GeneVenn - A web application for comparing gene lists using Venn diagrams

Numerous methods are available to compare results of multiple microarray studies. One of the simplest but most effective of these procedures is to examine the overlap of resulting gene lists in a Venn diagram. Venn diagrams are graphical ways of representing interactions among sets to display information that can be read easily. Here we propose a simple but effective web application creating Venn diagrams from two or three gene lists. Each gene in the group list has link to the related information in NCBI's Entrez Nucleotide database. AVAILABILITY: GeneVenn is available for free at http://mcbc.usm.edu/genevenn/     

InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams

Background

Set comparisons permeate a large number of data analysis workflows, in particular workflows in biological sciences. Venn diagrams are frequently employed for such analysis but current tools are limited.

Results

We have developed InteractiVenn, a more flexible tool for interacting with Venn diagrams including up to six sets. It offers a clean interface for Venn diagram construction and enables analysis of set unions while preserving the shape of the diagram. Set unions are useful to reveal differences and similarities among sets and may be guided in our tool by a tree or by a list of set unions. The tool also allows obtaining subsets’ elements, saving and loading sets for further analyses, and exporting the diagram in vector and image formats. InteractiVenn has been used to analyze two biological datasets, but it may serve set analysis in a broad range of domains.

Conclusions

InteractiVenn allows set unions in Venn diagrams to be explored thoroughly, by consequence extending the ability to analyze combinations of sets with additional observations, yielded by novel interactions between joined sets. InteractiVenn is freely available online at: www.interactivenn.net.     

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/     

Generalized Venn diagrams: a new method of visualizing complex genetic set relations

MOTIVATION: Microarray experiments generate vast amounts of data. The unknown or only partially known functional context of differentially expressed genes may be assessed by querying the Gene Ontology database via GOMiner. Resulting tree representations are difficult to interpret and are not suited for visualization of this type of data. Methods are needed to effectively visualize these complex set relationships. RESULTS: We present a visualization approach for set relationships based on Venn diagrams. The proposed extension enhances the usual notion of Venn diagrams by incorporating set size information. The cardinality of the sets and intersection sets is represented by their corresponding circle (polygon) sizes. To avoid local minima, solutions to this problem are sought by evolutionary optimization. This generalized Venn diagram approach has been implemented as an interactive Java application (VennMaster) specifically designed for use with GOMiner in the context of the Gene Ontology database. AVAILABILITY: VennMaster is platform-independent (Java 1.4.2) and has been tested on Windows (XP, 2000), Mac OS X, and Linux. Supplementary information and the software (free for non-commercial use) are available at http://www.informatik.uni-ulm.de/ni/mitarbeiter/HKestler/vennm together with a user documentation. CONTACT: hans.kestler@medizin.uni-ulm.de.     

GeneXplorer: an interactive web application for microarray data visualization and analysis

Background

When publishing large-scale microarray datasets, it is of great value to create supplemental websites where either the full data, or selected subsets corresponding to figures within the paper, can be browsed. We set out to create a CGI application containing many of the features of some of the existing standalone software for the visualization of clustered microarray data.

Results

We present GeneXplorer, a web application for interactive microarray data visualization and analysis in a web environment. GeneXplorer allows users to browse a microarray dataset in an intuitive fashion. It provides simple access to microarray data over the Internet and uses only HTML and JavaScript to display graphic and annotation information. It provides radar and zoom views of the data, allows display of the nearest neighbors to a gene expression vector based on their Pearson correlations and provides the ability to search gene annotation fields.

Conclusions

The software is released under the permissive MIT Open Source license, and the complete documentation and the entire source code are freely available for download from CPAN http://search.cpan.org/dist/Microarray-GeneXplorer/.

     

Experience using web services for biological sequence analysis

Programmatic access to data and tools through the web using so-called web services has an important role to play in bioinformatics. In this article, we discuss the most popular approaches based on SOAP/WS-I and REST and describe our, a cross section of the community, experiences with providing and using web services in the context of biological sequence analysis. We briefly review main technological approaches as well as best practice hints that are useful for both users and developers. Finally, syntactic and semantic data integration issues with multiple web services are discussed.     

