TagSmart: analysis and visualization for yeast mutant fitness data measured by tag microarrays

Background  

A nearly complete collection of gene-deletion mutants (96% of annotated open reading frames) of the yeast Saccharomyces cerevisiae has been systematically constructed. Tag microarrays are widely used to measure the fitness of each mutant in a mutant mixture. The tag array experiments can have a complex experimental design, such as time course measurements and drug treatment with multiple dosages.     

Integrated functional visualization of eukaryotic genomes

Background  

Increasing amounts of data from large scale whole genome analysis efforts demands convenient tools for manipulation, visualization and investigation. Whole genome plots offer an intuitive window to the analysis. We describe two applications that enable users to easily plot and explore whole genome data from their own or other researchers' experiments.     

SpA: web-accessible spectratype analysis: data management, statistical analysis and visualization

SUMMARY: SpA is a web-accessible system for the management, visualization and statistical analysis of T-cell receptor spectratype data. Users upload data from their spectratype analyzers to SpA, which saves the raw data and user-defined supplementary covariates to a secure database. The statistical engine performs several data analyses and statistical summaries. The visualization engine displays spectratype histograms in a Java applet and in an image file suitable for download. All of these results are also saved to the database and remain accessible to the user. Additional statistical tools specific to the analysis of multiple spectratypes are also available through the SpA interface. AVAILABILITY: The service is freely accessible via the web at http://www.duke.edu/~kepler/spa.html. Additional technical support and specialized statistical analysis and consultation are available by arrangement with the authors and, depending on the service requested, may be subject to fee.     

GenomeGraphs: integrated genomic data visualization with R

Background  

Biological studies involve a growing number of distinct high-throughput experiments to characterize samples of interest. There is a lack of methods to visualize these different genomic datasets in a versatile manner. In addition, genomic data analysis requires integrated visualization of experimental data along with constantly changing genomic annotation and statistical analyses.     

Analysis and visualization of chromosomal abnormalities in SNP data with SNPscan

Background  

A variety of diseases are caused by chromosomal abnormalities such as aneuploidies (having an abnormal number of chromosomes), microdeletions, microduplications, and uniparental disomy. High density single nucleotide polymorphism (SNP) microarrays provide information on chromosomal copy number changes, as well as genotype (heterozygosity and homozygosity). SNP array studies generate multiple types of data for each SNP site, some with more than 100,000 SNPs represented on each array. The identification of different classes of anomalies within SNP data has been challenging.     

Java Treeview--extensible visualization of microarray data

Open source software encourages innovation by allowing users to extend the functionality of existing applications. Treeview is a popular application for the visualization of microarray data, but is closed-source and platform-specific, which limits both its current utility and suitability as a platform for further development. Java Treeview is an open-source, cross-platform rewrite that handles very large datasets well, and supports extensions to the file format that allow the results of additional analysis to be visualized and compared. The combination of a general file format and open source makes Java Treeview an attractive choice for solving a class of visualization problems. An applet version is also available that can be used on any website with no special server-side setup.     

GAAS: gene array analyzer software for management,analysis and visualization of gene expression data

SUMMARY: GAAS, Gene Array Analyzer Software supports multi-user efficient management and suitable analyses of large amounts of gene expression data across replicated experiments. Its management framework handles input data generated by different technologies. A multi-user environment allows each user to store his/her own data visualization scheme, analysis parameters used, values and formats of the output data. The analysis engine performs: background and spot quality evaluation, data normalization, differential gene expression analyses in single and multiple replica experiments. Results of expression profiles can be interactively navigated through graphical interfaces and stored into output databases.     

A normalization strategy for comparing tag count data

Background

High-throughput sequencing, such as ribonucleic acid sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) analyses, enables various features of organisms to be compared through tag counts. Recent studies have demonstrated that the normalization step for RNA-seq data is critical for a more accurate subsequent analysis of differential gene expression. Development of a more robust normalization method is desirable for identifying the true difference in tag count data.

Results

We describe a strategy for normalizing tag count data, focusing on RNA-seq. The key concept is to remove data assigned as potential differentially expressed genes (DEGs) before calculating the normalization factor. Several R packages for identifying DEGs are currently available, and each package uses its own normalization method and gene ranking algorithm. We compared a total of eight package combinations: four R packages (edgeR, DESeq, baySeq, and NBPSeq) with their default normalization settings and with our normalization strategy. Many synthetic datasets under various scenarios were evaluated on the basis of the area under the curve (AUC) as a measure for both sensitivity and specificity. We found that packages using our strategy in the data normalization step overall performed well. This result was also observed for a real experimental dataset.

Conclusion

Our results showed that the elimination of potential DEGs is essential for more accurate normalization of RNA-seq data. The concept of this normalization strategy can widely be applied to other types of tag count data and to microarray data.     

GeneTrack--a genomic data processing and visualization framework

MOTIVATION: High-throughput 'ChIP-chip' and 'ChIP-seq' methodologies generate sufficiently large data sets that analysis poses significant informatics challenges, particularly for research groups with modest computational support. To address this challenge, we devised a software platform for storing, analyzing and visualizing high resolution genome-wide binding data. GeneTrack automates several steps of a typical data processing pipeline, including smoothing and peak detection, and facilitates dissemination of the results via the web. Our software is freely available via the Google Project Hosting environment at http://genetrack.googlecode.com     

Integrated network reconstruction,visualization and analysis using YANAsquare

Background  

Modeling of metabolic networks includes tasks such as network assembly, network overview, calculation of metabolic fluxes and testing the robustness of the network.     

