Biology subjects

Bernardini, G.

Publications and source records attributed to Bernardini, G..

3 recordsLinked to original sources

bioRxiv2024

On the relationship between spatial environmental variability, dispersion and biodiversity

AimWe establish quantitative relationships between species richness and the rate of spatial change in controlled, digital, environments. We use a simplified, first-principles, stochastic, evolutionary model in which artificial organisms can evolve and disperse. We develop an understanding of how environmental variability in space influences species richness and how it is affected by organisms ability to disperse. Time periodAt each time step of the experiment, each organism can reproduce sexually, disperse and die. Each experiment is run for 100,000 time steps. The life span of each organism is 15 time steps. LocationThe model uses an artificial, digital, landscape consisting of a uniform (x, y) grid of cells, with a single environmental variable that changes sinusoidally in the x direction. Major taxa studiedOrganisms are defined by a 64-bit genome and reproduce sexually. These digital organisms are designed to mimic the basic principles of biological evolution. MethodsEach experiment starts with a single organism, which can mutate and reproduce sexually, producing offspring that can do all of the above and disperse in the environment. Monte Carlo experimentation is used to generate statistical insight into species richness by producing thousands of replicate simulations. ResultsA strong correlation is observed between mean species richness and the rate of spatial change in the environmental variable. This relationship holds true for a wide range of dispersal abilities, but diminishes when dispersal ability is very low. Main ConclusionsWe predict that the rate of change of environmental variables (e.g. the derivative of elevation) is a driver of real-world biodiversity where dispersal is sufficient. Our results suggest that the ability of organisms to disperse plays an important role in determining how biodiversity responds to environmental gradients.

ecology↗

bioRxiv2020

Triplet-based similarity score for fully multi-labeled trees with poly-occurring labels

The latest advances in cancer sequencing, and the availability of a wide range of methods to infer the evolutionary history of tumors, have made it important to evaluate, reconcile and cluster different tumor phylogenies. Recently, several notions of distance or similarities have been proposed in the literature, but none of them has emerged as the golden standard. Moreover, none of the known similarity measures is able to manage mutations occurring multiple times in the tree, a circumstance often occurring in real cases. To overcome these limitations, in this paper we propose MP3, the first similarity measure for tumor phylogenies able to effectively manage cases where multiple mutations can occur at the same time and mutations can occur multiple times. Moreover, a comparison of MP3 with other measures shows that it is able to classify correctly similar and dissimilar trees, both on simulated and on real data.

bioinformatics↗

bioRxiv2019

MALVA: genotyping by Mapping-free ALlele detection of known VAriants

The amount of genetic variation discovered and characterized in human populations is huge, and is growing rapidly with the widespread availability of modern sequencing technologies. Such a great deal of variation data, that accounts for human diversity, leads to various challenging computational tasks, including variant calling and genotyping of newly sequenced individuals. The standard pipelines for addressing these problems include read mapping, which is a computationally expensive procedure. A few mapping-free tools were proposed in recent years to speed up the genotyping process. While such tools have highly efficient run-times, they focus on isolated, bi-allelic SNPs, providing limited support for multi-allelic SNPs, indels, and genomic regions with high variant density.\n\nTo address these issues, we introduce MALVA, a fast and lightweight mapping-free method to genotype an individual directly from a sample of reads. MALVA is the first mapping-free tool that is able to genotype multi-allelic SNPs and indels, even in high density genomic regions, and to effectively handle a huge number of variants such as those provided by the 1000 Genome Project. An experimental evaluation on whole-genome data shows that MALVA requires one order of magnitude less time to genotype a donor than alignment-based pipelines, providing similar accuracy. Remarkably, on indels, MALVA provides even better results than the most widely adopted variant discovery tools.

bioinformatics↗