Bioinformatics Omic Platform for Circadian Biomedical Research

Circadian rhythms are fundamental for understanding biology: they date to the origin of life, are found in
virtually every species from cyanobacteria to mammals, and coordinate many important biological functions from
the sleep-wake cycle, to metabolism, to cognitive functions. Circadian rhythms are equally fundamental for health
and medicine: diet modifications have been linked to molecular-level changes in circadian rhythms; disruptions
of circadian rhythms have been linked to health problems ranging from depression to learning disorders to
diabetes, to obesity, to cardiovascular disease, to cancer, and to premature aging; finally, a large fraction of drug
targets have been found to oscillate in a circadian manner in one or several tissues. A better understanding of
circadian oscillations at the molecular level has many direct applications to precision health and medicine.
To illuminate circadian oscillations at the molecular level, modern high-throughput technologies are being
used to measure the concentrations of many molecular species, including transcripts, proteins, and metabolites
along the circadian cycle in different organs and tissues, and under different conditions. Yet informatics tools for
processing, analyzing, and integrating the growing wealth of molecular circadian data are not yet in place.
This effort will fill this fundamental gap by continuing to develop and disseminate informatics tools to
enable the collection, integration, and analyses of this wealth of information and lead to novel and fundamental
insights about circadian oscillations' organization and regulation, roles in health and disease, and future
applications to precision medicine. Specifically, via close collaborations among computational and experimental
scientists, this effort will have four main aims: (1) Data: Aggregate the largest possible collection of circadian
omic (e.g., transcriptomic, metabolomic, proteomic) experimental datasets covering as many species, cells,
tissues, organs, and conditions (e.g., genetic, epigenetic, environmental) as possible. (2) Analysis: Develop
analytical tools, including deep learning tools, to analyze these datasets to identify molecular species with a
periodic concentration profile with statistical determination and conduct integrated differential analyses across
the different datasets. (3) Web Platform: Import the analyses' datasets and results into an integrated database
and serve them publicly through a web server (CircadiOmics platform) as a one-stop shop for viewing or
downloading circadian data, annotations, tools, and analyses, enabling other scientists to view and analyze
circadian data comparatively. And (4) Applications: Apply the CircadiOmics platform's datasets and tools to
specific biomedical problems via multiple efforts in collaboration with other experimental labs to identify the role
of circadian oscillations in health and disease and generate mechanistic molecular hypotheses that can then be
tested in the lab. One example of such collaboration is the study of the interplay between Alzheimer's disease
and circadian rhythms. All data, software, and results will be freely available for academic research purposes
and broadly disseminated through multiple channels to benefit the biomedical community and society at large.

Grant Number: 5R01GM123558-06
NIH Institute/Center: NIH
Principal Investigator: Pierre Baldi