GenePro: a Cytoscape plug-in for advanced visualization and analysis of interaction networks

MOTIVATION: Analyzing the networks of interactions between genes and proteins has become a central theme in systems biology. Versatile software tools for interactively displaying and analyzing these networks are therefore very much in demand. The public-domain open software environment Cytoscape has been developed with the goal of facilitating the design and development of such software tools by the scientific community. RESULTS: We present GenePro, a plugin to Cytoscape featuring a set of versatile tools that greatly facilitates the visualization and analysis of protein networks derived from high-throughput interactions data and the validation of various methods for parsing these networks into meaningful functional modules. AVAILABILITY: The GenePro plugin is available at the website http://genepro.ccb.sickkids.ca.     

VisANT: an online visualization and analysis tool for biological interaction data

Background  

New techniques for determining relationships between biomolecules of all types – genes, proteins, noncoding DNA, metabolites and small molecules – are now making a substantial contribution to the widely discussed explosion of facts about the cell. The data generated by these techniques promote a picture of the cell as an interconnected information network, with molecular components linked with one another in topologies that can encode and represent many features of cellular function. This networked view of biology brings the potential for systematic understanding of living molecular systems.     

Topological analysis and interactive visualization of biological networks and protein structures

Computational analysis and interactive visualization of biological networks and protein structures are common tasks for gaining insight into biological processes. This protocol describes three workflows based on the NetworkAnalyzer and RINalyzer plug-ins for Cytoscape, a popular software platform for networks. NetworkAnalyzer has become a standard Cytoscape tool for comprehensive network topology analysis. In addition, RINalyzer provides methods for exploring residue interaction networks derived from protein structures. The first workflow uses NetworkAnalyzer to perform a topological analysis of biological networks. The second workflow applies RINalyzer to study protein structure and function and to compute network centrality measures. The third workflow combines NetworkAnalyzer and RINalyzer to compare residue networks. The full protocol can be completed in ～2 h.     

Putting microarrays in a context: integrated analysis of diverse biological data

In recent years, multiple types of high-throughput functional genomic data that facilitate rapid functional annotation of sequenced genomes have become available. Gene expression microarrays are the most commonly available source of such data. However, genomic data often sacrifice specificity for scale, yielding very large quantities of relatively lower-quality data than traditional experimental methods. Thus sophisticated analysis methods are necessary to make accurate functional interpretation of these large-scale data sets. This review presents an overview of recently developed methods that integrate the analysis of microarray data with sequence, interaction, localisation and literature data, and further outlines current challenges in the field. The focus of this review is on the use of such methods for gene function prediction, understanding of protein regulation and modelling of biological networks.     

NMR View: A computer program for the visualization and analysis of NMR data

Summary NMR View is a computer program designed for the visualization and analysis of NMR data. It allows the user to interact with a practically unlimited number of 2D, 3D and 4D NMR data files. Any number of spectral windows can be displayed on the screen in any size and location. Automatic peak picking and facilitated peak analysis features are included to aid in the assignment of complex NMR spectra. NMR View provides structure analysis features and data transfer to and from structure generation programs, allowing for a tight coupling between spectral analysis and structure generation. Visual correlation between structures and spectra can be done with the Molecular Data Viewer, a molecular graphics program with bidirectional communication to NMR View. The user interface can be customized and a command language is provided to allow for the automation of various tasks.Inquiries concerning the availability of NMR View and the Molecular Data Viewer should be sent via email to johnsonb@merck.com or to Bruce A. Johnson, Merck Research Laboratories, RY80Y-103, P.O. Box 2000, Rahway, NJ 07065, U.S.A.     

VitaPad: visualization tools for the analysis of pathway data

MOTIVATION: Packages that support the creation of pathway diagrams are limited by their inability to be readily extended to new classes of pathway-related data. RESULTS: VitaPad is a cross-platform application that enables users to create and modify biological pathway diagrams and incorporate microarray data with them. It improves on existing software in the following areas: (i) It can create diagrams dynamically through graph layout algorithms. (ii) It is open-source and uses an open XML format to store data, allowing for easy extension or integration with other tools. (iii) It features a cutting-edge user interface with intuitive controls, high-resolution graphics and fully customizable appearance. AVAILABILITY: http://bioinformatics.med.yale.edu CONTACTS: matthew.holford@yale.edu; hongyu.zhao@yale.edu.     