TableView: portable genomic data visualization

TableView is a generalized scientific visualization program for exploration of various biological data, including EST, SAGE, microarray and annotation data. Written in Java, TableView is portable, is easily used together with other software including DBMSs and is versatile enough to be applied to any tabular data AVAILABILITY: TableView is freely available at: http://ccgb.umn.edu/software/java/apps/TableView/.     

P-value based visualization of codon usage data

Two important and not yet solved problems in bacterial genome research are the identification of horizontally transferred genes and the prediction of gene expression levels. Both problems can be addressed by multivariate analysis of codon usage data. In particular dimensionality reduction methods for visualization of multivariate data have shown to be effective tools for codon usage analysis. We here propose a multidimensional scaling approach using a novel similarity measure for codon usage tables. Our probabilistic similarity measure is based on P-values derived from the well-known chi-square test for comparison of two distributions. Experimental results on four microbial genomes indicate that the new method is well-suited for the analysis of horizontal gene transfer and translational selection. As compared with the widely-used correspondence analysis, our method did not suffer from outlier sensitivity and showed a better clustering of putative alien genes in most cases.     

Post-normalization quality assessment visualization of microarray data

Post-normalization checking of microarrays rarely occurs, despite the problems that using unreliable data for inference can cause. This paper considers a number of different ways to check microarrays after normalization for a variety of potential problems. Four types of problem with microarray data that these checks can identify are: clerical mistakes, array-wide hybridization problems, problems with normalization and mishandling problems. Any of these can seriously affect the results of any analysis. The three main techniques used to identify these problems are dimension reduction techniques, false array plots and correlograms. None of the techniques are computationally very intensive and all can be carried out in the R statistical package. Once discovered, problems can either be rectified or excluded from the data.     

Detection, annotation and visualization of alternative splicing from RNA-Seq data with SplicingViewer

Alternative splicing is a crucial mechanism by which diverse gene products can be generated from a limited number of genes, and is thought to be involved in complex orchestration of eukaryotic gene expression. Next-generation sequencing technologies, with reduced time and cost, provide unprecedented opportunities for deep interrogation of alternative splicing at the genome-wide scale. In this study, an integrated software SplicingViewer has been developed for unambiguous detection, annotation and visualization of splice junctions and alternative splicing events from RNA-Seq data. Specifically, it allows easy identification and characterization of splice junctions, and holds a versatile computational pipeline for in-depth annotation and classification of alternative splicing with different patterns. Moreover, it provides a user-friendly environment in which an alternative splicing landscape can be displayed in a straightforward and flexible manner. In conclusion, SplicingViewer can be widely used for studying alternative splicing easily and efficiently. SplicingViewer can be freely accessed at http://bioinformatics.zj.cn/splicingviewer.     

Software for quantitation and visualization of expression array data

Software is described that facilitates the analysis of phosphoimages from large array hybridizations. The Macintosh PowerPC-compatible application and its manual are available at no charge from http:?people.bu.edu/strehlow. The software is compatible with both custom formats and array filters from three commercial manufacturers. It allows the rapid quantitation of every spot on images of hybridizations to large arrays. The user drags grids of squares over the spots on the image to define the coordinates of each spot, then aligns and edits the position of the grid. The software then corrects the positions as necessary and quantitates up to 27,000 spots per image. It stores the numerical values for each signal in a format called the fingerprint file. Fingerprint files can be directly averaged or compared, allowing the user to find mean values or differences in data from independent hybridization experiments. Data can be recalled from the fingerprint file and can be output in a variety of spreadsheet formats with several options for background correction. Finally, the software offers an output format that allows the convenient visualization of data points using animated, three-dimensional graphs.     

Gene Expression Browser: large-scale and cross-experiment microarray data integration, management, search & visualization

Background

In the last decade, a large amount of microarray gene expression data has been accumulated in public repositories. Integrating and analyzing high-throughput gene expression data have become key activities for exploring gene functions, gene networks and biological pathways. Effectively utilizing these invaluable microarray data remains challenging due to a lack of powerful tools to integrate large-scale gene-expression information across diverse experiments and to search and visualize a large number of gene-expression data points.

Results

Gene Expression Browser is a microarray data integration, management and processing system with web-based search and visualization functions. An innovative method has been developed to define a treatment over a control for every microarray experiment to standardize and make microarray data from different experiments homogeneous. In the browser, data are pre-processed offline and the resulting data points are visualized online with a 2-layer dynamic web display. Users can view all treatments over control that affect the expression of a selected gene via Gene View, and view all genes that change in a selected treatment over control via treatment over control View. Users can also check the changes of expression profiles of a set of either the treatments over control or genes via Slide View. In addition, the relationships between genes and treatments over control are computed according to gene expression ratio and are shown as co-responsive genes and co-regulation treatments over control.

Conclusion

Gene Expression Browser is composed of a set of software tools, including a data extraction tool, a microarray data-management system, a data-annotation tool, a microarray data-processing pipeline, and a data search & visualization tool. The browser is deployed as a free public web service (http://www.ExpressionBrowser.com) that integrates 301 ATH1 gene microarray experiments from public data repositories (viz. the Gene Expression Omnibus repository at the National Center for Biotechnology Information and Nottingham Arabidopsis Stock Center). The set of Gene Expression Browser software tools can be easily applied to the large-scale expression data generated by other platforms and in other species.