Our work on ATLAS is now available as a bioRxiv preprint. 🎉
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- June 25, 2026
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In this work, we introduce ATLAS (Advanced Trajectory Learning from multi-omics At Single-cell resolution), a Python package for trajectory inference from paired single-cell RNA-seq and ATAC-seq data. By integrating transcriptomic and chromatin accessibility information into a unified framework, ATLAS aims to better capture how cells transition between states and uncover regulatory programs that are difficult to observe from a single modality alone.
ATLAS is already available on PyPI, fully compatible with the scverse ecosystem, making it easy to integrate into existing single-cell analysis workflows.
We’ve also put together a documentation website with installation instructions, tutorials, and examples:
📚 https://atlas-smilies.readthedocs.io/en/latest/
If you’re working with single-cell multi-omics, we’d love for you to give ATLAS a try. Whether you’re exploring a new dataset or comparing trajectory inference methods, your feedback will help us improve the package and shape its future development.
A huge thank you to my co-authors Lorenzo Martini, Roberta Bardini, Alessandro Savino, Stefano Di Carlo and the whole SMILIES Polito Research Group for sharing ideas, feedback, and encouragement throughout this journey! This project wouldn’t have been possible without you.
📄 bioRxiv preprint: https://www.biorxiv.org/content/10.64898/2026.05.23.727175v2
💻 GitHub repository: https://github.com/smilies-polito/atlas-smilies
📚 Documentation: https://atlas-smilies.readthedocs.io/en/latest/
Shared by: Alessia Leclercq
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