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GAISHI

GAISHI is a Python package for Genomic Analysis of Introgressed-Site and -Haplotype Identification using machine learning.

It supports:

  • logistic regression models
  • extra-trees classifiers
  • UNet++ models

Requirements

GAISHI works on Unix/Linux operating systems and is tested with the environment in this env.yaml:

  • Python 3.9.19
  • Python packages:
    • black=24.10.0
    • demes=0.2.3
    • flake8=7.1.1
    • h5py=3.10.0
    • msprime=1.3.1
    • numpy=1.26.4
    • onnx=1.11.0
    • onnxconverter-common==1.9.0
    • onnxruntime=1.19.2
    • ortools==8.2.8710
    • pandas=2.2.1
    • pip=24.0
    • protobuf=3.20.0
    • pydantic=2.11.7
    • pyranges=0.0.129
    • pytest=8.1.1
    • pytest-cov=5.0.0
    • pyyaml=6.0.1
    • safetensors==0.4.5
    • scikit-allel=1.3.7
    • scikit-learn=1.4.1.post1
    • scipy=1.12.0
    • skl2onnx==1.11.2
    • torch==2.2.0
    • tskit=0.5.6
    • https://github.com/xin-huang/seriate.git@dev

Installation

Users can install GAISHI with mamba:

git clone https://github.com/xin-huang/gaishi
cd gaishi
mamba env create -f env.yaml
conda activate gaishi

To check the installation:

gaishi -h

Citations

If you find GAISHI useful, please cite:

  • Huang X, Hackl J, Pawar H, Kuhlwilm M. 2026. GAISHI: A Python package for detecting ghost introgression with machine learning. bioRxiv: 2026.01.31.703038.