MetaReg: a platform for modeling, analysis and visualization of biological systems using large-scale experimental data

MetaReg http://acgt.cs.tau.ac.il/metareg/application.html is a computational tool that models cellular networks and integrates experimental results with such models. MetaReg represents established knowledge about a biological system, available today mostly in informal form in the literature, as probabilistic network models with underlying combinatorial regulatory logic. MetaReg enables contrasting predictions with measurements, model improvements and studying what-if scenarios. By summarizing prior knowledge and providing visual and computational aids, it helps the expert explore and understand her system better.     

A novel framework for the comparative analysis of biological networks

Genome sequencing projects provide nearly complete lists of the individual components present in an organism, but reveal little about how they work together. Follow-up initiatives have deciphered thousands of dynamic and context-dependent interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Here, we present a novel framework for the alignment and comparative analysis of biological networks of arbitrary topology. Our strategy includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in the current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which vastly increases its performance with respect to existing tools. Finally, we illustrate the biological significance of the results through the identification of novel complex components and potential cases of cross-talk between pathways and alternative signaling routes.     

PATIKAweb: a Web interface for analyzing biological pathways through advanced querying and visualization

Patikaweb provides a Web interface for retrieving and analyzing biological pathways in the Patika database, which contains data integrated from various prominent public pathway databases. It features a user-friendly interface, dynamic visualization and automated layout, advanced graph-theoretic queries for extracting biologically important phenomena, local persistence capability and exporting facilities to various pathway exchange formats.     

SNAVI: Desktop application for analysis and visualization of large-scale signaling networks

Background  

Studies of cellular signaling indicate that signal transduction pathways combine to form large networks of interactions. Viewing protein-protein and ligand-protein interactions as graphs (networks), where biomolecules are represented as nodes and their interactions are represented as links, is a promising approach for integrating experimental results from different sources to achieve a systematic understanding of the molecular mechanisms driving cell phenotype. The emergence of large-scale signaling networks provides an opportunity for topological statistical analysis while visualization of such networks represents a challenge.     

Phenostat: visualization and statistical tool for analysis of phenotyping data

The effective extraction of information from multidimensional data sets derived from phenotyping experiments is a growing challenge in biology. Data visualization tools are important resources that can aid in exploratory data analysis of complex data sets. Phenotyping experiments of model organisms produce data sets in which a large number of phenotypic measures are collected for each individual in a group. A critical initial step in the analysis of such multidimensional data sets is the exploratory analysis of data distribution and correlation. To facilitate the rapid visualization and exploratory analysis of multidimensional complex trait data, we have developed a user-friendly, web-based software tool called Phenostat. Phenostat is composed of a dynamic graphical environment that allows the user to inspect the distribution of multiple variables in a data set simultaneously. Individuals can be selected by directly clicking on the graphs and thus displaying their identity, highlighting corresponding values in all graphs, allowing their inclusion or exclusion from the analysis. Statistical analysis is provided by R package functions. Phenostat is particularly suited for rapid distribution and correlation analysis of subsets of data. An analysis of behavioral and physiologic data stemming from a large mouse phenotyping experiment using Phenostat reveals previously unsuspected correlations. Phenostat is freely available to academic institutions and nonprofit organizations and can be used from our website at .     

BIOZON: a system for unification, management and analysis of heterogeneous biological data

Background  

Integration of heterogeneous data types is a challenging problem, especially in biology, where the number of databases and data types increase rapidly. Amongst the problems that one has to face are integrity, consistency, redundancy, connectivity, expressiveness and updatability.     

