Skip to content

High-throughput image analysis of meiotic tetrads by deep learning in Arabidopsis thaliana

License

Notifications You must be signed in to change notification settings

abysslover/deeptetrad

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

DeepTetrad Package

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

Contributions

  • The project was initiated by Prof. Choi and the code is written by Dr. Lim (abysslover) at Pohang University of Science and Technology (POSTECH) Plant Genomic Recombination (PGR) Laboratory.

  • This research was conducted in collaborations with the following people: Eun-Cheon Lim1, Jaeil Kim1, Jihye Park1, Eun-Jung Kim1, Juhyun Kim1, Yeong Mi Park1, Hyun Seob Cho1, Dohwan Byun1, Ian R. Henderson2, Gregory P. Copenhaver3, Ildoo Hwang1 and Kyuha Choi1.

  1. Department of Life Sciences, Pohang University of Science and Technology, Pohang, Gyeongbuk, Republic of Korea
  2. Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK
  3. Department of Biology and the Integrative Program for Biological and Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

Getting Started

A step-by-step tutorial is provided in the following sections.

Prerequisites

You should install CUDA-enabled GPU cards with at least 12GB GPU memory manufactured by nVidia, e.g., Titan XP.

Prepare fluorescent images

  1. Take photos of pollen tetrads by using microscopy.
  2. The resolution of a fluorescent image must be (2560x1920).
  3. The SUFFIX of filenames is VERY IMPORTANT to be recognized by DeepTetrad. The conventions are listed below:

Filename Description
I3bc_(1)-1.jpg a bright-field image (first)
I3bc_(1)-2.jpg a fluorescent image of RED light wave (second)
I3bc_(1)-3.jpg a fluorescent image of GREEN light wave (third)
I3bc_(1)-4.jpg a fluorescent image of BLUE light wave (fourth)

Prepare folder structures

  1. Put fluorescent images into folders.
    • The parent folder name determines how DeepTetrad recognizes the name of samples. For instance, the parent folder name of I3bc (1) represents the sample name of fluorescent images.
    • The grand-parent folder name will be matched against the names in the physical T-DNA locations when determining Tetrad types. For example, "I3bc" in the figure, will be matched with "Cyan-Yellow-Red", which is the order of the protein colors of the I3bc Fluorescent Tagged Transgenic Line(FTL).

  1. Prepare a T-DNA map file
    • The file is a plain text file in which FTL names with T-DNA color orders are listed.
    • An example of "T-DNA.txt" is shown below:
I1bc	GRC
I1fg	GCR
I2ab	CGR
I2fg	RGC
I3bc	CGR
I5ab	RGC
  1. Examples
    • A folder structure for testing

- Fluorescent images for testing

Install DeepTetrad

  1. Install Anaconda
    • Download an Anaconda distribution: Link
  2. Create a Conda environment
	conda create -n pac
  1. Install DeepTetrad in Linux
	conda activate pac
	conda install -c abysslover deeptetrad
  1. Install DeepTetrad in Window
	conda activate pac
	conda install -c abysslover deeptetrad
	pip install deeptetrad
  1. Run DeepTetrad
	conda activate pac
	deeptetrad --physical_loc=T_DNA.txt --path=./test
  1. Update DeepTetrad
	conda activate pac
	conda update -c abysslover deeptetrad
	deeptetrad -r

NOTE:

  1. You must activate the conda enviroment before running deeptetrad if you opened a new console.
  2. The model weights are not updated by conda update command, hence you must specify -r option in the first run after the update for refreshing the model weights.

Contact

Please contact abysslover@gmail.com and kyuha@postech.ac.kr if you have any questions about DeepTetrad.

About

High-throughput image analysis of meiotic tetrads by deep learning in Arabidopsis thaliana

Topics

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages