DeepTetrad: high-throughput visual tetrad analysis by deep learning
Eun‐Cheon Lim, Jae Il Kim, Ji Hye Park, Eun-Jung Kim, Yeongmi Park, Juhyun Kim, Hyun Seob Cho, Ian R. Henderson, Gregory P. Copenhaver, Il Doo Hwang, Kyuha Choi · Open Access System for Information Sharing (Pohang University of Science and Technology) · 2019
Meiotic crossover recombination creates new combinations of alleles in gametes, profoundly affecting genome diversity and breeding. Crossover frequency varies along chromosomes and interference limits coincidence of closely spaced crossovers, which can be measured by fluorescent pollen tetrad assay in Arabidopsis. Here, we establish DeepTetrad, a deep learning-based image recognition package for tetrad analysis which enables rapid, automatic high-throughput measurements of crossover frequency and interference in a large number of fluorescent pollen tetrads in individual plant.