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Pagliuca, P.

Publications and source records attributed to Pagliuca, P..

2 recordsLinked to original sources

CENdetectHOR: a comprehensive tool for CENtromere profiling and HOR detection

Centromeres are essential for accurate chromosome segregation and are characterized by long arrays of repetitive satellite DNA, showing extensive variation in sequence, length, and organization across species. Despite extensive research, fully characterizing centromeric DNA has been challenging due to its repetitive nature and rapid evolution. Here, we present CENdetectHOR, a computational tool to identify and analyze higher-order repeat (HOR) arrays in centromeric regions across diverse organisms, requiring no a priori information. We validate its efficacy using human and arabidopsis genomes, demonstrating its ability to reveal the complexity and diversity of centromeric architectures. CENdetectHOR also recognizes HOR variants, elucidating interindividual and interspecific variations in centromeric regions. These findings establish CENdetectHOR as a powerful, versatile, and rapid tool for advancing research on centromere structure, evolution, and function.

genetics↗

Global Progress in Competitive Co-Evolution: a Systematic Comparison of Alternative Methods

We investigate the use of competitive co-evolution for synthesizing progressively better solutions. Specifically, we introduce a set of methods to measure historical and global progress. We discuss the factors that facilitate genuine progress. Finally, we compare the efficacy of four qualitatively different algorithms. The selected algorithms promote genuine progress by creating an archive of opponents used to evaluate evolving individuals, generating archives that include high-performing and well-differentiated opponents, identifying and discarding variations that lead to local progress only (i.e. progress against a subset of possible opponents and retrogressing against a larger set). The results obtained in a predator-prey scenario, commonly used to study competitive evolution, demonstrate that all the considered methods lead to global progress in the long term. However, the rate of progress and the ratio of progress versus retrogressions vary significantly among algorithms.

neuroscience↗