Temporal variation of genetic diversity and differentiation of albacore, Thunnus alalunga, in the Mediterranean Sea

This study is on albacore (Thunnus alalunga, Bonnaterre 1788), an epi- and mesopelagic oceanic tuna species cosmopolitan in the tropical and temperate waters of all oceans including the Mediterranean Sea, extending in a broad band between 40°N and 40°S. What it’s known about albacore population structure is based on different studies that used fisheries data, RFLP, mtDNA control region and nuDNA markers, blood lectins analysis, individual tags and microsatellite. At the moment, for T. alalunga six management units are recognized: the North Pacific, South Pacific, Indian, North Atlantic, South Atlantic and Mediterranean stocks. In this study I have done a temporal and spatial comparison of genetic variability between different Mediterranean populations of Thunnus alalunga matching an historical dataset ca. from 1920s composed of 43 individuals divided in 3 populations (NADR, SPAIN and CMED) with a modern dataset composed of 254 individuals and 7 populations (BAL, CYP, LIG, TYR, TUR, ADR, ALB). The investigation was possible using a panel of 94 nuclear SNPs, built specifically for the target species at the University of Basque Country UPV/EHU. First analysis done was the Hardy-Weinberg, then the number of clusters (K) was determined using STRUCTURE and to assess the genetic variability, allele frequencies, the average number of alleles per locus, expected (He) and observed (Ho) heterozygosis, and the index of polymorphism (P) was used the software Genetix. Historical and modern samples gives different results, showing a clear loss of genetic diversity over time leading to a single cluster in modern albacore instead of the two found in historical samples. What this study reveals is very important for conservation concerns, and additional research endeavours are needed.

Implementation of network entropy algorithms on hpc machines, with application to high-dimensional experimental data

Network Theory is a prolific and lively field, especially when it approaches Biology. New concepts from this theory find application in areas where extensive datasets are already available for analysis, without the need to invest money to collect them. The only tools that are necessary to accomplish an analysis are easily accessible: a computing machine and a good algorithm. As these two tools progress, thanks to technology advancement and human efforts, wider and wider datasets can be analysed. The aim of this paper is twofold. Firstly, to provide an overview of one of these concepts, which originates at the meeting point between Network Theory and Statistical Mechanics: the entropy of a network ensemble. This quantity has been described from different angles in the literature. Our approach tries to be a synthesis of the different points of view. The second part of the work is devoted to presenting a parallel algorithm that can evaluate this quantity over an extensive dataset. Eventually, the algorithm will also be used to analyse high-throughput data coming from biology.