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The European sardine (Sardina pilchardus, Walbaum 1792) is indisputably a commercially important species. Previous studies using uneven sampling or a limited number of makers have presented sometimes conflicting evidence of the genetic structure of S. pilchardus populations. Here, we show that whole genome data from 108 individuals from 16 sampling areas across 5000 km of the species' distribution range (from the Eastern Mediterranean to the archipelago of Azores) support at least three genetic clusters. One includes individuals from Azores and Madeira, with evidence of substructure separating these two archipelagos in the Atlantic. Another cluster broadly corresponds to the center of the distribution, including the sampling sites around Iberia, separated by the Almeria–Oran front from the third cluster that includes all of the Mediterranean samples, except those from the Alboran Sea. Individuals from the Canary Islands appear to belong to the Mediterranean cluster. This suggests at least two important geographical barriers to gene flow, even though these do not seem complete, with many individuals from around Iberia and the Mediterranean showing some patterns compatible with admixture with other genetic clusters. Genomic regions corresponding to the top outliers of genetic differentiation are located in areas of low recombination indicative that genetic architecture also has a role in shaping population structure. These regions include genes related to otolith formation, a calcium carbonate structure in the inner ear previously used to distinguish S. pilchardus populations. Our results provide a baseline for further characterization of physical and genetic barriers that divide European sardine populations, and information for transnational stock management of this highly exploited species towards sustainable fisheries. [ABSTRACT FROM AUTHOR]

Virus discovery from high throughput sequencing data often follows a bottom-up approach where taxonomic annotation takes place prior to association to disease. Albeit effective in some cases, the approach fails to detect novel pathogens and remote variants not present in reference databases. We have developed a species independent pipeline that utilises sequence clustering for the identification of nucleotide sequences that co-occur across multiple sequencing data instances. We applied the workflow to 686 sequencing libraries from 252 cancer samples of different cancer and tissue types, 32 non-template controls, and 24 test samples. Recurrent sequences were statistically associated to biological, methodological or technical features with the aim to identify novel pathogens or plausible contaminants that may associate to a particular kit or method. We provide examples of identified inhabitants of the healthy tissue flora as well as experimental contaminants. Unmapped sequences that co-occur with high statistical significance potentially represent the unknown sequence space where novel pathogens can be identified. [ABSTRACT FROM AUTHOR]