Detecting biological oscillation in time series omics data using combination of bootstrap and maximal information coefficient
Systems Biology Program, Graduate School of Media and Governance
Hitohis Iuchi

Oscillation in biological process plays a important roll to keep biological homeostasis. It is generated by intercellular negative feedback loop of transcripts and proteins. Then, detection of oscillating molecules in large data sets such as transcriptome, proteome and metabolome has been huge issue. Traditional method based on Fourier transform is widely used, but sensitive for experimental and biological noises. Here we present non-parametric rhythm detection method using bootstrap and maximal information-based nonparametric exploration (MINE) statistics. Requipment study for parameter setting was performed and applied for open mouse liver proteome data. Then, not only oscillating molecules detected in previous study but also novel significant rhythmic protein were detected. This combination approach using bootstrap and MINE is valuable for biological oscillation study