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White, T. A.

Publications and source records attributed to White, T. A..

2 recordsLinked to original sources

Long-term reciprocal gene flow in wild and domestic geese reveals complex domestication history

Hybridization has frequently been observed between wild and domestic species and can substantially impact genetic diversity of both counterparts. Geese show some of the highest levels of interspecific hybridization across all bird orders, and two of the goose species in genus Anser have been domesticated providing excellent opportunity for joint study of domestication and hybridization. Until now, knowledge on the details of the goose domestication process has come from archaeological findings and historical writings supplemented with few studies based on mitochondrial DNA. Here, we used genome-wide markers to make the first genome-based inference of the timing of European goose domestication. We also analyzed the impact of hybridization on the genome-wide genetic variation in current populations of the European domestic goose and its wild progenitor: the greylag goose (Anser anser). Our dataset consisted of 58 wild greylags sampled around Eurasia and 75 domestic geese representing 14 breeds genotyped for 33,527 single nucleotide polymorphisms. Demographic reconstruction and clustering analysis suggested that divergence between wild and domestic geese around 5,300 generations ago was followed by long-term genetic exchange, and that greylag populations have 3.2-58.0% admixture proportions with domestic geese, with distinct geographic patterns. Surprisingly, many modern European breeds share considerable (> 10%) ancestry with Chinese domestic geese that is derived from the swan goose Anser cygnoid. We show that domestication process can progress despite continued and pervasive gene flow from the wild form.\n\nSignificance StatementReproductive isolation between conspecific wild and domestic populations is a cornerstone of the domestication process, yet gene flow between such wild and domestic populations has been frequently documented. European domestic geese and their wild progenitor (greylags) co-occur and can hybridize and we show that they represent a particularly persuasive case where wild and domestic populations are not isolated gene pools. Our study makes a first genome-based estimate of goose domestication, which up to now has mostly relied on archaeological findings and historical writings. We show ongoing gene flow between greylags and European domestic geese following domestication, but we also observe a surprisingly large contribution of Chinese domestic geese (a separate species) to the genetic make-up of European domestic geese.

evolutionary biology

DRPnet - Automated Particle Picking In Cryo-Electron Micrographs Using Deep Regression

AO_SCPLOWBSTRACTC_SCPLOWSelection of individual protein particles in cryo-electron micrographs is an important step in single particle analysis. In this study, we developed a deep learning-based method to automatically detect particle centers from cryoEM micrographs. This is a challenging task because of the low signal-to-noise ratio of cryoEM micrographs and the size, shape, and grayscale-level variations in particles. We propose a double convolutional neural network (CNN) cascade for automated detection of particles in cryo-electron micrographs. Particles are detected by the first network, a fully convolutional regression network (FCRN), which maps the particle image to a continuous distance map that acts like a probability density function of particle centers. Particles identified by FCRN are further refined (or classified) to reduce false particle detections by the second CNN. This approach, entitled Deep Regression Picker Network or \"DRPnet\", is simple but very effective in recognizing different grayscale patterns corresponding to 2D views of 3D particles. Our experiments showed that DRPnets first CNN pretrained with one dataset can be used to detect particles from a different datasets without retraining. The performance of this network can be further improved by re-training the network using specific particle datasets. The second network, a classification convolutional neural network, is used to refine detection results by identifying false detections. The proposed fully automated \"deep regression\" system, DRPnet, pretrained with TRPV1 (EMPIAR-10005) [1], and tested on {beta}-galactosidase (EMPIAR-10017) [2] and {beta}-galactosidase (EMPIAR-10061) [3], was then compared to RELIONs interactive particle picking. Preliminary experiments resulted in comparable or better particle picking performance with drastically reduced user interactions and improved processing time.

molecular biology