A fast and lightweight toolkit for storing, manipulating, and analyzing large-scale DNA methylation data at the sequence level.
For detailed documentation, tutorials, and usage examples, visit the YAME User Guide.
YAME is designed for efficient sequence-level DNA methylation data management, capable of handling both bulk and single-cell DNA methylome workflows. It introduces a family of compact binary formats (CX formats) that represent methylation values, MU counts, categorical states, fraction data, masks, and genomic coordinates in a uniform compressed structure.
- Ultra-fast performance with high compression for methylation matrices
- Scalable to hundreds of thousands of single cells
- Versatile data support: MU counts, binary methylation, chromatin states, fractions, differential calls, and CpG coordinate streams
- Comprehensive toolkit: packing, unpacking, downsampling, subsetting, row operations, enrichment testing, and summarization
- Consistent internal API: all data stored as
cdata_tblocks inside BGZF frames - Integrates seamlessly with bedtools, KYCGKB, and other methylation workflows
Install YAME using conda from the bioconda channel:
conda install yame -c biocondaIf you use YAME in your research, please cite:
Goldberg*, Fu*, Atkins, Moyer, Lee, Deng, Zhou† (2025). "KnowYourCG: Facilitating Base-level Sparse Methylome Interpretation." Science Advances. https://doi.org/10.1126/sciadv.adw3027
- Documentation: https://zhou-lab.github.io/YAME/
- Issues: Please report bugs and feature requests on the GitHub Issues page
YAME is dual-licensed:
- AGPL-3.0 for academic, educational, and non-profit research use
- Commercial License for commercial applications
YAME is free to use for academic research, educational purposes, and non-profit organizations under the GNU Affero General Public License v3.0 (AGPL-3.0).
If you wish to use YAME in commercial products or services, or if the AGPL-3.0 restrictions are not suitable for your use case, please contact us for a commercial license: [zhouw3@chop.edu]
Developed by the Zhou Lab