GAISHI: A Python Package for Detecting Ghost Introgression with Machine Learning

Abstract Summary Ghost introgression is a challenging problem in population genetics. Recent studies have explored supervised learning models, namely logistic regression and UNet++, to detect genomic footprints of ghost introgression. However, their applicability is limited because existing implementations are tailored to tasks in their respective publications, but not available as user-friendly software implementations. Here, we present GAISHI, a Python package for identifying ghost introgressed segments and alleles using multiple machine learning algorithms and demonstrate its usage in different introgression scenarios. Availability and implementation GAISHI is available on GitHub under the GNU General Public License v3.0. The source code can be found at https://github.com/xin-huang/gaishi .