disperseNN2: a neural network for estimating dispersal distance from georeferenced polymorphism data

BMC Bioinformatics

Software

Chris C. R. Smith¹  Andrew D. Kern¹ 

[1] Institute of Ecology and Evolution, University of Oregon, 97403, Eugene, OR, USA;

关键词: Dispersal;    Population genetics;    Machine learning;    Demographic inference;    Spatial;    Geography;   

DOI  :  10.1186/s12859-023-05522-7

 received in 2023-08-20, accepted in 2023-10-05,  发布年份 2023

来源: Springer

【 摘 要 】

Spatial genetic variation is shaped in part by an organism’s dispersal ability. We present a deep learning tool, disperseNN2, for estimating the mean per-generation dispersal distance from georeferenced polymorphism data. Our neural network performs feature extraction on pairs of genotypes, and uses the geographic information that comes with each sample. These attributes led disperseNN2 to outperform a state-of-the-art deep learning method that does not use explicit spatial information: the mean relative absolute error was reduced by 33% and 48% using sample sizes of 10 and 100 individuals, respectively. disperseNN2 is particularly useful for non-model organisms or systems with sparse genomic resources, as it uses unphased, single nucleotide polymorphisms as its input. The software is open source and available from https://github.com/kr-colab/disperseNN2, with documentation located at https://dispersenn2.readthedocs.io/en/latest/.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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