We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS‐CoV‐2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large‐scale community effort to build an open access, interoperable and computable repository of COVID‐19 molecular mechanisms. The COVID‐19 Disease Map (C19DMap) is a graphical, interactive representation of disease‐relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph‐based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS‐CoV‐2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID‐19 or similar pandemics in the long‐term perspective.

Breast cancer is rarely diagnosed in very young women (35years old or younger), and it often presents with distinct clinical-pathological features related to a more aggressive phenotype and worse prognosis when diagnosed at this early age. A pending question is whether breast cancer in very young women arises from the deregulation of different underlying mechanisms, something that will make this disease an entity differentiated from breast cancer diagnosed in older patients.

Background: Few high penetrance genes are known in Malignant Melanoma (MM), however, the involvement of low-penetrance genes such as MC1R, OCA2, ASIP, SLC45A2 and TYR has been observed. Lately, genome-wide association studies (GWAS) have been the ideal strategy to identify new common, low-penetrance susceptibility loci. In this case–control study, we try to validate in our population nine melanoma associated markers selected from published GWAS in melanoma predisposition. Methods: We genotyped the 9 markers corresponding to 8 genes (PARP1, MX2, ATM, CCND1, NADSYN1, CASP8, IRF4 and CYP2R1) in 566 cases and 347 controls from a Spanish population using KASPar probes. Genotypes were analyzed by logistic regression and adjusted by phenotypic characteristics. Results: We confirm the protective role in MM of the rs3219090 located on the PARP1 gene (p-value 0.027). Additionally, this SNP was also associated with eye color (p-value 0.002). A second polymorphism, rs12203592, located on the IRF4 gene was associated with protection to develop MM for the dominant model (p-value 0.037). We have also observed an association of this SNP with both lentigines (p-value 0.014) and light eye color (p-value 3.76 × 10^-4). Furthermore, we detected a novel association with rs1485993, located on the CCND1 gene, and dark eye color (p-value 4.96 × 10^-4). Finally, rs1801516, located on the ATM gene, showed a trend towards a protective role in MM similar to the one firstly described in a GWAS study. Conclusions: To our knowledge, this is the first time that these SNPs have been associated with MM in a Spanish population. We confirmed the proposed role of rs3219090, located on the PARP1 gene, and rs12203592, located on the IRF4 gene, as protective to MM along the same lines as have previous genome-wide associated works. Finally, we have seen associations between IRF4, PARP1, and CCND1 and phenotypic characteristics, confirming previous results for the IRF4 gene and presenting novel data for the last two, suggesting that pigmentation characteristics correlated with eye color are potential mediators between PARP1 and MM protection.