Nguyen Lien, Brun Virginie, Combes Florence, Loux Valentin, Vandenbrouck Yves
Université Grenoble Alpes, CEA, Inserm, BGE U1038, Grenoble, France.
INRA, MAIAGE Unit, University Paris-Saclay, Jouy-en-Josas, France.
Methods Mol Biol. 2019;1959:275-289. doi: 10.1007/978-1-4939-9164-8_18.
Knowledge-based approaches using large-scale biological ("omics") data are a powerful way to identify mechanistic biomarkers, provided that scientists have access to computational solutions even when they have little programming experience or bioinformatics support. To achieve this goal, we designed a set of tools under the Galaxy framework to allow biologists to define their own strategy for reproducible biomarker selection. These tools rely on retrieving experimental data from public databases, and applying successive filters derived from information relating to disease pathophysiology. A step-by-step protocol linking these tools was implemented to select tissue-leakage biomarker candidates of myocardial infarction. A list of 24 candidates suitable for experimental assessment by MS-based proteomics is proposed. These tools have been made publicly available at http://www.proteore.org , allowing researchers to reuse them in their quest for biomarker discovery.
利用大规模生物学（“组学”）数据的基于知识的方法是识别机制性生物标志物的有力途径，前提是即使科学家几乎没有编程经验或生物信息学支持，也能获得计算解决方案。为实现这一目标，我们在Galaxy框架下设计了一套工具，使生物学家能够定义自己可重复的生物标志物选择策略。这些工具依赖于从公共数据库检索实验数据，并应用从与疾病病理生理学相关的信息中得出的连续筛选。实施了一个将这些工具联系起来的逐步方案，以选择心肌梗死的组织渗漏生物标志物候选物。提出了一份适合通过基于质谱的蛋白质组学进行实验评估的24种候选物清单。这些工具已在http://www.proteore.org上公开提供，使研究人员能够在寻找生物标志物的过程中重新使用它们。
