pyaging is a cutting-edge Python package designed for the longevity research community, offering a comprehensive suite of GPU-optimized biological aging clocks.
Installation - Clock gallery - Search, cite, get metadata and clock parameters - Illumina Human Methylation Arrays - Illumina Mammalian Methylation Arrays - RRBS DNA methylation - Bulk histone mark ChIP-Seq - Bulk ATAC-Seq - Bulk RNA-Seq - Blood chemistry - API Reference
With a growing number of aging clocks and biomarkers of aging, comparing and analyzing them can be challenging. pyaging simplifies this process, allowing researchers to input various molecular layers (DNA methylation, histone ChIP-Seq, ATAC-seq, transcriptomics, etc.) and quickly analyze them using multiple aging clocks, thanks to its GPU-backed infrastructure. This makes it an ideal tool for large datasets and multi-layered analysis.
If you have recently developed an aging clock and would like it to be integrated into pyaging, please email us. We aim to incorporate it within two weeks! We are also happy to adapt to any licensing terms for commercial entities.
For coding-related queries, feedback, and discussions, please visit our GitHub Issues page.
To cite pyaging, please use the following:
@article{de_Lima_Camillo_pyaging,
author = {de Lima Camillo, Lucas Paulo},
title = "{pyaging: a Python-based compendium of GPU-optimized aging clocks}",
journal = {Bioinformatics},
pages = {btae200},
year = {2024},
month = {04},
issn = {1367-4811},
doi = {10.1093/bioinformatics/btae200},
url = {https://doi.org/10.1093/bioinformatics/btae200},
eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btae200/57218155/btae200.pdf},
}
- Add SymphonyAge