SUMMARY: MicroRNAs have been shown to be able to modulate the tumor microenvironment and the immune response and hence could be interesting biomarkers and therapeutic targets in immuno-oncology; however, dedicated analysis tools are missing. Here, we present a user-friendly web platform MIO and a Python toolkit miopy integrating various methods for visualization and analysis of provided or custom bulk microRNA and gene expression data. We include regularized regression and survival analysis and provide information of 40 microRNA target prediction tools as well as a collection of curated immune related gene and microRNA signatures and processed TCGA data including estimations of infiltrated immune cells and the immunophenoscore. The integration of several machine learning methods enables the selection of prognostic and predictive microRNAs and gene interaction network biomarkers. AVAILABILITY AND IMPLEMENTATION: https://mio.icbi.at, https://github.com/icbi-lab/mio and https://github.com/icbi-lab/miopy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: GPViz is a versatile Java-based software for dynamic gene-centered visualization of genomic regions and/or variants. User-defined data can be loaded in common formats as resulting from analysis workflows used in sequencing applications and studied in the context of the gene, the corresponding transcript isoforms, proteins and their domains or other protein features. Both the genomic regions and variants can be also defined interactively. Various gene filter options are provided to enable an intersection of variants, genomic regions and affected protein features. Finally, by using GPViz, we identified differentially expressed exons, which could indicate alternative splicing events, and found somatic variants in different cancer types affecting metabolic proteins. GPViz is freely available at http://icbi.at/gpviz (released under GNU general public license), is based on Java 7 and can be used as a stand-alone or Web Start application. AVAILABILITY: http://icbi.at/gpviz

Abstract Summary: We have developed ClueGO, an easy to use Cytoscape plug-in that strongly improves biological interpretation of large lists of genes. ClueGO integrates Gene Ontology (GO) terms as well as KEGG/BioCarta pathways and creates a functionally organized GO/pathway term network. It can analyze one or compare two lists of genes and comprehensively visualizes functionally grouped terms. A one-click update option allows ClueGO to automatically download the most recent GO/KEGG release at any time. ClueGO provides an intuitive representation of the analysis results and can be optionally used in conjunction with the GOlorize plug-in. Availability: http://www.ici.upmc.fr/cluego/cluegoDownload.shtml Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Microarray analysis has become a widely used technique for the study of gene-expression patterns on a genomic scale. As more and more laboratories are adopting microarray technology, there is a need for powerful and easy to use microarray databases facilitating array fabrication, labeling, hybridization, and data analysis. The wealth of data generated by this high throughput approach renders adequate database and analysis tools crucial for the pursuit of insights into the transcriptomic behavior of cells. RESULTS: MARS (Microarray Analysis and Retrieval System) provides a comprehensive MIAME supportive suite for storing, retrieving, and analyzing multi color microarray data. The system comprises a laboratory information management system (LIMS), a quality control management, as well as a sophisticated user management system. MARS is fully integrated into an analytical pipeline of microarray image analysis, normalization, gene expression clustering, and mapping of gene expression data onto biological pathways. The incorporation of ontologies and the use of MAGE-ML enables an export of studies stored in MARS to public repositories and other databases accepting these documents. CONCLUSION: We have developed an integrated system tailored to serve the specific needs of microarray based research projects using a unique fusion of Web based and standalone applications connected to the latest J2EE application server technology. The presented system is freely available for academic and non-profit institutions. More information can be found at http://genome.tugraz.at.

SUMMARY: ArrayNorm is a user-friendly, versatile and platform-independent Java application for the visualization, normalization and analysis of two-color microarray data. A variety of normalization options were implemented to remove the systematic and random errors in the data, taking into account the experimental design and the particularities of every slide. In addition, ArrayNorm provides a module for statistical identification of genes with significant changes in expression. AVAILABILITY: The package is freely available for academic and non-profit institutions from http://genome.tugraz.at

SUMMARY: A visual Java-based tool for drawing and annotating biological pathways was developed. This tool integrates the possibilities of charting elements with different attributes (size, color, labels), drawing connections between elements in distinct characteristics (color, structure, width, arrows), as well as adding links to molecular biology databases, promoter sequences, information on the function of the genes or gene products, and references. It is easy to use and system independent. The result of the editing process is a PNG (portable network graphics) file for the images and XML (extended markup language) file for the appropriate links.