Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, 252-8520, Japan.
Institute for Advanced Biosciences, Keio University, Tsuruoka, 997-0052, Japan.
BMC Bioinformatics. 2018 Jun 28;19(1):249. doi: 10.1186/s12859-018-2257-4.
Circadian rhythms comprise oscillating molecular interactions, the disruption of the homeostasis of which would cause various disorders. To understand this phenomenon systematically, an accurate technique to identify oscillating molecules among omics datasets must be developed; however, this is still impeded by many difficulties, such as experimental noise and attenuated amplitude.
To address these issues, we developed a new algorithm named Maximal Information Coefficient-based Oscillation Prediction (MICOP), a sine curve-matching method. The performance of MICOP in labeling oscillation or non-oscillation was compared with four reported methods using Mathews correlation coefficient (MCC) values. The numerical experiments were performed with time-series data with (1) mimicking of molecular oscillation decay, (2) high noise and low sampling frequency and (3) one-cycle data. The first experiment revealed that MICOP could accurately identify the rhythmicity of decaying molecular oscillation (MCC > 0.7). The second experiment revealed that MICOP was robust against high-level noise (MCC > 0.8) even upon the use of low-sampling-frequency data. The third experiment revealed that MICOP could accurately identify the rhythmicity of noisy one-cycle data (MCC > 0.8). As an application, we utilized MICOP to analyze time-series proteome data of mouse liver. MICOP identified that novel oscillating candidates numbered 14 and 30 for C57BL/6 and C57BL/6 J, respectively.
In this paper, we presented MICOP, which is an MIC-based algorithm, for predicting periodic patterns in large-scale time-resolved protein expression profiles. The performance test using artificially generated simulation data revealed that the performance of MICOP for decaying data was superior to that of the existing widely used methods. It can reveal novel findings from time-series data and may contribute to biologically significant results. This study suggests that MICOP is an ideal approach for detecting and characterizing oscillations in time-resolved omics data sets.
昼夜节律由振荡的分子相互作用组成，其体内平衡的破坏会导致各种疾病。为了系统地理解这一现象，必须开发一种准确的技术来识别组学数据集中的振荡分子；然而，这仍然受到许多困难的阻碍，例如实验噪声和衰减幅度。
为了解决这些问题，我们开发了一种新的算法，名为基于最大信息系数的振荡预测（MICOP），这是一种正弦曲线匹配方法。通过使用马修斯相关系数（MCC）值，将 MICOP 在标记振荡或非振荡方面的性能与四种已报道的方法进行了比较。数值实验是在具有以下特点的时间序列数据上进行的：（1）分子振荡衰减的模拟，（2）高噪声和低采样频率，（3）一个周期的数据。第一项实验表明，MICOP 可以准确识别衰减分子振荡的节律性（MCC > 0.7）。第二项实验表明，即使使用低采样频率的数据，MICOP 也能抵抗高水平的噪声（MCC > 0.8）。第三项实验表明，MICOP 可以准确识别嘈杂的单周期数据的节律性（MCC > 0.8）。作为一种应用，我们利用 MICOP 分析了小鼠肝脏的时间序列蛋白质组数据。MICOP 鉴定出 C57BL/6 和 C57BL/6J 分别有 14 个和 30 个新的振荡候选物。
在本文中，我们提出了一种基于 MIC 的算法 MICOP，用于预测大规模时间分辨蛋白质表达谱中的周期性模式。使用人工生成的模拟数据进行的性能测试表明，MICOP 对衰减数据的性能优于现有的广泛使用的方法。它可以从时间序列数据中揭示新的发现，并可能有助于产生有生物学意义的结果。本研究表明，MICOP 是检测和描述时间分辨组学数据集中振荡的理想方法。
