SUMMARY: The generation of large volumes of omics data to conduct exploratory studies has become feasible and is now extensively used to gain new insights in life sciences. The effective exploration of the generated data by experts is a crucial step for the successful extraction of knowledge from these datasets. This requires availability of intuitive and interactive visualization tools that can display complex data. Matrix heatmaps are graphical representations frequently used for the description of complex omics data. Here, we present jHeatmap, a web-based tool that allows interactive matrix heatmap visualization and exploration. It is an adaptable javascript library designed to be embedded by means of basic coding skills into web portals to visualize data matrices as interactive and customizable heatmaps. AVAILABILITY: jHeatmap is freely available at the GitHub code repository at https://github.com/jheatmap/jheatmap. Working examples and the documentation may be found at http://jheatmap.github.io/jheatmap.

MOTIVATION: Several computational methods have been developed to identify cancer drivers genes-genes responsible for cancer development upon specific alterations. These alterations can cause the loss of function (LoF) of the gene product, for instance, in tumor suppressors, or increase or change its activity or function, if it is an oncogene. Distinguishing between these two classes is important to understand tumorigenesis in patients and has implications for therapy decision making. Here, we assess the capacity of multiple gene features related to the pattern of genomic alterations across tumors to distinguish between activating and LoF cancer genes, and we present an automated approach to aid the classification of novel cancer drivers according to their role. RESULT: OncodriveROLE is a machine learning-based approach that classifies driver genes according to their role, using several properties related to the pattern of alterations across tumors. The method shows an accuracy of 0.93 and Matthew's correlation coefficient of 0.84 classifying genes in the Cancer Gene Census. The OncodriveROLE classifier, its results when applied to two lists of predicted cancer drivers and TCGA-derived mutation and copy number features used by the classifier are available at http://bg.upf.edu/oncodrive-role. AVAILABILITY AND IMPLEMENTATION: The R implementation of the OncodriveROLE classifier is available at http://bg.upf.edu/oncodrive-role. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Spatial data visualization is very useful to represent biological data and quickly interpret the results. For instance, to show the expression pattern of a gene in different tissues of a fly, an intuitive approach is to draw the fly with the corresponding tissues and color the expression of the gene in each of them. However, the creation of these visual representations may be a burdensome task. Here we present SVGMap, a java application that automatizes the generation of high-quality graphics for singular data items (e.g. genes) and biological conditions. SVGMap contains a browser that allows the user to navigate the different images created and can be used as a web-based results publishing tool. AVAILABILITY: SVGMap is freely available as precompiled java package as well as source code at http://bg.upf.edu/svgmap. It requires Java 6 and any recent web browser with JavaScript enabled. The software can be run on Linux, Mac OS X and Windows systems. CONTACT: [email protected]