2021
DOI: 10.48550/arxiv.2101.00004
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Deep Unsupervised Identification of Selected SNPs between Adapted Populations on Pool-seq Data

Abstract: The exploration of selected single nucleotide polymorphisms (SNPs) to identify genetic diversity between different sequencing population pools (Pool-seq) is a fundamental task in genetic research. As underlying sequence reads and their alignment are error-prone and univariate statistical solutions only take individual positions of the genome into account, the identification of selected SNPs remains a challenging process. Deep learning models like convolutional neural networks (CNNs) are able to consider large … Show more

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