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.     

VENNTURE--a novel Venn diagram investigational tool for multiple pharmacological dataset analysis

As pharmacological data sets become increasingly large and complex, new visual analysis and filtering programs are needed to aid their appreciation. One of the most commonly used methods for visualizing biological data is the Venn diagram. Currently used Venn analysis software often presents multiple problems to biological scientists, in that only a limited number of simultaneous data sets can be analyzed. An improved appreciation of the connectivity between multiple, highly-complex datasets is crucial for the next generation of data analysis of genomic and proteomic data streams. We describe the development of VENNTURE, a program that facilitates visualization of up to six datasets in a user-friendly manner. This program includes versatile output features, where grouped data points can be easily exported into a spreadsheet. To demonstrate its unique experimental utility we applied VENNTURE to a highly complex parallel paradigm, i.e. comparison of multiple G protein-coupled receptor drug dose phosphoproteomic data, in multiple cellular physiological contexts. VENNTURE was able to reliably and simply dissect six complex data sets into easily identifiable groups for straightforward analysis and data output. Applied to complex pharmacological datasets, VENNTURE's improved features and ease of analysis are much improved over currently available Venn diagram programs. VENNTURE enabled the delineation of highly complex patterns of dose-dependent G protein-coupled receptor activity and its dependence on physiological cellular contexts. This study highlights the potential for such a program in fields such as pharmacology, genomics, and bioinformatics.     

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/     

VennDIS: A JavaFX‐based Venn and Euler diagram software to generate publication quality figures

Venn diagrams are graphical representations of the relationships among multiple sets of objects and are often used to illustrate similarities and differences among genomic and proteomic datasets. All currently existing tools for producing Venn diagrams evince one of two traits; they require expertise in specific statistical software packages (such as R), or lack the flexibility required to produce publication‐quality figures. We describe a simple tool that addresses both shortcomings, Venn Diagram Interactive Software (VennDIS), a JavaFX‐based solution for producing highly customizable, publication‐quality Venn, and Euler diagrams of up to five sets. The strengths of VennDIS are its simple graphical user interface and its large array of customization options, including the ability to modify attributes such as font, style and position of the labels, background color, size of the circle/ellipse, and outline color. It is platform independent and provides real‐time visualization of figure modifications. The created figures can be saved as XML files for future modification or exported as high‐resolution images for direct use in publications.     

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.     

jvenn: an interactive Venn diagram viewer

Background

Venn diagrams are commonly used to display list comparison. In biology, they are widely used to show the differences between gene lists originating from different differential analyses, for instance. They thus allow the comparison between different experimental conditions or between different methods. However, when the number of input lists exceeds four, the diagram becomes difficult to read. Alternative layouts and dynamic display features can improve its use and its readability.

Results

jvenn is a new JavaScript library. It processes lists and produces Venn diagrams. It handles up to six input lists and presents results using classical or Edwards-Venn layouts. User interactions can be controlled and customized. Finally, jvenn can easily be embeded in a web page, allowing to have dynamic Venn diagrams.

Conclusions

jvenn is an open source component for web environments helping scientists to analyze their data. The library package, which comes with full documentation and an example, is freely available at http://bioinfo.genotoul.fr/jvenn.     

Building protein diagrams on the web with the residue-based diagram editor RbDe

The residue-based diagram editor (RbDe) is web-based software that greatly simplifies the construction of schematic diagrams of proteins. Residue-based diagrams display the sequence of a given protein in the context of its secondary and tertiary structure. Such diagrams are frequently used to summarize mutations or sequence features, in the context of the overall topology of a protein. The initial version of RbDe was designed for transmembrane proteins and has enabled many users to create diagrams of large systems such as G protein-coupled receptors or transporters. We present an extended diagram editor that supports other families of proteins. Users can now import custom-diagram layouts, use them to render members of any protein family and generate high-quality output for publication purposes. RbDe is available free over the web, at http://icb.mssm.edu/crt/RbDe     

