Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this necessity, a large number of specialised simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and tskit library. We summarise msprime's many features, and show that its performance is excellent, often many times faster and more memory efficient than specialised alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.
Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this necessity, a large number of specialised simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and tskit library. We summarise msprime's many features, and show that its performance is excellent, often many times faster and more memory efficient than specialised alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.
During the last decade, convolutional neural networks (CNNs) have revolutionised the application of deep learning (DL) methods to classification tasks and object recognition. These procedures can capture key features of image data that are not easily visible to the human eye and use them to classify and predict outcomes with exceptional precision. Here, we show for the first time that CNNs provide highly accurate predictions for small‐scale genetic differentiation and diversity in Ctenomys australis, a subterranean rodent from central Argentina. Using microsatellite genotypes and high‐resolution satellite imagery, we trained a simple CNN to predict local FST and mean allele richness. To identify landscape features with high impact on predicted values, we applied species distribution models to obtain the distribution of suitable habitat. Subsequent use of a machine learning algorithm (random forest) allowed us to identify the attributes that contribute the most to predictions of population genetic metrics. Predictions obtained from the CNN accounted for more than 98% of the variation observed both in FST and mean allele richness values. Random forest regression on landscape metrics indicated that features involving connectivity and consistent prevalence of suitable habitat promoted genetic diversity and reduced genetic differentiation in C. australis. Validation with synthetic data via simulations of genetic differentiation based on the landscape structure of the study area and of a nearby area showed that DL models are able to capture complex relationships between actual data and synthetic data in the same landscape and between synthetic data generated under different landscapes. Our approach represents an objective and powerful approach to landscape genetics because it can extract information from patterns that are not easily identified by humans. Spatial predictions from the CNN may assist in the identification of areas of interest for biodiversity conservation and management of populations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.