1 Department of Genetics, Stanford University , Stanford, California.
2 Stanford Center for Genomics and Personalized Medicine, Stanford University , Stanford, California.
Big Data. 2013 Dec;1(4):202-6. doi: 10.1089/big.2013.0040.
The integrative personal omics profiling study introduced a novel, integrative approach based on personalized, longitudinal, multi-omics data. The study collected genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14-month period. The results revealed various medical risks and extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. The current article is a data publication that provides the checklists for the metadata of the proteomics (see Table 1 ) and metabolomics (see Table 2 ) datasets of the study. The proposed checklist was recently developed and endorsed by the Data-Enabled Life Sciences Alliance (DELSA Global). We call for the broader use of data publications using the metadata checklist to make omics data more discoverable, interpretable, and reusable, while enabling appropriate attribution to data generators and infrastructure science builders.
整合个人组学分析研究引入了一种新颖的、基于个性化、纵向、多组学数据的综合方法。该研究在 14 个月的时间内从单个个体中收集了基因组、转录组、蛋白质组、代谢组和自身抗体谱。结果揭示了各种医疗风险以及不同分子成分和生物途径在健康和疾病状态下的广泛、动态变化。本文是一份数据出版物，提供了该研究的蛋白质组学（见表 1）和代谢组学（见表 2）数据集的元数据检查表。该检查表是最近由数据驱动的生命科学联盟（DELSA Global）开发和认可的。我们呼吁更广泛地使用带有元数据检查表的数据出版物，使组学数据更具发现性、可解释性和可重用性，同时能够适当归因于数据生成者和基础设施科学建设者。
