


Djordjevic M, et al. A biophysical approach to transcription factor binding site discovery. [Genome Research 13(11): 2381-90]: Discusses a bioinformatics method that classifies potential binding sites based on the estimate of sequence-specific binding energy of a given transcription factor. The method also estimates the chemical potential of the factor that defines the threshold of binding. According to the authors, the approach provides “a significant improvement in the number of expected false positives, particularly in the ubiquitous case of low-specificity factors.”


Drabenstot S, et al. FELINES: a utility for extracting and examining EST-defined introns and exons. [Nucleic Acids Research 31(22):e141]: Introduces FELINES (Finding and Examining Lots of Intron ‘N’ Exon Sequences), a utility for automating the construction and analysis of intron and exon sequence databases produced from EST-to-genomic sequence alignments. Availability: http://www.genome.ou.edu/informatics.html.


Liu L, et al. Robust singular value decomposition analysis of microarray data. [Proc. Natl. Acad. Sci. USA 100(23) 13167-13172]: Describes a combination of mathematical and statistical methods to progressively take a microarray data set apart so that different aspects can be examined for both general patterns and very specific effects.


Martínez-Cruz L, et al. GARBAN: genomic analysis and rapid biological annotation of cDNA microarray and proteomic data. [Bioinformatics 19(16): 2158-2160]: Presents GARBAN (Genomic Analysis and Rapid Biological Annotation), an integrated framework for analyzing and comparing multiple data sets from microarray or proteomic experiments. Availability: http://garban.tecnun.es.


Matthiessen M. BioWareDB: the biomedical software and database search engine. [Bioinformatics 19(17): 2319-20]: Describes a catalog and search engine for bioinformatics software and databases, which currently “live a shadow existence compared to experimental results and methods that are widely published in journals and relatively easily found through publication databases such as PubMed,” according to the authors. BioWareDB currently contains 2,800 entries. Availability: http://www.biowaredb.org/.


Nikitin, A, et al. Pathway studio — the analysis and navigation of molecular networks. [Bioinformatics 19(16): 2155-2157]: Describes PathwayAssist, a software application for navigating and analyzing biological pathways, gene regulation networks, and protein interaction maps. Availability: www.ariadnegenomics.com/downloads/.


Romualdi C, et al. Improved detection of differentially expressed genes in microarray experiments through multiple scanning and image integration. [Nucleic Acids Research 31(23) e149]: Describes software that creates a virtual image to integrate microarray images and statistically summarize a series of consecutive scans of a microarray. According to the authors, the increase in the final number of differentially expressed genes detected with this approach is 50 percent more for microarrays hybridized with targets labeled by reverse transcriptase, and 200 percent more for microarrays developed with the tyramide signal amplification technique.


Thareau T, et al. Automatic design of gene-specific sequence tags for genome-wide functional studies. [Bioinformatics 19(17): 2191-2198]: Describes SPADS (Specific Primer and Amplicon Design Software), which designs gene-specific sequence tags by comparing divergent regions in each gene with a completely annotated genome sequence and selecting optimal primer pairs for the PCR amplification of one divergent region per gene. Availability: http://genoplante-info.infobiogen.fr/spads.


Wang J, et al. MGraph: graphical models for microarray data analysis. [Bioinformatics 19(17): 2210-2211]: Introduces Mgraph, a Matlab toolbox that uses graphical models to formulate and solve problems in microarray data analysis. Availability: http://www.uio.no/ ~junbaiw/mgraph/mgraph.html.


Zhang Y, et al. PCAS — a precomputed proteome annotation database resource. [BMC Genomics 4:42]: Reports the development of PCAS (ProteinCentric Annotation System), an online resource for pre-computed proteome annotation data. Availability: http://pak.cbi.pku.edu.cn/proteome/gca.php.

 

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