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In Print: Bioinformatics Tool-Related Papers of Note, May 2004

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Cherkasov A, Jones S. An approach to large scale identification of non-obvious structural similarities between proteins. [BMC Bioinformatics 2004, 5:61]: Presents a sequence-independent search method for proteins with similar three-dimensional structures. The approach uses the numerical outputs from sequence-structure threading to identify the potential structural similarity between a pair of proteins by correlating the threading scores of the corresponding two primary sequences against the library of the standard folds.


Conant G, Wagner, A. A fast algorithm for determining the best combination of local alignments to a query sequence. [BMC Bioinformatics 2004, 5:62]: Describes a graph-based algorithm called find_max_cover that combines multiple local alignments into a single combination of alignments that either covers the maximal portion of the query or results in the single highest alignment score to the query. Availability: http://www.unm.edu/~compbio/software/find_max_cover/.


Dhar P, et al. Cellware — a multi-algorithmic software for computational systems biology. [Bioinformatics 2004 20(8): 1319-21]: Presents Cellware, a multi-algorithmic environment for modeling and simulating both deterministic and stochastic events in the cell. Availability: http://www.bii.a-star.edu.sg/sbg/cellware.


Edgar R, Sjölander K. COACH: profile-profile alignment of protein families using hidden Markov models. [Bioinformatics 2004 20(8): 1309-18]: Discusses a profile-profile alignment approach called Comparison of Alignments by Constructing Hidden Markov Models (COACH), which aligns two multiple sequence alignments by constructing a profile HMM from one alignment and aligning the other to that HMM.


Gan H, et al. RAG: RNA-As-Graphs database-concepts, analysis, and features. [Bioinformatics 2004 20(8): 1285-1291]: Introduces the RNA-As-Graphs (RAG) database, which describes and ranks all mathematically possible RNA secondary motifs on the basis of graphical enumeration techniques. All RNA motifs are catalogued by a graph vertex number (a measure of sequence length) and ranked by topological complexity. Availability: http://monod.biomath.nyu.edu/rna.


Glenisson P, et al. TXTGate: profiling gene groups with text-based information. [Genome Biology 2004, 5:R43]: Describes TXTGate, which combines literature indices of selected public biological resources in a system designed for analyzing groups of genes. Availability: http://www.esat.kuleuven.ac.be/txtgate/index.jsp.


Halling-Brown M, et al. A Fugu-Human Genome Synteny Viewer: web software for graphical display and annotation reports of synteny between Fugu genomic sequence and human genes. [Nucleic Acids Research 2004 32(8): 2618-2622]: Describes a web server for accessing annotation and graphical reports of synteny and gene order between the Fugu genome and human genes. Availability: http://fugu.rfcgr.mrc.ac.uk/.


Kiriakidou M, et al. A combined computational-experimental approach predicts human microRNA targets. [Genes Dev. 2004 18(10): 1165-78]: Describes a computational program, DIANA-microT, that identifies mRNA targets for animal miRNAs and predicts mRNA targets for human and mouse miRNAs. Availability: http://diana.pcbi.upenn.edu/.


Kleinjung J, et al. Contact-based sequence alignment. [Nucleic Acids Research 2004 32(8): 2464-2473]: Introduces a method of contact-based protein sequence alignment, where structural information in the form of contact mutation probabilities is incorporated into an alignment routine using contact-mutation matrices.


Leone M, Pagnani A. Predicting protein functions with message-passing algorithms. [arXiv pre-print archive: http://arXiv.org/abs/q-bio/0405007]: Describes a method for predicting protein function using a message-passing algorithm called Belief Propagation, which takes as input a network of protein physical interactions and a catalog of known protein functions, and returns the probabilities for each unclassified protein of having one chosen function.


Lin M, et al. dChipSNP: significance curve and clustering of SNP-array-based loss-of-heterozygosity data. [Bioinformatics 2004 20(8): 1233-40]: Presents software for loss-of-heterozygosity analysis of paired normal and tumor samples using SNP arrays. Availability: http://biosun1.harvard.edu/complab/dchip/snp/.


Mathews D, et al. Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. [Proc. Natl. Acad. Sci. USA 2004 101(19): 7287-7292]: Presents RNAstructure, a dynamic programming algorithm for predicting RNA secondary structure. Availability: http://rna.chem.rochester.edu/RNAstructure.html.


Negre V, Grunau C. eL-DASionator: an LDAS upload file generator. [BMC Bioinformatics 2004, 5:55]: Presents a software tool for generating upload files for the LDAS “Lightweight DAS” server. Availability: http://atgc.lirmm.fr/eldasionator.html.


Tong W, et al. Development of public toxicogenomics software for microarray data management and analysis. [Mutat Res. 2004 549(1-2); 241-53]: Introduces a microarray data management and analysis software package called ArrayTrack for storing both microarray data and experimental parameters associated with toxicogenomics studies. Availability: http://edkb.fda.gov/webstart/arraytrack/.


Vernizzi A, Orland H, See A. Prediction of RNA pseudoknots by Monte Carlo simulations. [arXiv pre-print archive: http://arXiv.org/abs/q-bio/0405014]: Describes a Monte Carlo algorithm for the prediction of pseudoknots in an RNA molecule based on a graphical representation in which the secondary structures are described by planar diagrams and pseudoknots are identified as non-planar diagrams.


Zhang S, Gant T. A statistical framework for the design of microarray experiments and effective detection of differential gene expression. [arXiv pre-print archive: http://arXiv.org/abs/q-bio/0405015]: Discusses a new statistical T-test to determine differentially expressed genes in the context of microarray experiments, along with an exact formula for calculating the detection power of the T-test.


Zhang Y, Skolnick J. Automated structure prediction of weakly homologous proteins on a genomic scale. [Proc. Natl. Acad. Sci. USA 2004 101(20): 7594-7599]: Presents TASSER (Threading Assembly Refinement), a hierarchical protein-structure-prediction method that consists of template identification by threading, followed by tertiary structure assembly.

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