Step 1: Find the GEO data you want to analyze


Step 2: Compute biological age of samples


Step 3: Visulization and statistical analysis



Input data flexbility

You can upload your data in .tsv (tab-delimited) and .csv (comma-delimited) format. In case you have many samples and/or millions of features (i.e. WGBS data), we recommend compressing the files with gzip. Thus, you can also upload data as .tsv.gz, .csv.gz.

Orientation and transposition

Please input dataframes where rows correspond to features (CpGs, genes, etc…) and samples are columns. Transposition into this format can be accomplished in Python with df.T, or in R with t(df), where df is the dataframe in question. This will speed up loading in the data for computation, and helps deliver results faster. The name of features should be the first row, and the name of samples should be the first column.


We recommend normalizing microarray data using the BMIQ method developed by Andrew Teschendorff and Steve Horvath (refer to the “R code for normalizing the DNA methylation data” file in the Horvath 2013 paper for more information).


Our tool accepts both microarray and sequencing data of any kind.

  • For microarray data, please index using cg probe identifiers (i.e., cg02582848).
  • For sequencing data, please index using genomic positions (i.e., chr10_111559529).


Please provide a metadata table, with samples as rows and features (i.e., ages/groups/treatments) as columns. Note that the sample names should match the sample names in the data table, and needs to be in the first column of the metadata.


For sequencing data in mice, please map reads or translate positions (using LiftOver from UCSC) to the GRCm38/mm10 genome.



Welcome to ClockBase, a comprehensive platform for biological age computation using high-dimensional molecular data.

There are now dozens of clocks across several species and modalities, each with their own algorithmic intricacies. This can make biological age computation a daunting task for newcomers and experts alike.

Our goal with ClockBase is to create an accessible, easy-to-use atlas of molecular aging clocks.

We aim to regularly update ClockBase to provide rapid, up-to-date biological age predictions for the aging research community.

For any questions or suggestions, please feel free to contact us at: or

Follow us on Twitter: @KejunYing and @gladyshev_lab

Cite us

ClocBase preprint is avaliable here, please cite us if you used ClockBase in your work.

K. Ying, A. Tyshkovskiy, A. Trapp, H. Liu, M. Moqri, C. Kerepesi, V. N. Gladyshev, ClockBase: a comprehensive platform for biological age profiling in human and mouse (2023), p. 2023.02.28.530532, bioRxiv, doi:10.1101/2023.02.28.530532.


The initial version of ClockBase (SudoClock) was originally built by Kejun Ying and Alexandre Trapp at 2021. The ClockBase is then further developed by Kejun Ying, with the help of Alexandre Trapp, Alexander Tyshkovskiy, Mahdi Moqri, and other member from Gladyshev Lab.


Please note that this tool is intended solely for research and academic purposes and should not be used for any commercial or practical applications. While all clocks implemented are publicly available and can be accessed through the Clock Info page, it is important to note that some may be subject to commercial patents and/or licenses. Therefore, any use or reproduction of these clocks for commercial purposes may require appropriate permission or licensing from the respective patent holders. We recommend referring to the original publications for additional information and guidance on the appropriate use of these clocks.