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


Barrett CL, Palsson BO Iterative Reconstruction of Transcriptional Regulatory Networks: An Algorithmic Approach. [PLoS Comput Biol 2(5): e52]: Introduces an algorithm for experimental design to efficiently reconstruct transcriptional regulatory networks. The algorithm is meant to be used in conjunction with an experimental laboratory component. It uses probability estimates based on a wide range of computational and experimental sources to suggest experiments "with the highest potential of discovering the greatest amount of new regulatory knowledge," according to the authors.

Bonneau R, Reiss DJ, Shannon P, Facciotti M, Hood L, Baliga NS, Thorsson V. The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. [Genome Biology 2006, 7:R36]: Presents the Inferelator, a method for deriving genome-wide transcriptional regulatory interactions. The approach uses regression and variable selection to identify transcriptional influences on genes based on the integration of genome annotation and expression data.

Callegaro A, Spinelli R, Beltrame L, Bicciato S, Caristina L, Censuales S, De Bellis G, Battaglia C. Algorithm for automatic genotype calling of single nucleotide polymorphisms using the full course of TaqMan real-time data. [Nucleic Acids Research 2006 34(7):e56]: Describes the best cycle genotyping algorithm (BCGA), an algorithm for automatic genotype calling based on real-time PCR data. BCGA is written in R, and is based on the assumption that classification "depends on the time (cycle) of amplification and that it is possible to identify a best discriminating cycle for each SNP assay," according to the abstract. The authors note that the method eliminates the need for positive controls and permits accurate genotyping even in the absence of a genotype class, for example when one allele is rare.

Cameron M, Williams HE, Cannane A. A Deterministic Finite Automaton for Faster Protein Hit Detection in BLAST. [J Comput Biol. 2006 May;13(4):965-78]: Describes an optimization for the first stage of the Blast algorithm specifically designed for protein search. The method produces the same results as NCBI-Blast, but in around 59 percent of the time on Intel-based platforms, according to the authors. The approach uses a deterministic finite automaton and is optimized for modern hardware, "making careful use of cache-conscious approaches to improve speed." Availability:

Cioffi A, Fleury TJ, Stein A. Aspects of large-scale chromatin structures in mouse liver nuclei can be predicted from the DNA sequence. [Nucleic Acids Research 2006 34(7):1974-1981]: Presents evidence "that it is possible to predict computationally, from the DNA sequence, loci in mouse liver nuclei that possess distinctive nucleosome arrays," according to the abstract. The authors note that the findings may be useful for identifying distinctive chromatin structures computationally from DNA sequence.

Eargle J, Luthey-Schulten Z. Visualizing the dual space of biological molecules. [Comput Biol Chem. 2006 May 2 (e-pub ahead of print)]: Presents a tessellation method for visualizing the dual space around, within, and between biological molecules. The method uses Delaunay triangulation to construct a three-dimensional graph to provide "a displayable discretization of the continuous volume," according to the abstract. The graph structure is used to compare the dual space of a system in two different states. The authors have developed a cross-platform implementation of the algorithm called Tessellator. Availability:

Kane D, Hohman MM, Cerami EG, McCormick MW, Kuhlmman KF, Byrd JA. Agile methods in biomedical software development: a multi-site experience report. [BMC Bioinformatics 2006, 7:273]: Discusses the use of agile methods, described as "an iterative approach to software development that relies on strong collaboration and automation to keep pace with dynamic environments," in the creation and maintenance of biomedical software. The authors present a qualitative study of their experiences using these methods, which they find to be "well suited to the exploratory and iterative nature of scientific inquiry," and "a robust framework for reproducing scientific results and for developing clinical support systems."

Kozakov D, Brenke R, Comeau S, Vajda S. PIPER: An FFT-based Protein Docking Program with Pairwise Potentials. [ArXiv preprint archive:]: Describes a variation of the fast Fourier transform correlation approach to protein-protein docking, which removes the restriction of describing the energy in the form of a correlation function. The authors also introduce a new class of structure-based pairwise intermolecular potentials called DARS (Decoys As the Reference State) potentials.

Murakami Y, Jones S. SHARP2: protein-protein interaction predictions using patch analysis. [Bioinformatics. 2006 May 3 (e-pub ahead of print)]: Introduces SHARP(2), a web-based bioinformatics tool for predicting potential protein-protein interaction sites on protein structures. It uses a predictive algorithm that calculates six parameters for overlapping patches of residues on the surface of a protein: solvation potential, hydrophobicity, accessible surface area, residue interface propensity, planarity, and protrusion. Parameter scores for each patch are combined, and the patch with the highest combined score is predicted as a potential interaction site. Availability:

Sun N, Carroll RJ, Zhao H. Bayesian error analysis model for reconstructing transcriptional regulatory networks. [Proc Natl Acad Sci USA. 2006 May 23;103(21):7988-93]: Discusses a Bayesian error analysis model to integrate protein-DNA binding data and gene expression data to reconstruct transcriptional regulatory networks. Transcription is modeled as a set of biochemical reactions to develop a linear system model with "clear biological interpretation," according to the abstract. Measurement errors in both protein-DNA binding data and gene expression data are explicitly considered in a Bayesian hierarchical model framework and model parameters are inferred through Markov chain Monte Carlo.

Valouev A, Zhang Y, Schwartz DC, Waterman MS. Refinement of optical map assemblies. [Bioinformatics 2006 22(10):1217-1224]: Discusses a computationally efficient model-based method for improving the assembly of genomic optical maps. According to the authors, the method is highly accurate, even with moderate coverage. The method uses a hidden Markov model to represent the consensus map and uses the expectation-maximization algorithm to drive the refinement process. Availability:

White AM, Daly DS, Varnum SM, Anderson KK, Bollinger N, Zangar RC. ProMAT: protein microarray analysis tool. [Bioinformatics 2006 22(10):1278-1279]: Introduces ProMAT, a software tool for statistically analyzing data from enzyme-linked immunosorbent assay microarray experiments. The software estimates standard curves, sample protein concentrations and their uncertainties for multiple assays. Availability:

Wiuf C, Brameier M, Hagberg O, Stumpf MP. C, Brameier M, Hagberg O, Stumpf MP. Likelihood approach to analysis of network data. [Proc Natl Acad Sci USA. 2006 May 16;103(20):7566-70]: Describes a full-likelihood approach for estimating parameters for general models of network growth that can be expressed in terms of recursion relations. As proof of concept, the authors use the approach to estimate growth parameters for the Caenorhabditis elegans protein interaction network.

Wu X, Zhu L, Guo J, Zhang DY, Lin K. Prediction of yeast protein-protein interaction network: insights from the Gene Ontology and annotations. [Nucleic Acids Research 2006 34(7):2137-2150]: Proposes a new method of reconstructing a yeast protein-protein interaction map based solely on Gene Ontology annotations.

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