eulerAPE: Drawing Area-Proportional 3-Venn Diagrams Using Ellipses

Venn diagrams with three curves are used extensively in various medical and scientific disciplines to visualize relationships between data sets and facilitate data analysis. The area of the regions formed by the overlapping curves is often directly proportional to the cardinality of the depicted set relation or any other related quantitative data. Drawing these diagrams manually is difficult and current automatic drawing methods do not always produce appropriate diagrams. Most methods depict the data sets as circles, as they perceptually pop out as complete distinct objects due to their smoothness and regularity. However, circles cannot draw accurate diagrams for most 3-set data and so the generated diagrams often have misleading region areas. Other methods use polygons to draw accurate diagrams. However, polygons are non-smooth and non-symmetric, so the curves are not easily distinguishable and the diagrams are difficult to comprehend. Ellipses are more flexible than circles and are similarly smooth, but none of the current automatic drawing methods use ellipses. We present eulerAPE as the first method and software that uses ellipses for automatically drawing accurate area-proportional Venn diagrams for 3-set data. We describe the drawing method adopted by eulerAPE and we discuss our evaluation of the effectiveness of eulerAPE and ellipses for drawing random 3-set data. We compare eulerAPE and various other methods that are currently available and we discuss differences between their generated diagrams in terms of accuracy and ease of understanding for real world data.     

Textrous!: Extracting Semantic Textual Meaning from Gene Sets

The un-biased and reproducible interpretation of high-content gene sets from large-scale genomic experiments is crucial to the understanding of biological themes, validation of experimental data, and the eventual development of plans for future experimentation. To derive biomedically-relevant information from simple gene lists, a mathematical association to scientific language and meaningful words or sentences is crucial. Unfortunately, existing software for deriving meaningful and easily-appreciable scientific textual ‘tokens’ from large gene sets either rely on controlled vocabularies (Medical Subject Headings, Gene Ontology, BioCarta) or employ Boolean text searching and co-occurrence models that are incapable of detecting indirect links in the literature. As an improvement to existing web-based informatic tools, we have developed Textrous!, a web-based framework for the extraction of biomedical semantic meaning from a given input gene set of arbitrary length. Textrous! employs natural language processing techniques, including latent semantic indexing (LSI), sentence splitting, word tokenization, parts-of-speech tagging, and noun-phrase chunking, to mine MEDLINE abstracts, PubMed Central articles, articles from the Online Mendelian Inheritance in Man (OMIM), and Mammalian Phenotype annotation obtained from Jackson Laboratories. Textrous! has the ability to generate meaningful output data with even very small input datasets, using two different text extraction methodologies (collective and individual) for the selecting, ranking, clustering, and visualization of English words obtained from the user data. Textrous!, therefore, is able to facilitate the output of quantitatively significant and easily appreciable semantic words and phrases linked to both individual gene and batch genomic data.     

GSDS: 基因结构显示系统

构建了一个用于绘制基因结构示意图的网站系统(http://gsds.cbi.pku.edu.cn/)。用户可提交核酸序列、NCBI核酸序列号或基因外显子位置信息, 得到基因结构示意图; 并可指定在基因结构图上标注某些特定区域。系统允许用户同时输入多个基因, 并指定输出次序和标注区域。结果可用位图和矢量图两种图形格式显示。点击位图格式结果, 可以查看相应序列。系统提供中英文两种用户界面。     

Network-Based Meta-Analyses of Associations of Multiple Gene Expression Profiles with Bone Mineral Density Variations in Women

Background

Existing microarray studies of bone mineral density (BMD) have been critical for understanding the pathophysiology of osteoporosis, and have identified a number of candidate genes. However, these studies were limited by their relatively small sample sizes and were usually analyzed individually. Here, we propose a novel network-based meta-analysis approach that combines data across six microarray studies to identify functional modules from human protein-protein interaction (PPI) data, and highlight several differentially expressed genes (DEGs) and a functional module that may play an important role in BMD regulation in women.

Methods

Expression profiling studies were identified by searching PubMed, Gene Expression Omnibus (GEO) and ArrayExpress. Two meta-analysis methods were applied across different gene expression profiling studies. The first, a nonparametric Fisher’s method, combined p-values from individual experiments to identify genes with large effect sizes. The second method combined effect sizes from individual datasets into a meta-effect size to gain a higher precision of effect size estimation across all datasets. Genes with Q test’s p-values < 0.05 or I² values > 50% were assessed by a random effects model and the remainder by a fixed effects model. Using Fisher’s combined p-values, functional modules were identified through an integrated analysis of microarray data in the context of large protein–protein interaction (PPI) networks. Two previously published meta-analysis studies of genome-wide association (GWA) datasets were used to determine whether these module genes were genetically associated with BMD. Pathway enrichment analysis was performed with a hypergeometric test.

