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 .

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

We introduce a method for docking small flexible ligands of the size of dipeptides and phosphocholine and test it against crystallographic complexes. We then show how the method can be used as the basis for a strategy for solving the much more difficult problem of docking fully flexible peptides in the 8-10-residue size range. After developing the method we apply it to peptide-MHC class I systems and find that the predictions are in accord with biological and crystallographic data.

Toward computational determination of peptide-receptor structure

New Jersey Comission on Cancer Research (CCR-703054-03); Institute for Advanced Study through the David and Lucile Packard Foundation; Shelby White and Leon Levy Initiative Fund

Data Perturbation Independent Diagnosis and Validation of Breast Cancer Subtypes Using Clustering and Patterns

Empirical free energy as a target function in docking and design: application to HIV-1 protease inhibitors

The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT’s unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the “symbiotic layout” of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

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Visualization of metabolic interaction networks in microbial communities using VisANT 5.0

Publisher Summary This chapter describes the physical chemistry and cellular regulation of cell surface receptors and discusses the situations in which signal induction follows binding of ligand to a cell surface receptor, without requiring crosslinking or clustering of ligand-receptor complexes. The fundamental aspects of ligand-receptor kinetics are reviewed, with special emphasis on the difference between cell surface receptors and receptors free in solution. The chapter also describes various possible dose-response curves, expected for simple receptor-occupancy mechanisms. The magnitude of a signal transmitted by receptors to the cell's interior is directly dependent on the degree to which they are occupied, but totally independent of their spatial distribution. Such simple occupancy-dependent mechanisms can be thought of in terms of a static surface with uniformly distributed receptors and equilibrium controlled receptor-ligand interactions. Interactions between two cells or between a cell and a surface are mediated through ligand–receptor binding. Examples include agglutination of lymphocytes by lectins, rosetting of lymphocytes or erythrocytes by antibodies, phagocytosis of particles by leukocytes or macrophages, and regulatory cell interactions during an immune response.

Charles DeLisi

Papers

Toward computational determination of peptide-receptor structure

Data Perturbation Independent Diagnosis and Validation of Breast Cancer Subtypes Using Clustering and Patterns

Empirical free energy as a target function in docking and design: application to HIV-1 protease inhibitors

Visualization of metabolic interaction networks in microbial communities using VisANT 5.0

Cell surface receptors: physical chemistry and cellular regulation.