Kiwi: a tool for integration and visualization of network topology and gene-set analysis

Background

The analysis of high-throughput data in biology is aided by integrative approaches such as gene-set analysis. Gene-sets can represent well-defined biological entities (e.g. metabolites) that interact in networks (e.g. metabolic networks), to exert their function within the cell. Data interpretation can benefit from incorporating the underlying network, but there are currently no optimal methods that link gene-set analysis and network structures.

Results

Here we present Kiwi, a new tool that processes output data from gene-set analysis and integrates them with a network structure such that the inherent connectivity between gene-sets, i.e. not simply the gene overlap, becomes apparent. In two case studies, we demonstrate that standard gene-set analysis points at metabolites regulated in the interrogated condition. Nevertheless, only the integration of the interactions between these metabolites provides an extra layer of information that highlights how they are tightly connected in the metabolic network.

Conclusions

Kiwi is a tool that enhances interpretability of high-throughput data. It allows the users not only to discover a list of significant entities or processes as in gene-set analysis, but also to visualize whether these entities or processes are isolated or connected by means of their biological interaction. Kiwi is available as a Python package at http://www.sysbio.se/kiwi and an online tool in the BioMet Toolbox at http://www.biomet-toolbox.org.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0408-9) contains supplementary material, which is available to authorized users.     

SeqExpress: desktop analysis and visualization tool for gene expression experiments

SUMMARY: SeqExpress is a stand-alone desktop application for the identification of relevant genes within collections of microarray or SAGE experiments. A number of analysis, filtering and visualization tools are provided to aid in the selection of groups of genes. If R is installed then the application can use this to provide further analysis. AVAILABILITY: SeqExpress is available at: http://www.seqexpress.com     

ANDVisio: a new tool for graphic visualization and analysis of literature mined associative gene networks in the ANDSystem

The ANDVisio tool is designed to reconstruct and analyze associative gene networks in the earlier developed Associative Network Discovery System (ANDSystem) software package. The ANDSystem incorporates utilities for automated extraction of knowledge from Pubmed published scientific texts, analysis of factographic databases, also the ANDCell database containing information on molecular-genetic events retrieved from texts and databases. ANDVisio is a new user's interface to the ANDCell database stored in a remote server. ANDVisio provides graphic visualization, editing, search, also saving of associative gene networks in different formats resulting from user's request. The associative gene networks describe semantic relationships between molecular-genetic objects (proteins, genes, metabolites and others), biological processes, and diseases. ANDVisio is provided with various tools to support filtering by object types, relationships between objects and information sources; graph layout; search of the shortest pathway; cycles in graphs.     

Immunogenetic Management Software: a new tool for visualization and analysis of complex immunogenetic datasets

Here we describe the Immunogenetic Management Software (IMS) system, a novel web-based application that permits multiplexed analysis of complex immunogenetic traits that are necessary for the accurate planning and execution of experiments involving large animal models, including nonhuman primates. IMS is capable of housing complex pedigree relationships, microsatellite-based MHC typing data, as well as MHC pyrosequencing expression analysis of class I alleles. It includes a novel, automated MHC haplotype naming algorithm and has accomplished an innovative visualization protocol that allows users to view multiple familial and MHC haplotype relationships through a single, interactive graphical interface. Detailed DNA and RNA-based data can also be queried and analyzed in a highly accessible fashion, and flexible search capabilities allow experimental choices to be made based on multiple, individualized and expandable immunogenetic factors. This web application is implemented in Java, MySQL, Tomcat, and Apache, with supported browsers including Internet Explorer and Firefox on Windows and Safari on Mac OS. The software is freely available for distribution to noncommercial users by contacting Leslie.kean@emory.edu. A demonstration site for the software is available at http://typing.emory.edu/typing_demo , user name: imsdemo7@gmail.com and password: imsdemo.     

GObar: A Gene Ontology based analysis and visualization tool for gene sets

Background  

Microarray experiments, as well as other genomic analyses, often result in large gene sets containing up to several hundred genes. The biological significance of such sets of genes is, usually, not readily apparent.     

TranscriptomeBrowser 3.0: introducing a new compendium of molecular interactions and a new visualization tool for the study of gene regulatory networks

Background  

Deciphering gene regulatory networks by in silico approaches is a crucial step in the study of the molecular perturbations that occur in diseases. The development of regulatory maps is a tedious process requiring the comprehensive integration of various evidences scattered over biological databases. Thus, the research community would greatly benefit from having a unified database storing known and predicted molecular interactions. Furthermore, given the intrinsic complexity of the data, the development of new tools offering integrated and meaningful visualizations of molecular interactions is necessary to help users drawing new hypotheses without being overwhelmed by the density of the subsequent graph.     