Fast protocol for radiochromic film dosimetry using a cloud computing web application

PurposeTo investigate the feasibility of a fast protocol for radiochromic film dosimetry to verify intensity-modulated radiotherapy (IMRT) plans.Method and materialsEBT3 film dosimetry was conducted in this study using the triple-channel method implemented in the cloud computing application (Radiochromic.com). We described a fast protocol for radiochromic film dosimetry to obtain measurement results within 1 h.Ten IMRT plans were delivered to evaluate the feasibility of the fast protocol. The dose distribution of the verification film was derived at 15, 30, 45 min using the fast protocol and also at 24 h after completing the irradiation. The four dose maps obtained per plan were compared using global and local gamma index (5%/3 mm) with the calculated one by the treatment planning system. Gamma passing rates obtained for 15, 30 and 45 min post-exposure were compared with those obtained after 24 h.ResultsSmall differences respect to the 24 h protocol were found in the gamma passing rates obtained for films digitized at 15 min (global: 99.6% ± 0.9% vs. 99.7% ± 0.5%; local: 96.3% ± 3.4% vs. 96.3% ± 3.8%), at 30 min (global: 99.5% ± 0.9% vs. 99.7% ± 0.5%; local: 96.5% ± 3.2% vs. 96.3 ± 3.8%) and at 45 min (global: 99.2% ± 1.5% vs. 99.7% ± 0.5%; local: 96.1% ± 3.8% vs. 96.3 ± 3.8%).ConclusionsThe fast protocol permits dosimetric results within 1 h when IMRT plans are verified, with similar results as those reported by the standard 24 h protocol.     

GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists

Background

Although expression microarrays have become a standard tool used by biologists, analysis of data produced by microarray experiments may still present challenges. Comparison of data from different platforms, organisms, and labs may involve complicated data processing, and inferring relationships between genes remains difficult.

Results

S TAR N ET 2 is a new web-based tool that allows post hoc visual analysis of correlations that are derived from expression microarray data. S TAR N ET 2 facilitates user discovery of putative gene regulatory networks in a variety of species (human, rat, mouse, chicken, zebrafish, Drosophila, C. elegans, S. cerevisiae, Arabidopsis and rice) by graphing networks of genes that are closely co-expressed across a large heterogeneous set of preselected microarray experiments. For each of the represented organisms, raw microarray data were retrieved from NCBI's Gene Expression Omnibus for a selected Affymetrix platform. All pairwise Pearson correlation coefficients were computed for expression profiles measured on each platform, respectively. These precompiled results were stored in a MySQL database, and supplemented by additional data retrieved from NCBI. A web-based tool allows user-specified queries of the database, centered at a gene of interest. The result of a query includes graphs of correlation networks, graphs of known interactions involving genes and gene products that are present in the correlation networks, and initial statistical analyses. Two analyses may be performed in parallel to compare networks, which is facilitated by the new H EAT S EEKER module.

Conclusion

S TAR N ET 2 is a useful tool for developing new hypotheses about regulatory relationships between genes and gene products, and has coverage for 10 species. Interpretation of the correlation networks is supported with a database of previously documented interactions, a test for enrichment of Gene Ontology terms, and heat maps of correlation distances that may be used to compare two networks. The list of genes in a S TAR N ET network may be useful in developing a list of candidate genes to use for the inference of causal networks. The tool is freely available at http://vanburenlab.medicine.tamhsc.edu/starnet2.html, and does not require user registration.     

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.     

