In the last decade, different omic technologies have experiences an exponential technological advancement. However, metabolomics has not followed a similarly vertiginous improving improvement and are far from genomics or transcriptomics in terms of throughput, cost and even accuracy. Therefore, genome-scale in-silico methodologies to estimate metabolic activities from genomic data constitute an active field. The solutions available fall into two extremes: those with few assumptions about the relationships among proteins and metabolites, which are easy-to-use but less accurate and those that account for the complex relationships among molecules and proteins defined in the metabolic pathways, which are more accurate but require mathematical skills. Here, we introduce Metabolica, an algorithm that considers the complex functional relationships among all the molecules and proteins involved in the metabolic pathway analyzed but keeping an easy use that do not require of advanced mathematical skills. Metabolica has been implemented in a freely available software R package. The software inputs transcriptomic data and infers the activities of the reactions that produce the different metabolites in the pathway analyzed. An example shows how detected dysregulated metabolites in several cancers are related to patient survival.

During the last decades West Nile Virus (WNV) outbreaks have continuously occurred in the Mediterranean area. In August 2020 a new WNV outbreak affected 71 people with meningoencephalitis in Andalusia and 6 more cases in Extremadura (south-west of Spain), causing a total of eight deaths. The whole genomes of four viral isolates were obtained and phylogenetically analyzed in the context of recent outbreaks. The Andalusian viral samples belonged to the lineage 1 and were relatively similar to previous outbreaks occurred in the Mediterranean region. Here we present a detailed analysis of the outbreak, including an extensive phylogenetic study.