Rhythmic Component Analysis Tool (RCAT): A Precise, Efficient and User-Friendly Tool for Circadian Clock Genes Analysis
Rhythmic Component Analysis Tool (RCAT): A Precise, Efficient and User-Friendly Tool for Circadian Clock Genes Analysis
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DOI：
10.1007/s12539-021-00471-2
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发表时间：
2021-08
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影响因子：
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通讯作者：
Zhibo Liu;Meng Meng-Meng;Shufan Zhang;Hao Qiu;Zhiwei Liu;Moli Huang
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文献类型：
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作者：
Zhibo Liu;Meng Meng-Meng;Shufan Zhang;Hao Qiu;Zhiwei Liu;Moli Huang
高通量下一代测序（NGS）技术和实时昼夜节律动态报告系统产生大量关于昼夜节律领域的RNA和蛋白质水平的实验数据，因此需要统计知识和计算技能来进行定量分析。尽管有许多软件应用程序可以处理这些数据，但它们通常难以使用且计算效率低下。因此，需要一种方便、用户友好的工具来准确获取生物钟基因的节律成分（周期、幅度和相位）。在这里，我们开发了一种新的分析工具，名为节奏成分分析工具（RCAT），它具有易于理解的界面，具有一键操作的特点，将所有结果以表格和图像的形式呈现，并自动将它们保存为CSV文件。我们使用昼夜节律研究领域广泛采用的标准相对振幅误差 (RAE) 来评估结果的质量。为了说明RCAT在不同情况下的分析能力，我们通过CircaInSilico（一种用于生成合成基因组生物学数据的网络服务器，为研究生物节律的统计方法提供基准）生成四组不同采集间隔和幅度范围的时间序列数据，并使用RCAT对其进行分析。为了证明RCAT的有效性，我们分析了两组具有时间序列数据的案例研究：一组使用来自基因表达综合库（GEO）存储库的微阵列和RNA-Seq数据来识别肝脏中具有显着周期性的核心时钟基因（CCG），另一组使用Lumicycle®（一种常用的光度计）收集的实时荧光报告数据来计算精确的周期， 幅度和相位。在这些示例中，RCAT 在短时间内成功检测到大多数循环样本，并且还成功计算出准确的节律分量。这些结果表明，RCAT 提高了周期性振荡数据分析的灵活性和便利性。 RCAT 可免费获取：https://github.com/lzbbest/Rhythmic-Component-Analysis-Tool/releases。它作为一款跨平台软件，不仅可以运行在Linux上，还可以运行在Win10、Win8、Win7上。 图文摘要
High-throughput next-generation sequencing (NGS) technologies and real-time circadian dynamics reporting systems produce large amounts of experimental data on RNA and protein levels in the field of circadian rhythm and therefore require statistical knowledge and computational skills for quantitative analysis. Although there are many software applications that can process these data, they are often difficult to use and computationally inefficient. Hence, a convenient, user-friendly tool that can accurately acquire rhythmic components (period, amplitude, and phase) of circadian clock genes is necessary. Here, we develop a new analysis tool named rhythmic component analysis tool (RCAT), which has an easily understood interface featuring a one-button operation, that presents all results as tables and images and automatically saves them as CSV files. We use the relative amplitude error (RAE), widely-adopted criteria on the circadian research field to estimate the quality of results. To illustrate the analytical ability of the RCAT under different situations, we generate four groups of time-series data by CircaInSilico (a web server for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms) with different collection intervals and amplitude ranges and use RCAT to analyze them. To demonstrate the effectiveness of RCAT, we analyze two sets of case studies with time-series data: one set uses microarray and RNA-Seq data from the gene expression omnibus (GEO) repository to identify core clock genes (CCGs) with significant periodicity in the liver, and the other set uses real-time fluorescence reporting data collected by Lumicycle®(a commonly-used luminometer) to calculate the precise period, amplitude and phase. In these examples, most cycling samples are successfully detected by the RCAT within a short collection time, and accurate rhythmic components are also successfully computed. These results indicate that RCAT improves flexibility and convenience in periodic oscillation data analysis. RCAT, is freely available at: https://github.com/lzbbest/Rhythmic-Component-Analysis-Tool/releases. It, as a cross-platform software, can be run not only on Linux, but also on Win10, Win8 and Win7.Graphical abstract