BUSCADOR RECOLECTA

Oceanographic data acquired during the ABRIC-4 Cruise (29GD20230310) on board the Research Vessel García del Cid in 2023., Assessment of Bottom‐trawling Resuspension Impacts in deep benthic Communities (ABRIC)

Oceanographic data acquired during the ARCO Cruise (29GD20230626) on board the Research Vessel García del Cid in 2023., Assessment of fishing grounds Restoration after a Change of bottom trawling Otter boards (ARCO). The main objective of the ARCO project is to study the current sedimentary processes related with trawling activity in the Palamós submarine canyon after the change of trawl doors by the Palamós harbor fleet. The impacts of this type of fishing in deep-sea environments are considered very high due to their intensity and recurrence, and to the fact that sediment dynamics are less energetic and the benthonic ecosystems are more vulnerable and have less resilience than in neritic or shelf environments

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.

A tool aimed at the rational design of cancer gene panels. It estimates the cost-effectiveness of the designed panel on a cohort of tumors and provides reports on the importance of individual mutations for tumorigenesis or therapy.

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.

Method to identify genomic regions, both coding and non-coding, bearing mutations with significant shift towards high functional impact across a cohort of tumos (FMbias), which are candidates to function as cancer drivers, through a local test.

OncodriveCLUST is a method aimed to identify genes whose mutations are biased towards a large spatial clustering. This method is designed to exploit the feature that mutations in cancer genes, especially oncogenes, often cluster in particular positions of the protein. We consider this as a sign that mutations in these regions change the function of these proteins in a manner that provides an adaptive advantage to cancer cells and consequently are positively selected during clonal evolution of tumours, and this property can thus be used to nominate novel candidate driver genes./nThe method does not assume that the baseline mutation probability is homogeneous across all gene positions but it creates a background model using silent mutations. Coding silent mutations are supposed to be under no positive selection and may reflect the baseline clustering of somatic mutations. Given recent evidences of non-random mutation processes along the genome, the assumption of homogenous mutation probabilities is likely an oversimplication introducing bias in the detection of meaningful events.

OncodriveFM detects candidate cancer driver genes and pathways from catalogs of somatic mutations in a cohort of tumors by computing the bias towards the accumulation of functional mutations (FM bias).This novel approach avoids some known limitations of recurrence-based approaches, such as the dif?culty to estimate background mutation rate, and the fact that they usually fail to identify lowly recurrently mutated driver genes.

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