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DeepTetrad: high-throughput image analysis of meiotic tetrads by deep learning in Arabidopsis thaliana

Title
DeepTetrad: high-throughput image analysis of meiotic tetrads by deep learning in Arabidopsis thaliana
Authors
LIM, EUNCHEONKIM, JAE ILKIM, JAE ILKIM, EUNJUNGKIM, JUHYUNPARK, YEONGMICHO, HYUN SEOBBYUN, DOHWANHENDERSON, IAN RCOPENHAVER, GREGORY PHWANG, IL DOOCHOI, KYUHA
Date Issued
4-Nov-2019
Publisher
Cold Spring Harbor Conferences Asia
Abstract
Meiotic crossovers facilitate chromosome segregation and create new combinations of alleles in gametes. Crossover frequency varies along chromosomes and crossover interference limits the coincidence of closely spaced crossovers. Crossovers can be measured by observing the inheritance of linked transgenes expressing different colors of fluorescent protein in Arabidopsis pollen tetrads. Here we establish DeepTetrad, a deep learning-based image recognition package for pollen tetrad analysis that enables high-throughput measurements of crossover frequency and interference in individual plants. DeepTetrad will accelerate genetic dissection of mechanisms that control meiotic recombination.
URI
http://oasis.postech.ac.kr/handle/2014.oak/100027
Article Type
Conference
Citation
2019 Cold Spring Harbor Asia Conference: PLANT CELL & DEVELOPMENTAL BIOLOGY, 2019-11-04
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 HWANG, IL DOO
Dept of Life Sciences
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