cropgbm 1.1.7

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

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

Download

Download source code : https://github.com/YuetongXU/CropGBM/releases/tag/cropgbm-v1.1.2

Installation

Install via Conda

$ conda install -c xu_cau_cab cropgbm 

Install via pip

$ pip install --user cropgbm

Install via source code

$ tar -zxf CropGBM.tar.gz

# Install Python package dependencies of CropGBM: setuptools, wheel, numpy, scipy, pandas, scikit-learn, lightgbm, matplotlib, seaborn
$ pip install --user setuptools wheel numpy scipy pandas scikit-learn lightgbm matplotlib seaborn

# Install external dependencies of CropGBM: PLINK 1.90 
$ wget s3.amazonaws.com/plink1-assets/plink_linux_x86_64_20191028.zip
$ mkdir plink_1.90
$ unzip plink_linux_x86_64_20191028.zip -d ./plink_1.90

# Add CropGBM, PLINK to the system environment variables for quick use:
$ vi ~/.bashrc
export PATH="/userpath/CropGBM:$PATH"
export PATH="/userpath/plink1.90:$PATH"
$ source ~/.bashrc

Test

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 via ensemble learning.

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

Contact us: [e-mail]

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