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MICOP: Maximal information coefficient-based oscillation prediction to detect biological rhythms in proteomics data.

Hitoshi Iuchi1,2, Masahiro Sugimoto3,4, Masaru Tomita1,2,5

  • 1Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, 252-8520, Japan.

BMC Bioinformatics
|June 30, 2018
PubMed
Summary

We developed Maximal Information Coefficient-based Oscillation Prediction (MICOP), a new algorithm to accurately identify oscillating molecules in omics data, even with noise and low sampling rates. MICOP successfully identified novel oscillating candidates in mouse liver proteome data.

Keywords:
Circadian rhythmMutual informationProteomics

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Genomics

Background:

  • Circadian rhythms involve molecular oscillations crucial for homeostasis; disruptions cause disorders.
  • Identifying oscillating molecules in omics data is challenging due to experimental noise and low amplitude signals.

Purpose of the Study:

  • To develop and validate a novel algorithm for accurate detection of molecular oscillations in time-series omics data.
  • To address limitations of existing methods in handling noisy and low-frequency biological data.

Main Methods:

  • Developed Maximal Information Coefficient-based Oscillation Prediction (MICOP), a sine curve-matching algorithm.
  • Compared MICOP's performance against four existing methods using Mathews correlation coefficient (MCC).
  • Tested MICOP on simulated time-series data with decaying oscillations, high noise, low sampling frequency, and one-cycle data.

Main Results:

  • MICOP accurately identified rhythmicity in decaying oscillations (MCC > 0.7) and was robust against high noise and low sampling frequencies (MCC > 0.8).
  • MICOP effectively detected rhythmicity in noisy one-cycle data (MCC > 0.8).
  • Applied to mouse liver proteome data, MICOP identified 14 and 30 novel oscillating candidates in C57BL/6 and C57BL/6 J mice, respectively.

Conclusions:

  • MICOP is a robust algorithm for predicting periodic patterns in large-scale, time-resolved omics data.
  • MICOP outperforms existing methods for analyzing decaying oscillation data and identifying novel biological insights.
  • MICOP is an ideal tool for detecting and characterizing oscillations in time-resolved omics datasets.