AlignClock
Multi-clock biological age estimation from gene expression
The Challenge
Biological age—how old your body actually is versus your calendar age—has emerged as a powerful predictor of health outcomes, mortality risk, and treatment response. While epigenetic clocks have dominated this space, transcriptomic clocks offer complementary insights by measuring the functional state of gene expression rather than epigenetic marks.
Most biological age tools focus on a single metric, but aging is multidimensional. Mortality risk, cellular vitality, and gene silencing patterns represent distinct aspects of biological aging that may respond differently to interventions. What’s needed is a multi-clock approach that captures these different dimensions from a single RNA-seq sample—with uncertainty quantification and the ability to flag when different biological systems are aging at different rates.
How AlignClock Helps
AlignClock calculates three validated biological age scores from standard bulk RNA-seq data, each capturing a distinct dimension of biological aging:
- TraMA (Transcriptomic Mortality-risk Age): 35 genes, AUC = 0.959, Cohen’s d = 2.44. Our most powerful predictor, outputting biological age in years with a 5-category interpretation (much younger → much older).
- Moqri (Epigenetic Silencing): 106 genes, AUC = 0.702. Measures age-related gene silencing patterns from transcription data, classified as low, moderate, or high silencing.
- CV Ratio (Cellular Vitality): 118 genes, AUC = 0.573. Assesses the balance between ribosomal protein synthesis and inflammatory gene expression.
Each score includes sex-stratified percentile rankings against the Allen Sound Life reference cohort (n=838: 479 Female, 359 Male), so you know exactly where each patient stands relative to their demographic group.
AlignClock provides optional bootstrap confidence intervals (95% CI) for all three scores, quantifying uncertainty from gene-level sampling. When scores point in different directions, the built-in discordance detection flags the disagreement and explains which biological systems are aging differently.
Three Dimensions of Biological Aging
| Score | Genes | AUC | Cohen’s d | What It Measures |
|---|---|---|---|---|
| TraMA | 35 | 0.959 | 2.44 | Mortality-risk biological age (years) |
| Moqri | 106 | 0.702 | -0.58 | Epigenetic silencing (z-score) |
| CV Ratio | 118 | 0.573 | -0.23 | Cellular vitality (ribosome/inflammation ratio) |
What You Receive
Three Clock Scores
TraMA (biological age in years), Moqri (epigenetic silencing z-score), and CV Ratio (cellular vitality) for every sample, with rich category interpretation.
Percentile Rankings
Sex-stratified percentile rankings against the Allen Sound Life reference cohort (479 Female, 359 Male). Know exactly where each patient stands.
Confidence Intervals
Bootstrap 95% CIs for all three scores, quantifying measurement uncertainty. Essential for clinical and cohort study applications.
Discordance Detection
Automatic flagging when scores point in different biological directions—helping identify individuals where different systems are aging at different rates.
Methodology & Validation
MOQRI EPIGENETIC
CV RATIO
SEX-STRATIFIED
BOOTSTRAP CI
ALLEN VALIDATED
Validated on the Allen Sound Life cohort (n=838, ages 25-67, 479 Female, 359 Male). All three scores perfectly reproduce the Phase 5 research code (r = 1.000 per-sample correlation). The TraMA clock’s AUC of 0.959 and Cohen’s d of 2.44 represent state-of-the-art performance for transcriptomic biological age prediction.
The software is backed by 115 unit tests covering scoring, interpretation, confidence intervals, sex-stratified percentiles, and backward compatibility. Available as a Python package, CLI tool, and interactive Shiny web interface.
References: Klopack et al., Aging 2025 (TraMA); Moqri et al., Nat Commun 2026 (Moqri); Zhu et al., Sci Adv 2023 (CV Ratio).
Ideal For
- Longevity and aging intervention studies measuring biological age changes
- Clinical trials with mortality or healthspan endpoints
- Cohort studies investigating biological vs chronological aging
- Personalized medicine applications requiring health risk assessment
- Pharmaceutical studies tracking intervention effects on biological age
- Studies where scores disagree—discordance detection helps interpret multi-dimensional aging
- Any whole blood bulk RNA-seq where biological aging is relevant
Start Your Analysis
Ready to analyze your data with AlignClock? Submit your project and we'll scope a plan tailored to your experimental design.