DeepTetrad: high-throughput image analysis of meiotic tetrads by deep learning in Arabidopsis thaliana.
Eun-Cheon LimJaeil KimJihye ParkEun-Jung KimJuhyun KimYeong Mi ParkHyun Seob ChoDohwan ByunIan R HendersonGregory P CopenhaverIldoo HwangKyuha ChoiPublished in: The Plant journal : for cell and molecular biology (2019)
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 the genetic dissection of mechanisms that control meiotic recombination.
Keyphrases
- deep learning
- high throughput
- arabidopsis thaliana
- open label
- artificial intelligence
- convolutional neural network
- double blind
- placebo controlled
- single cell
- machine learning
- copy number
- dna damage
- transcription factor
- mitochondrial dna
- quantum dots
- dna repair
- genome wide
- clinical trial
- dna methylation
- binding protein
- randomized controlled trial
- protein protein
- amino acid
- oxidative stress
- living cells
- gene expression