Elementary network reconstruction: A framework for the analysis of regulatory networks in biological systems

Complexity of regulatory networks arises from the high degree of interaction between network components such as DNA, RNA, proteins, and metabolites. We have developed a modeling tool, elementary network reconstruction (ENR), to characterize these networks. ENR is a knowledge-driven, steady state, deterministic, quantitative modeling approach based on linear perturbation theory. In ENR we demonstrate a novel means of expressing control mechanisms by way of dimensionless steady state gains relating input and output variables, which are purely in terms of species abundances (extensive variables). As a result of systematic enumeration of network species in n×n matrix, the two properties of linear perturbation are manifested in graphical representations: transitive property is evident in a special L-shape structure, and additive property is evident in multiple L-shape structures arriving at the same matrix cell. Upon imposing mechanistic (lowest-level) gains, network self-assembly through transitive and additive properties results in elucidation of inherent topology and explicit cataloging of higher level gains, which in turn can be used to predict perturbation results. Application of ENR to the regulatory network behind carbon catabolite repression in Escherichia coli is presented. Through incorporation of known molecular mechanisms governing transient and permanent repressions, the ENR model correctly predicts several key features of this regulatory network, including a 50% downshift in intracellular cAMP level upon exposure to glucose. Since functional genomics studies are mainly concerned with redistribution of species abundances in perturbed systems, ENR could be exploited in the system-level analysis of biological systems.     

Chemotography for multi-target SAR analysis in the context of biological pathways

The increasing amount of chemogenomics data, that is, activity measurements of many compounds across a variety of biological targets, allows for better understanding of pharmacology in a broad biological context. Rather than assessing activity at individual biological targets, today understanding of compound interaction with complex biological systems and molecular pathways is often sought in phenotypic screens. This perspective poses novel challenges to structure-activity relationship (SAR) assessment. Today, the bottleneck of drug discovery lies in the understanding of SAR of rich datasets that go beyond single targets in the context of biological pathways, potential off-targets, and complex selectivity profiles. To aid in the understanding and interpretation of such complex SAR, we introduce Chemotography (chemotype chromatography), which encodes chemical space using a color spectrum by combining clustering and multidimensional scaling. Rich biological data in our approach were visualized using spatial dimensions traditionally reserved for chemical space. This allowed us to analyze SAR in the context of target hierarchies and phylogenetic trees, two-target activity scatter plots, and biological pathways. Chemotography, in combination with the Kyoto Encyclopedia of Genes and Genomes (KEGG), also allowed us to extract pathway-relevant SAR from the ChEMBL database. We identified chemotypes showing polypharmacology and selectivity-conferring scaffolds, even in cases where individual compounds have not been tested against all relevant targets. In addition, we analyzed SAR in ChEMBL across the entire Kinome, going beyond individual compounds. Our method combines the strengths of chemical space visualization for SAR analysis and graphical representation of complex biological data. Chemotography is a new paradigm for chemogenomic data visualization and its versatile applications presented here may allow for improved assessment of SAR in biological context, such as phenotypic assay hit lists.     

ArrayCyGHt: a web application for analysis and visualization of array-CGH data

ArrayCyGHt is a web-based application tool for analysis and visualization of microarray-comparative genomic hybridization (array-CGH) data. Full process of array-CGH data analysis, from normalization of raw data to the final visualization of copy number gain or loss, can be straightforwardly achieved on this arrayCyGHt system without the use of any further software. ArrayCyGHt, therefore, provides an easy and fast tool for the analysis of copy number aberrations in any kinds of data format. AVAILABILITY: ArrayCyGHt can be accessed at http://genomics.catholic.ac.kr/arrayCGH/     

GGT 2.0: versatile software for visualization and analysis of genetic data

Ever since its first release in 1999, the free software package for visualization of molecular marker data, graphical genotype (GGT), has been constantly adapted and improved. The GGT package was developed in a plant-breeding context and thus focuses on plant genetic data but was not intended to be limited to plants only. The current version has many options for genetic analysis of populations including diversity analyses and simple association studies. A second release of the GGT package, GGT 2.0 (available through http://www.plantbreeding.wur.nl), is therefore presented in this paper. An overview of existing and new features that are available within GGT 2.0, and a case study in which GGT 2.0 is applied to analyze an existing set of plant genetic data, are presented and discussed.