Correlation networks are increasingly being used in bioinformatics applications For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets These methods have been successfully applied in various biological contexts, eg cancer, mouse genetics, yeast genetics, and analysis of brain imaging data While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software Along with the R package we also present R software tutorials While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings The WGCNA package provides R functions for weighted correlation network analysis, eg co-expression network analysis of gene expression data The R package along with its source code and additional material are freely available at http://wwwgeneticsuclaedu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA
 

/pdf/wgcna-an-r-package-for-weighted-correlation-network-analysis-4vjh9rn783.pdf

WGCNA: an R package for weighted correlation network analysis.

The abbreviated name,‘mfold web server’,describes a number of closely related software applications available on the World Wide Web (WWW) for the prediction of the secondary structure of single stranded nucleic acids. The objective of this web server is to provide easy access to RNA and DNA folding and hybridization software to the scientific community at large. By making use of universally available web GUIs (Graphical User Interfaces),the server circumvents the problem of portability of this software. Detailed output,in the form of structure plots with or without reliability information,single strand frequency plots and ‘energy dot plots’, are available for the folding of single sequences. A variety of ‘bulk’ servers give less information,but in a shorter time and for up to hundreds of sequences at once. The portal for the mfold web server is http://www.bioinfo.rpi.edu/applications/ mfold. This URL will be referred to as ‘MFOLDROOT’.

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Mfold web server for nucleic acid folding and hybridization prediction

Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes

Motivation: Next-generation sequencing technologies generate very large numbers of short reads. Even with very deep genome coverage, short read lengths cause problems in de novo assemblies. The use of paired-end libraries with a fragment size shorter than twice the read length provides an opportunity to generate much longer reads by overlapping and merging read pairs before assembling a genome.

Results: We present FLASH, a fast computational tool to extend the length of short reads by overlapping paired-end reads from fragment libraries that are sufficiently short. We tested the correctness of the tool on one million simulated read pairs, and we then applied it as a pre-processor for genome assemblies of Illumina reads from the bacterium Staphylococcus aureus and human chromosome 14. FLASH correctly extended and merged reads >99% of the time on simulated reads with an error rate of <1%. With adequately set parameters, FLASH correctly merged reads over 90% of the time even when the reads contained up to 5% errors. When FLASH was used to extend reads prior to assembly, the resulting assemblies had substantially greater N50 lengths for both contigs and scaffolds.

Availability and Implementation: The FLASH system is implemented in C and is freely available as open-source code at http://www.cbcb.umd.edu/software/flash.

Contact: moc.liamg@cogam.t

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FLASH: Fast Length Adjustment of Short Reads to Improve Genome Assemblies

A novel and robust automated docking method that predicts the bound conformations of flexible ligands to macromolecular targets has been developed and tested, in combination with a new scoring function that estimates the free energy change upon binding. Interestingly, this method applies a Lamarckian model of genetics, in which environmental adaptations of an individual's phenotype are reverse transcribed into its genotype and become . heritable traits sic . We consider three search methods, Monte Carlo simulated annealing, a traditional genetic algorithm, and the Lamarckian genetic algorithm, and compare their performance in dockings of seven protein)ligand test systems having known three-dimensional structure. We show that both the traditional and Lamarckian genetic algorithms can handle ligands with more degrees of freedom than the simulated annealing method used in earlier versions of AUTODOCK, and that the Lamarckian genetic algorithm is the most efficient, reliable, and successful of the three. The empirical free energy function was calibrated using a set of 30 structurally known protein)ligand complexes with experimentally determined binding constants. Linear regression analysis of the observed binding constants in terms of a wide variety of structure-derived molecular properties was performed. The final model had a residual standard y1 y1 .

/pdf/automated-docking-using-a-lamarckian-genetic-algorithm-and-2t6y95r7fm.pdf

Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function

The detection and classification of membrane-spanning proteins

Hydrophobicity scales and computational techniques for detecting amphipathic structures in proteins

We propose, on the basis of physical chemical and biological requirements for T-cell activation by antigen, that sites on a protein that can stimulate T lymphocytes will be capable of forming a stable amphipathic structure (i.e., one with separated hydrophobic and hydrophilic surfaces), displaying periodicity in hydrophobic residues. A spectral analysis of the 12 antigenic sites to which the method could be applied indicates that the amphipathic periodicity hypothesis is valid for 10 of them, generally with reliabilities that are well above 98%, with periodicities compatible with an alpha-helical structure. An 11th case manifests a different type of amphipathicity. The analyses require only a knowledge of amino acid sequence. The finding that T-cell antigenic sites show a high correlation with amphipathicity greatly simplifies the search for such sites and is potentially important for vaccine development.

T-cell antigenic sites tend to be amphipathic structures.

We have used a data base of 23 known immunodominant helper T cell antigenic sites located on 12 proteins to systematically develop an optimized algorithm for predicting T cell antigenic sites. The algorithm is based on the amphipathic helix model in which antigenic sites are postulated to be helices with one face predominantly polar and the opposite face predominantly apolar. Such amphipathic structures can form when the polarity of residues along the sequence varies with a more or less regular period. Hence they can be identified by methods (so called power spectrum procedures) that detect periodic variations in properties of a sequence. The choice of power spectrum procedure, hydrophobicity scale, and model parameters are examined. An algorithm is tested by comparing the predicted amphipathic segments with the locations of the known T cell sites, counting the number of matches, and calculating the probability of getting this number by chance alone. The optimum algorithm, which predicts the largest number of sites with the lowest chance probability, uses the Fauchere-Pliska hydrophobicity scale and a least squares fit of a sinusoid as its power spectrum procedure. By applying this algorithm, 18 of the 23 known sites are identified (75% sensitivity) with a high degree of significance (p less than 0.001). The success of the algorithm supports the hypothesis that stable amphipathic helices are fundamentally important in determining immunodominance. This approach may be of practical value in designing synthetic vaccines aimed at T cell immunity.

Charles DeLisi

Papers

The detection and classification of membrane-spanning proteins

Hydrophobicity scales and computational techniques for detecting amphipathic structures in proteins

T-cell antigenic sites tend to be amphipathic structures.

Prediction of immunodominant helper T cell antigenic sites from the primary sequence.

Determination of atomic desolvation energies from the structures of crystallized proteins.