MetaXcan is a command-line and library framework for transcriptome-wide association studies (TWAS). It integrates GWAS summary statistics or individual-level genotype data with gene expression prediction models to compute gene-level association statistics. The core methods implemented are S-PrediXcan (summary-based), PrediXcan (individual-level), MultiXcan, and S-MultiXcan (multi-tissue meta-analysis). The architecture consists of Python scripts that orchestrate data processing steps (e.g., parsing GWAS, aligning alleles, computing z-scores) using a core library (`metax`) with modules for genotype reading, GWAS loading, prediction model management, and covariance matrix handling. The system is designed for bioinformatics researchers and requires external reference data (e.g., GTEx models, 1000 Genomes LD). While functional, the codebase exhibits significant technical debt: Python 2 compatibility remnants, deprecated modules, hardcoded URLs, and limited test coverage. The GUI component (Tkinter) is unmaintained. The repository is hybrid, containing both application scripts and data processing pipelines, but the dominant character is a software tool for running TWAS analyses.