You can upload your data in .tsv (tab-delimited), .csv (comma-delimited), or .h5 (Hierarchical Data Format 5) 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, and .h5.gz. For large files, we recommend submission as .h5.gz.
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.
Microarray normalization will be implemented in SudoClock shortly. For now, 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.
Please provide a metadata table, with samples as rows and features (i.e., ages/groups/treatments) as columns.
Welcome to SudoClock, 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 SudoClock is to create an accessible, easy-to-use atlas of molecular aging clocks.
We aim to regularly update SudoClock 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:
Last updated on 9/26/2021
This tool is intended for research and academic purposes. All clocks implemented are publicly available (see the Clock Atlas page), but some are commercially patented and/or licensed. Please refer to the original publications for more information.
Copyright © Alexandre Trapp & Kejun “Albert” Ying, 2021