Overview:
The dysregulation of circadian clocks has been associated with metabolic diseases and cancer in humans. Our laboratory has pioneered a method to infer circadian time from unlabeled RNA-seq samples, in particular from mouse and human tissues (Talamanca et al., Science, 2023). With the advent of large databases of human samples in non-diseased and cancer tissue, this offers an opportunity for deeper investigation into the circadian dysregulation in disease states.
Aims:
Utilize the GTEx and TCGA cancer sample databases (RNA-seq) to infer the state of the circadian clock in various samples. Develop a statistical clock score to quantify the state of the circadian clock in these samples, which will serve as a reliable metric for circadian dysregulation. Establish connections between the clock score, GWAS data, and eQTL, to explore potential associations between circadian dysregulation and disease or cancer states.
Prerequisites:
- A background in computational biology or bioinformatics is highly desirable.
- Familiarity with RNA-seq data analysis and a basic understanding of circadian biology are beneficial.
- Knowledge of statistical genetics, particularly related to GWAS and eQTL analysis would be helpful.