Boccard Julien, Rudaz Serge
School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva 4, Switzerland.
Swiss Centre for Applied Human Toxicology (SCAHT), Universities of Basel and Geneva, Basel, Switzerland.
Methods Mol Biol. 2018;1730:371-384. doi: 10.1007/978-1-4939-7592-1_28.
Assessing potential alterations of metabolic pathways using large-scale approaches today plays a central role in clinical research. Because several thousands of mass features can be measured for each sample with separation techniques hyphenated to mass spectrometry (MS) detection, adapted strategies should be implemented to detect altered pathways and help to elucidate the mechanisms of pathologies. These procedures include peak detection, sample alignment, normalization, statistical analysis, and metabolite annotation. Interestingly, considerable advances have been made over the last years in terms of analytics, bioinformatics, and chemometrics to help massive and complex metabolomic data to be more adequately handled with automated processing and data analysis workflows. Recent developments and remaining challenges related to MS signal processing, metabolite annotation, and biomarker discovery based on statistical models are illustrated in this chapter considering their application to clinical research.
如今，使用大规模方法评估代谢途径的潜在改变在临床研究中起着核心作用。由于采用与质谱（MS）检测联用的分离技术，每个样本可测量数千个质量特征，因此应实施适当的策略来检测改变的途径，并有助于阐明病理机制。这些程序包括峰检测、样本校准、归一化、统计分析和代谢物注释。有趣的是，在过去几年中，在分析学、生物信息学和化学计量学方面取得了相当大的进展，以帮助通过自动化处理和数据分析工作流程更充分地处理大量复杂的代谢组学数据。本章说明了与MS信号处理、代谢物注释以及基于统计模型的生物标志物发现相关的最新进展和仍然存在的挑战，并考虑了它们在临床研究中的应用。
