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AnnSQL: a Python SQL-based package for fast large-scale single-cell genomics analysis using minimal computational

Kenny Pavan1,2, Arpiar Saunders1

  • 1Vollum Institute, Oregon Health & Science University, Portland, OR 97239, United States.

Bioinformatics Advances
|May 26, 2025
PubMed
Summary
This summary is machine-generated.

AnnSQL is a new Python package for analyzing large single-cell genomics datasets. It uses SQL and DuckDB to significantly speed up analysis on personal computers.

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell genomics technologies are rapidly advancing, necessitating efficient computational tools for analyzing large datasets.
  • Existing tools often require substantial computational resources, limiting accessibility for large-scale single-cell data analysis.

Purpose of the Study:

  • To introduce AnnSQL, a Python package designed to enhance the analysis of atlas-scale single-cell genomics datasets.
  • To provide a user-friendly and computationally efficient solution for parsing and analyzing large single-cell datasets.

Main Methods:

  • AnnSQL constructs an AnnData-inspired database using the in-process DuckDB engine.
  • The package leverages SQL for data manipulation, offering an intuitive interface.
  • Performance was evaluated on a 4.4 million cell single-nucleus RNA-seq dataset, comparing AnnSQL with AnnData and Seurat.

Main Results:

  • AnnSQL demonstrated orders-of-magnitude performance improvements compared to existing tools.
  • AnnSQL operations on a large dataset were completed in minutes on a laptop.
  • Equivalent operations using AnnData or Seurat on a high-performance computing cluster were significantly slower or failed.

Conclusions:

  • AnnSQL significantly lowers computational barriers for large-scale single-cell/nucleus RNA-seq analysis on personal computers.
  • The package offers a promising computational infrastructure for complete single-cell workflows across various genome-wide measurements.