Tammen Harald, Hess Rüdiger
PXBioVisioN GmbH, Hannover, Germany.
Methods Mol Biol. 2018;1719:187-196. doi: 10.1007/978-1-4939-7537-2_12.
Mass spectrometric (MS) comparative analysis of peptides in biological specimens (nontargeted peptidomics) can result in large amounts of data due to chromatographic separation of a multitude of samples and subsequent MS analysis of numerous chromatographic fractions. Efficient yet effective strategies are needed to obtain relevant information. Combining visual and numerical data analysis offers a suitable approach to retrieve information and to filter data for significant differences as targets for succeeding MS/MS identifications.Visual analysis allows assessing features within a spatial context. Specific patterns are easily recognizable by the human eye. For example, derivatives representing modified forms of signals present are easily identifiable due to an apparent shift in mass and chromatographic retention times. On the other hand numerical data analysis offers the possibility to optimize spectra and to perform high-throughput calculations. A useful tool for such calculations is R, a freely available language and environment for statistical computing. R can be extended via packages to enable functionalities like mzML (open mass spectrometric data format) import and processing. R is capable of parallel processing enabling faster computation using the power of multicore systems.The combination and interplay of both approaches allows evaluating the data in a holistic way, thus helping the researcher to better understand data and experimental outcomes.
对生物样本中的肽进行质谱（MS）比较分析（非靶向肽组学），由于众多样品的色谱分离以及随后对大量色谱馏分的MS分析，会产生大量数据。需要高效且有效的策略来获取相关信息。结合视觉和数值数据分析提供了一种合适的方法来检索信息，并筛选出具有显著差异的数据作为后续MS/MS鉴定的目标。视觉分析允许在空间背景下评估特征。特定模式很容易被人眼识别。例如，由于质量和色谱保留时间的明显变化，代表信号修饰形式的衍生物很容易被识别。另一方面，数值数据分析提供了优化光谱和进行高通量计算的可能性。进行此类计算的一个有用工具是R，它是一种免费的统计计算语言和环境。R可以通过包进行扩展，以实现诸如mzML（开放质谱数据格式）导入和处理等功能。R能够进行并行处理，利用多核系统的能力实现更快的计算。两种方法的结合和相互作用允许以整体方式评估数据，从而帮助研究人员更好地理解数据和实验结果。