Using process diagrams for the graphical representation of biological networks

With the increased interest in understanding biological networks, such as protein-protein interaction networks and gene regulatory networks, methods for representing and communicating such networks in both human- and machine-readable form have become increasingly important. Although there has been significant progress in machine-readable representation of networks, as exemplified by the Systems Biology Mark-up Language (SBML) (http://www.sbml.org) issues in human-readable representation have been largely ignored. This article discusses human-readable diagrammatic representations and proposes a set of notations that enhances the formality and richness of the information represented. The process diagram is a fully state transition-based diagram that can be translated into machine-readable forms such as SBML in a straightforward way. It is supported by CellDesigner, a diagrammatic network editing software (http://www.celldesigner.org/), and has been used to represent a variety of networks of various sizes (from only a few components to several hundred components).     

GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists

Background  

Since the inception of the GO annotation project, a variety of tools have been developed that support exploring and searching the GO database. In particular, a variety of tools that perform GO enrichment analysis are currently available. Most of these tools require as input a target set of genes and a background set and seek enrichment in the target set compared to the background set. A few tools also exist that support analyzing ranked lists. The latter typically rely on simulations or on union-bound correction for assigning statistical significance to the results.     

GenomeComp: a visualization tool for microbial genome comparison

We have developed a software tool, GenomeComp, for summarizing, parsing and visualizing the genome sequences comparison results derived from voluminous BLAST textual output. With GenomeComp, the variation between genomes can be easily highlighted, such as repeat regions, insertions, deletions and rearrangements of genomic segments. This software provides a new visualizing tool for microbe comparative genomics.     

Analysis and visualization of H7 influenza using genomic,evolutionary and geographic information in a modular web service

We have reported previously on use of a web‐based application, Supramap ( http://supramap.org ) for the study of biogeographic, genotypic, and phenotypic evolution. Using Supramap we have developed maps of the spread of drug‐resistant influenza and host shifts in H1N1 and H5N1 influenza and coronaviruses such as SARS. Here we report on another zoonotic pathogen, H7 influenza, and provide an update on the implementation of Supramap as a web service. We find that the emergence of pathogenic strains of H7 is labile with many transitions from high to low pathogenicity, and from low to high pathogenicity. We use Supramap to put these events in a temporal and geospatial context. We identify several lineages of H7 influenza with biomarkers of high pathogenicity in regions that have not been reported in the scientific literature. The original implementation of Supramap was built with tightly coupled client and server software. Now we have decoupled the components to provide a modular web service for POY ( http://poyws.org ) that can be consumed by a data provider to create a novel application. To demonstrate the web service, we have produced an application, Geogenes ( http://geogenes.org ). Unlike in Supramap, in which the user is required to create and upload data files, in Geogenes the user works from a graphical interface to query an underlying dataset. Geogenes demonstrates how the web service can provide underlying processing for any sequence and metadata database. © The Willi Hennig Society 2012.     

JColorGrid: software for the visualization of biological measurements

Background  

Two-dimensional data colourings are an effective medium by which to represent three-dimensional data in two dimensions. Such "color-grid" representations have found increasing use in the biological sciences (e.g. microarray 'heat maps' and bioactivity data) as they are particularly suited to complex data sets and offer an alternative to the graphical representations included in traditional statistical software packages. The effectiveness of color-grids lies in their graphical design, which introduces a standard for customizable data representation. Currently, software applications capable of generating limited color-grid representations can be found only in advanced statistical packages or custom programs (e.g. micro-array analysis tools), often associated with steep learning curves and requiring expert knowledge.     

BLogo: a tool for visualization of bias in biological sequences

Blogo is a web-based tool that detects and displays statistically significant position-specific sequence bias with reduced background noise. The over-represented and under-represented symbols in a particular position are shown above and below the zero line. When the sequences are in open reading frames, the background frequency of nucleotides could be calculated separately for the three positions of a codon, thus greatly reducing the background noise. The chi(2)-test or Fisher's exact test is used to evaluate the statistical significance of every symbol in every position and only those that are significant are highlighted in the resulting logo. The perl source code of the program is freely available and can be run locally. AVAILABILITY: http://acephpx.cropdb.org/blogo/, http://www.bioinformatics.org/blogo/.