Hackl, J. | bioRxiv Science

bioRxiv2026

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

SummaryGhost 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 implementationGAISHI 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.

bioinformatics↗

bioRxiv2025

SAI: A Python Package for Statistics for Adaptive Introgression

Adaptive introgression is an important evolutionary process, yet widely used summary statistics--such as the number of uniquely shared sites and the quantile of the derived allele frequencies in such sites--lack accessible implementations, limiting reproducibility and methodological clarity. Here, we present SAI, a Python package for computing these statistics, and apply it to three datasets. First, using the 1000 Genomes Project data, we replicated previously reported candidate regions and identified additional ones, including a region detected by studies using supervised deep learning. Second, reanalysis of a Lithuanian genome dataset revealed no candidates in the HLA region. Finally, we investigated bonobo introgression into central chimpanzees and identified a candidate region that overlaps a high-frequency Denisovan-introgressed haplotype block reported in modern Papuans--an intriguing co-occurrence across divergent lineages. Discrepancies with prior results highlight the importance of transparent and reproducible analysis workflows, especially as machine learning becomes increasingly prevalent in evolutionary genomics.

evolutionary biology↗