Welcome to CropGBM！

Introduction

Crop Genomic Breeding machine (CropGBM) is a multifunctional Python3 program that integrates data preprocessing, population structure analysis, SNP selection, phenotype prediction, and data visualization. Has the following advantages:

• Use LightGBM algorithm to quickly and accurately predict phenotype values and support GPU-accelerated training.
• Supports selection and visualization of SNPs that are strongly related to phenotype.
• Support PCA and t-SNE two dimensionality reduction algorithms to extract SNP information.
• Support Kmeans and OPTICS two clustering algorithms to analyze the sample population structure.
• Plot histograms of heterozygosity rate, deletion rate, and frequency of alleles for genotype data.

Documentation

English version documentation: https://ibreeding.github.io

Chinese version documentation: https://ibreeding-ch.github.io

Requirements

The following are required before installing cooltools:

• python >=3.8,<=3.11
• numpy >=1.26.0,<2.0.0
• scipy >=1.7.0
• pandas >=1.3.0
• scikit-learn >=0.24.2
• lightgbm >=3.3.0,<4.0.0
• matplotlib >=3.4.0
• seaborn >=0.11.0
• plink >=1.9

Installation

Install via Conda or Mamba (Recommend)

$ conda install xu_cau_cab::cropgbm 

or

$ mamba install xu_cau_cab::cropgbm 

Install via pip

$ pip install --user cropgbm

Test (For Conda)

Enter the ‘/miniconda3/pkgs/cropgbm-1.1.7-py311_0/info/cropgbm/test/’ folder

Run the run_test.py to check whether cropgbm can run successfully locally.

About

Citation: Jun Yan, Yuetong Xu, Qian Cheng, Shuqin Jiang, Qian Wang, Yingjie Xiao, Chuang Ma, Jianbing Yan and Xiangfeng Wang. LightGBM: accelerated genomically-designed crop breeding through ensemble learning.

Supplementary Information: Support data and materials for the manuscript is available at https://github.com/YuetongXU/Cropgbm-Paper

Contact us: cropgbm@163.com

Note: Academic users can download directly, industrial users first contact us.