Perez-Sanz Fernando, Ruiz-Hernández Victoria, Terry Marta I, Arce-Gallego Sara, Weiss Julia, Navarro Pedro J, Egea-Cortines Marcos
Instituto Murciano de Investigaciones Biomédicas El Palmar, 30120 Murcia, Spain.
Department of Biosciences, University Salzburg, 5020 Salzburg, Austria.
Metabolites. 2021 Mar 31;11(4):211. doi: 10.3390/metabo11040211.
Metabolomes comprise constitutive and non-constitutive metabolites produced due to physiological, genetic or environmental effects. However, finding constitutive metabolites and non-constitutive metabolites in large datasets is technically challenging. We developed gcProfileMakeR, an R package using standard Excel output files from an Agilent Chemstation GC-MS for automatic data analysis using CAS numbers. gcProfileMakeR has two filters for data preprocessing removing contaminants and low-quality peaks. The first function NormalizeWithinFiles, samples assigning retention times to CAS. The second function NormalizeBetweenFiles, reaches a consensus between files where compounds in close retention times are grouped together. The third function getGroups, establishes what is considered as Constitutive Profile, Non-constitutive by Frequency i.e., not present in all samples and Non-constitutive by Quality. Results can be plotted with the plotGroup function. We used it to analyse floral scent emissions in four snapdragon genotypes. These included a wild type, and affecting floral identity and targeting a circadian clock gene. We identified differences in scent constitutive and non-constitutive profiles as well as in timing of emission. gcProfileMakeR is a very useful tool to define constitutive and non-constitutive scent profiles. It also allows to analyse genotypes and circadian datasets to identify differing metabolites.
代谢组由因生理、遗传或环境影响而产生的组成型和非组成型代谢物组成。然而，在大型数据集中查找组成型代谢物和非组成型代谢物在技术上具有挑战性。我们开发了gcProfileMakeR，这是一个R包，它使用来自安捷伦化学工作站气相色谱-质谱联用仪的标准Excel输出文件，通过化学物质登记号（CAS号）进行自动数据分析。gcProfileMakeR有两个用于数据预处理的过滤器，可去除污染物和低质量峰。第一个函数NormalizeWithinFiles，为样品分配保留时间到CAS号。第二个函数NormalizeBetweenFiles，在文件之间达成共识，将保留时间相近的化合物归为一组。第三个函数getGroups，确定什么被视为组成型谱、按频率划分的非组成型（即并非在所有样品中都存在）和按质量划分的非组成型。结果可以用plotGroup函数绘制。我们用它来分析四种金鱼草基因型的花香排放。这些包括一个野生型，以及影响花形态特征的和靶向一个生物钟基因的。我们确定了气味组成型和非组成型谱以及排放时间上的差异。gcProfileMakeR是定义组成型和非组成型气味谱的非常有用的工具。它还允许分析基因型和昼夜节律数据集以识别不同的代谢物。
