BUSCADOR RECOLECTA

The cancer bioMarkers database is curated and maintained by several clinical and scientific experts in the field of precision oncology supported by the European Union’s Horizon 2020 funding. This database is currently being integrated with knowledge databases of other institutions in a collaborative effort of the Global Alliance for Genomics and Health.

Analyses somatic mutations in thousands of tumor genomes to identify cancer driver genes.

Gitools is a framework for analysis and visualization of multidimensional genomic data using interactive heat-maps.

A needle-plot (aka stem-plot or lollipop-plot) plots each data point as a big dot and adds a vertical line that makes it appear like a needle.

Machine-learning based approach to classify cancer driver genes into to Activating or Loss of Function roles for cancer gene development.

Javascript library to create interactive heatmaps within webpages.

The aim of SVGMap is helping in the visualisation of experimental data which are associated with some graphical representation. Thus SVGMap browser allows to generate images with colored areas corresponding to the chosen data and color scale./nThe data is represented as a table and is searchable. All data as well as the generated images/figures can be exported easily through the interface./nAdditionally the tool allows to manage (add, edit or delete) experiments and configure the front-end user search appearance such as the number of images to be displayed, the scale types to use and more.

This database contains information on the genes identified as drivers in Rubio-Perez and Tamborero et al. (2015). It contains information on driver identification at mutational, CNA and gene fusion level. Additional ancillary information about the role and major clonality of drivers is also included. A table is also provided with the list of datasets used for mutational driver identification.

Bioinformatics method to identify individual driver mutations.

This database contains the results of the driver analysis performed by the Cancer Genome Interpreter across 6,792 exomes of a pan-cancer cohort of 28 tumor types. Validated oncogenic mutations are identified according to the state-of-the-art clinical and experimental data, whereas the effect of the mutations of unknown significance is predicted by the OncodriveMUT method.