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In Print: Bioinformatics Tool-Related Papers of Note, January 2006

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Mao F, Su Z, Olman V, Dam P, Liu Z, Xu Y. Mapping of orthologous genes in the context of biological pathways: An application of integer programming. [Proc Natl Acad Sci USA. 2006 Jan 3;103(1):129-34]: Describes an algorithm for pathway mapping across microbial genomes that takes into account both sequence similarity and genomic structure information such as operons and regulons. The authors have developed an integer-programming algorithm and implemented it as a computer software program, P-MAP. Availability: http://csbl.bmb.uga.edu/pmap2/


Matukumalli LK, Grefenstette JJ, Hyten DL, Choi IY, Cregan PB, Van Tassell CP. Application of machine learning in SNP discovery. [BMC Bioinformatics. 2006 Jan 6;7(1):4]: Describes a machine learning method to augment the polymorphism detection software packages PolyBayes and PolyPhred, which give high false positive SNP predictions. The study results indicate that a trained machine learning classifier can achieve 5-10 fold enhanced productivity.


Nye T, Liò P, Gilks W. A novel algorithm and web-based tool for comparing two alternative phylogenetic trees. [Bioinformatics 2006 22(1):117-119]: Presents an algorithm and software tool for comparing alternative phylogenetic trees obtained using different phylogenetic methods for some fixed set of species or obtained using different gene sequences from those species. Availability: http://www.mrc-bsu.cam.ac.uk/personal/thomas/phylo_comparison/comparison_page.html.


Phillips J, Chilukuri R, Fragoso G, Warzel D, Covitz PA. The caCORE Software Development Kit: Streamlining construction of interoperable biomedical information services. [BMC Med Inform Decis Mak. 2006 Jan 6;6(1):2]: Discusses the National Cancer Institute's cancer common ontologic representation environment (caCORE) and caCORE Software Development Kit (SDK). According to the authors, caCORE SDK "substantially lowers the barrier to implementing systems that are syntactically and semantically interoperable by providing workflow and automation tools that standardize and expedite modeling, development, and deployment."


Rabbee N, Speed T. A genotype-calling algorithm for Affymetrix SNP arrays. [Bioinformatics 2006 22(1):7-12]: Presents a classification algorithm, based on a multi-chip, multi-SNP approach, for calling genotypes on Affymetrix SNP arrays. The method, called RLMM, uses a supervised learning algorithm to obtain more accurate classification results on new data and uses the Mahalanobis distance for classification. Availability: implemented in R and available from Bioconductor (http://www.bioconductor.org/).


Shemesh R, Novik A, Edelheit S, Sorek R. Genomic fossils as a snapshot of the human transcriptome. [Proc Natl Acad Sci USA. 2006 Jan 31;103(5):1364-9]: Describes a method by which processed pseudogenes — cDNA sequences that were generated through reverse transcription of spliced mRNAs and have been reinserted at a new genomic location — can be used to generate a map of the transcriptome. The authors note that the approach can be applied to sequenced metazoan genomes in order to computationally annotate splicing variation even when expressed sequences are unavailable.


Teber ET, Crawford E, Bolton KB, Van Dyk D, Schofield PR, Kapoor V, Church WB. Djinn Lite: a tool for customized gene transcript modeling, annotation-data enrichment and exploration. [BMC Bioinformatics. 2006 Jan 23;7(1):33]: Describes Djinn Lite, a program for storing and visually exploring custom annotations relating to a eukaryotic gene sequence and its modeled gene products. Availability: http://129.78.154.89/churchlab/DjinnAbout.html.


Ting JC, Ye Y, Thomas GH, Ruczinski I, Pevsner J. Analysis and visualization of chromosomal abnormalities in SNP data with SNPscan. [BMC Bioinformatics. 2006 Jan 18;7(1):25]: Describes SNPscan, a web-based tool to analyze and visualize high density SNP data. SNPscan accepts data exported from Affymetrix Copy Number Analysis Tool as its input and is useful for identifying chromosomal abnormalities based on SNP intensity and heterozygosity data, according to the authors. Availability: http://pevsnerlab.kennedykrieger.org/snpscan.htm.


Vallenet D, Labarre L, Rouy Z, Barbe V, Bocs S, Cruveiller S, Lajus A, Pascal G, Scarpelli C, Medigue C. MaGe: a microbial genome annotation system supported by synteny results. [Nucleic Acids Res. 2006 Jan 10;34(1):53-65]: Introduces Magnifying Genomes (MaGe), a microbial genome annotation system based on a relational database containing information on bacterial genomes. Availability: http://www.genoscope.cns.fr/agc/mage.


Van den Bulcke T, Van Leemput K, Naudts B, van Remortel P, Ma H, Verschoren A, De Moor B, Marchal K. SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms. [BMC Bioinformatics. 2006 Jan 26;7(1):43]: Introduces a network generator that creates synthetic transcriptional regulatory networks and produces simulated gene expression data that approximates experimental data. According to the authors, the topological characteristics of the generated networks closely resemble the characteristics of real transcriptional networks and simulation of the network scales well to large networks.


Weinberg Z, Ruzzo W. Sequence-based heuristics for faster annotation of non-coding RNA families. [Bioinformatics 2006 22(1):35-39]: Describes the use of profile HMM-based heuristic filters as a heuristic for speeding covariance model searches for new members of a non-coding RNA gene family in a large genome database. Availability: http://bio.cs.washington.edu/supplements/zasha-HeurHmm-2004/.


Wiley JC, Prattipati M, Lin CP, Ladiges W. Comparative Mouse Genomics Centers Consortium: The Mouse Genotype Database. [Mutat Res. 2006 Jan 25]: Discusses the Comparative Mouse Genomics Centers Consortium (CMGCC), a branch of the Environmental Genome Project sponsored by the National Institute of Environmental Health Sciences that focuses on the identification of human SNPs that may confer disease susceptibility within the human population. The goal of the CMGCC is to make genetic mouse models for human SNPs within cell cycle control, DNA replication, and DNA repair genes that may be associated with human pathologies. In order to facilitate information sharing and analysis within the consortium, a set of informatics resources have been generated to support the mouse model development efforts. Availability: http://mrages.niehs.nih.gov/genotype/public/searchnew/.


Yi M, Horton JD, Cohen JC, Hobbs HH, Stephens RM. WholePathwayScope: A comprehensive pathway-based analysis tool for high-throughput data. [BMC Bioinformatics 2006 Jan 19;7(1):30]: Introduces WholePathwayScope (WPS), a software tool that extracts gene lists with shared biological themes through color cue templates. WPS statistically evaluates global functional category enrichment of gene lists and pathway-level pattern enrichment of data. Availability: http://www.abcc.ncifcrf.gov/wps/wps_index.php.

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