Results

Six gene expression datasets were identified, which included a total of 249 (129 high BMD and 120 low BMD) female subjects. Using a network-based meta-analysis, a consensus module containing 58 genes (nodes) and 83 edges was detected. Pathway enrichment analysis of the 58 module genes revealed that these genes were enriched in several important KEGG pathways including Osteoclast differentiation, B cell receptor signaling pathway, MAPK signaling pathway, Chemokine signaling pathway and Insulin signaling pathway. The importance of module genes was replicated by demonstrating that most module genes were genetically associated with BMD in the GWAS data sets. Meta-analyses were performed at the individual gene level by combining p-values and effect sizes. Five candidate genes (ESR1, MAP3K3, PYGM, RAC1 and SYK) were identified based on gene expression meta-analysis, and their associations with BMD were also replicated by two BMD meta-analysis studies.

Conclusions

In summary, our network-based meta-analysis not only identified important differentially expressed genes but also discovered biologically meaningful functional modules for BMD determination. Our study may provide novel therapeutic targets for osteoporosis in women.     

Venn Mapping: clustering of heterologous microarray data based on the number of co-occurring differentially expressed genes

MOTIVATION: To evaluate microarray data, clustering is widely used to group biological samples or genes. However, problems arise when comparing heterologous databases. As the clustering algorithm searches for similarities between experiments, it will most likely first separate the data sets, masking relationships that exist between samples from different databases. RESULTS: We developed a program, Venn Mapper, to calculate the statistical significance of the number of co-occurring differentially expressed genes in any of the two experiments. For proof of principle, we analysed a heterologous data set of 170 microarrays including breast and prostate cancer microarray analyses. Significant overlap was found in an unsupervised analysis between metastasized prostate cancer and metastasized breast cancer and BRCA mutated breast cancer. A comparison between single microarray data and the averaged breast and prostate data sets was also evaluated. This analysis suggests that genes expressed higher in stromal cells are also implicated in metastatic prostate cancer and BRCA mutated breast cancer. The Venn Mapper program identifies overlaps between samples from heterologous data sets and directly extracts the genes responsible for the overlap. From this information novel biological hypotheses may be addressed. AVAILABILITY: Venn Mapper is freely available on http://www.erasmusmc.nl/gatcplatform. SUPPLEMENTARY INFORMATION: http://www.erasmusmc.nl/gatcplatform/vennmapper.html.     

ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences

A computer program, ARAGORN, identifies tRNA and tmRNA genes. The program employs heuristic algorithms to predict tRNA secondary structure, based on homology with recognized tRNA consensus sequences and ability to form a base-paired cloverleaf. tmRNA genes are identified using a modified version of the BRUCE program. ARAGORN achieves a detection sensitivity of 99% from a set of 1290 eubacterial, eukaryotic and archaeal tRNA genes and detects all complete tmRNA sequences in the tmRNA database, improving on the performance of the BRUCE program. Recently discovered tmRNA genes in the chloroplasts of two species from the ‘green’ algae lineage are detected. The output of the program reports the proposed tRNA secondary structure and, for tmRNA genes, the secondary structure of the tRNA domain, the tmRNA gene sequence, the tag peptide and a list of organisms with matching tmRNA peptide tags.     

Extending pathways based on gene lists using InterPro domain signatures

Background

High-throughput technologies like functional screens and gene expression analysis produce extended lists of candidate genes. Gene-Set Enrichment Analysis is a commonly used and well established technique to test for the statistically significant over-representation of particular pathways. A shortcoming of this method is however, that most genes that are investigated in the experiments have very sparse functional or pathway annotation and therefore cannot be the target of such an analysis. The approach presented here aims to assign lists of genes with limited annotation to previously described functional gene collections or pathways. This works by comparing InterPro domain signatures of the candidate gene lists with domain signatures of gene sets derived from known classifications, e.g. KEGG pathways.

Results

In order to validate our approach, we designed a simulation study. Based on all pathways available in the KEGG database, we create test gene lists by randomly selecting pathway genes, removing these genes from the known pathways and adding variable amounts of noise in the form of genes not annotated to the pathway. We show that we can recover pathway memberships based on the simulated gene lists with high accuracy. We further demonstrate the applicability of our approach on a biological example.

Conclusion

Results based on simulation and data analysis show that domain based pathway enrichment analysis is a very sensitive method to test for enrichment of pathways in sparsely annotated lists of genes. An R based software package domainsignatures, to routinely perform this analysis on the results of high-throughput screening, is available via Bioconductor.     

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble’s output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) − k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer’s disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases.     

Seeking unique and common biological themes in multiple gene lists or datasets: pathway pattern extraction pipeline for pathway-level comparative analysis

Background  

One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene lists and then subject these genes to enrichment analysis to reveal the underlying biology. However, the capacity of this approach is largely restricted by the limited number of common genes shared by datasets from multiple experiments, which could be caused by the complexity of the biological system itself.