SNPTools: a software tool for visualization and analysis of microarray data

SUMMARY: We have created a software tool, SNPTools, for analysis and visualization of microarray data, mainly SNP array data. The software can analyse and find differences in intensity levels between groups of arrays and identify segments of SNPs (genes, clones), where the intensity levels differ significantly between the groups. In addition, SNPTools can show jointly loss-of-heterozygosity (LOH) data (derived from genotypes) and intensity data for paired samples of tumour and normal arrays. The output graphs can be manipulated in various ways to modify and adjust the layout. A wizard allows options and parameters to be changed easily and graphs replotted. All output can be saved in various formats, and also re-opened in SNPTools for further analysis. For explorative use, SNPTools allows various genome information to be loaded onto the graphs. AVAILABILITY: The software, example data sets and tutorials are freely available from http://www.birc.au.dk/snptools     

BicOverlapper: a tool for bicluster visualization

Summary: BicOverlapper is a tool to visualize biclusters fromgene-expression matrices in a way that helps to compare biclusteringmethods, to unravel trends and to highlight relevant genes andconditions. A visual approach can complement biological andstatistical analysis and reduce the time spent by specialistsinterpreting the results of biclustering algorithms. The techniqueis based on a force-directed graph where biclusters are representedas flexible overlapped groups of genes and conditions. Availability: The BicOverlapper software and supplementary materialare available at http://vis.usal.es/bicoverlapper Contact: rodri{at}usal.es Associate Editor: John Quackenbush The first two authors should be reported as joint first authors.     

MarC-V: a spreadsheet-based tool for analysis, normalization, and visualization of single cDNA microarray experiments

The comprehensive analysis and visualization of data extracted from cDNA microarrays can be a time-consuming and error-prone process that becomes increasingly tedious with increased number of gene elements on a particular microarray. With the increasingly large number of gene elements on today's microarrays, analysis tools must be developed to meet this challenge. Here, we present MarC-V, a Microsoft Excel spreadsheet tool with Visual Basic macros to automate much of the visualization and calculation involved in the analysis process while providing the familiarity and flexibility of Excel. Automated features of this tool include (i) lower-bound thresholding, (ii) data normalization, (iii) generation of ratio frequency distribution plots, (iv) generation of scatter plots color-coded by expression level, (v) ratio scoring based on intensity measurements, (vi) filtering of data based on expression level or specific gene interests, and (vii) exporting data for subsequent multi-array analysis. MarC-V also has an importing function included for GenePix results (GPR) raw data files.     

solQTL: a tool for QTL analysis,visualization and linking to genomes at SGN database

Background  

A common approach to understanding the genetic basis of complex traits is through identification of associated quantitative trait loci (QTL). Fine mapping QTLs requires several generations of backcrosses and analysis of large populations, which is time-consuming and costly effort. Furthermore, as entire genomes are being sequenced and an increasing amount of genetic and expression data are being generated, a challenge remains: linking phenotypic variation to the underlying genomic variation. To identify candidate genes and understand the molecular basis underlying the phenotypic variation of traits, bioinformatic approaches are needed to exploit information such as genetic map, expression and whole genome sequence data of organisms in biological databases.     

Genoviz Software Development Kit: Java tool kit for building genomics visualization applications

Background  

Visualization software can expose previously undiscovered patterns in genomic data and advance biological science.     

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 .     

Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research

SUMMARY: We present here Blast2GO (B2G), a research tool designed with the main purpose of enabling Gene Ontology (GO) based data mining on sequence data for which no GO annotation is yet available. B2G joints in one application GO annotation based on similarity searches with statistical analysis and highlighted visualization on directed acyclic graphs. This tool offers a suitable platform for functional genomics research in non-model species. B2G is an intuitive and interactive desktop application that allows monitoring and comprehension of the whole annotation and analysis process. AVAILABILITY: Blast2GO is freely available via Java Web Start at http://www.blast2go.de. SUPPLEMENTARY MATERIAL: http://www.blast2go.de -> Evaluation.     

scdNet: a computational tool for single-cell differential network analysis

Background

Single-cell RNA sequencing (scRNA-Seq) is an emerging technology that has revolutionized the research of the tumor heterogeneity. However, the highly sparse data matrices generated by the technology have posed an obstacle to the analysis of differential gene regulatory networks.

Results

Addressing the challenges, this study presents, as far as we know, the first bioinformatics tool for scRNA-Seq-based differential network analysis (scdNet). The tool features a sample size adjustment of gene-gene correlation, comparison of inter-state correlations, and construction of differential networks. A simulation analysis demonstrated the power of scdNet in the analyses of sparse scRNA-Seq data matrices, with low requirement on the sample size, high computation efficiency, and tolerance of sequencing noises. Applying the tool to analyze two datasets of single circulating tumor cells (CTCs) of prostate cancer and early mouse embryos, our data demonstrated that differential gene regulation plays crucial roles in anti-androgen resistance and early embryonic development.

Conclusions

Overall, the tool is widely applicable to datasets generated by the emerging technology to bring biological insights into tumor heterogeneity and other studies. MATLAB implementation of scdNet is available at https://github.com/ChenLabGCCRI/scdNet.

     

Cytoscape 2.8: new features for data integration and network visualization

Cytoscape is a popular bioinformatics package for biological network visualization and data integration. Version 2.8 introduces two powerful new features--Custom Node Graphics and Attribute Equations--which can be used jointly to greatly enhance Cytoscape's data integration and visualization capabilities. Custom Node Graphics allow an image to be projected onto a node, including images generated dynamically or at remote locations. Attribute Equations provide Cytoscape with spreadsheet-like functionality in which the value of an attribute is computed dynamically as a function of other attributes and network properties. Availability and implementation: Cytoscape is a desktop Java application released under the Library Gnu Public License (LGPL). Binary install bundles and source code for Cytoscape 2.8 are available for download from http://cytoscape.org.