Xin Huang

Genomic Landscapes of Natural Selection in Great Apes

The paper analyzes genomic diversity in great apes to identify loci under natural selection and characterizes the distribution of fitness effects across species.

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

GAISHI detects ghost introgression using machine learning algorithms and provides a user-friendly Python package for this task.

selscape: A Snakemake Workflow for Investigating Genomic Landscapes of Natural Selection

selscape automates genome-wide selection analysis using Snakemake, enabling efficient and scalable comparisons across populations.

A curated dataset of great ape genome diversity

The paper curates a dataset of 332 high-coverage great ape genomes to address evolutionary and diversity questions.

SAI: A Python Package for Statistics for Adaptive Introgression

SAI computes adaptive introgression statistics and applies them to three datasets, identifying candidate regions and highlighting discrepancies with prior results.

Accessible, realistic genome simulation with selection using stdpopsim

The paper extends the stdpopsim simulation framework to include realistic selection models, enabling studies of selection's impact on genetic variation across species.

Social and genetic diversity in first farmers of central Europe

This study analyzes ancient DNA from LBK Neolithic sites to show genetic differences between eastern and western LBK populations, with evidence of patrilocality and no evidence of male-biased WHG admixture.

dadi-cli: Automated and distributed population genetic model inference from allele frequency spectra

dadi-cli simplifies dadi usage and enables distributed computing for population genetic model inference from allele frequency spectra.

Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations

The paper expands the stdpopsim species catalog and improves genome simulations by adding non-crossover recombination and species-specific annotations, making simulations more realistic for non-model organisms.

sstar: A Python package for detecting archaic introgression from population genetic data with S*

sstar is a Python package that improves detection of archaic introgression in population genetic data by being robust to different demographic models.