1. Overview

1.1. Introduction

Aging is a multifaceted, time-dependent process influenced by various factors, including genetics, lifestyle, nutrition, mental well-being, as well as social and environmental conditions. Consequently, the aging speed can significantly differ among individuals, rendering chronological age (i.e., the number of years a person has been alive) an inadequate indicator of a person’s overall health status and predictive value for disease onset and treatment responses. In contrast, biological (or physiological) age employs bio-physiological measurements to more accurately gauge an individual’s life clock [1] [2].

DNA methylation-based biological age estimation has been widely used; however, a universally applicable bioinformatics tool is currently lacking. The Epical package provides a number of commands to calcuate epigenetic ages from DNA methylation data generated from Illumina HumanMethylation450 BeadChip (450K), MethylationEPIC v1.0 (850K) or MethylationEPIC v2.0 array.

1.2. Available clocks for Human

	Clock_name	Predictor CpGs	Unit	Tissue	Method
1	Horvath13	353	Year	Pan-tissue	Elastic Net
2	Horvath13_shrunk	110	Year	Pan-tissue	Elastic Net
3	Horvath18	391	Year	Skin & blood	Elastic Net
4	Levine	513	Year	Blood	Elastic Net
5	Hannum	71	Year	Blood	Elastic Net
6	Zhang_EN	514	Year	Blood, Saliva	Elastic Net
7	Zhang_BLUP	319607	Year	Blood, Saliva	BLUP
8	AltumAge	20318	Year	Pan-tissue	DNN
9	Cortical	347	Year	Brain cortex	Elastic Net
10	MEAT	200	Year	Skeletal muscle	Elastic Net
11	EPM	n/a	n/a	n/a	EM
12	Lu_DNAmTL	140	Kb	Blood	Elastic Net
13	Ped_Wu	111	Year	Blood	Elastic Net
14	PedBE	94	Year	Buccal epithelial	Elastic Net
15	GA_Bohlin	96	Day	Cord blood	LASSO
16	GA_Haftorn	176	Day	Cord blood	LASSO
17	GA_Knight	148	Week	Cord blood	Elastic Net
18	GA_Mayne	62	Week	Placental tissues	Elastic Net
19	GA_Lee_CPC	546	Week	Placental tissues	Elastic Net
20	GA_Lee_RPC	558	Week	Placental tissues	Elastic Net
21	GA_Lee_rRPC	395	Week	Placental tissues	Elastic Net

Note

Input data for these clocks are generated from Illumina ‘BeadChip’ methylation array.
The “EPM” algorithem needs user provide training data.
All the commands (i.e. Clock_name) are case-sensitive.

1.3. Available clocks for Mouse (Mus Musculus)

	Clock_name	Predictor CpGs	Unit	Tissue	Method
1	WLMT	435	Day	Pan-tissue	Elastic Net
2	YOMT	329	Day	Pan-tissue	Elastic Net
3	mmLiver	148	Day	Liver	Elastic Net
4	mmBlood	90	Day	Blood	Elastic Net

Note

Input data for these clocks are generated from RRBS (Reduced-Representation Bisulfite Sequencing) or WGBS (Whole Genome Bisulfite Sequencing).
WLMT = Whole Life Multiple Tissue. This clock was trained from mice aged 6 to 30 months.
YOMT = YOung Multiple Tissue. This clock was trained from mice aged 0 to 10 months.