Kolker Eugene, Özdemir Vural, Martens Lennart, Hancock William, Anderson Gordon, Anderson Nathaniel, Aynacioglu Sukru, Baranova Ancha, Campagna Shawn R, Chen Rui, Choiniere John, Dearth Stephen P, Feng Wu-Chun, Ferguson Lynnette, Fox Geoffrey, Frishman Dmitrij, Grossman Robert, Heath Allison, Higdon Roger, Hutz Mara H, Janko Imre, Jiang Lihua, Joshi Sanjay, Kel Alexander, Kemnitz Joseph W, Kohane Isaac S, Kolker Natali, Lancet Doron, Lee Elaine, Li Weizhong, Lisitsa Andrey, Llerena Adrian, Macnealy-Koch Courtney, Marshall Jean-Claude, Masuzzo Paola, May Amanda, Mias George, Monroe Matthew, Montague Elizabeth, Mooney Sean, Nesvizhskii Alexey, Noronha Santosh, Omenn Gilbert, Rajasimha Harsha, Ramamoorthy Preveen, Sheehan Jerry, Smarr Larry, Smith Charles V, Smith Todd, Snyder Michael, Rapole Srikanth, Srivastava Sanjeeva, Stanberry Larissa, Stewart Elizabeth, Toppo Stefano, Uetz Peter, Verheggen Kenneth, Voy Brynn H, Warnich Louise, Wilhelm Steven W, Yandl Gregory
1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington.
OMICS. 2014 Jan;18(1):10-4. doi: 10.1089/omi.2013.0149.
Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.
生物过程从根本上是由生物分子之间的复杂相互作用驱动的。综合高通量组学研究能够从多方面观察细胞、生物体或它们的群落。随着新的后基因组技术的出现，组学研究越来越普遍；然而，这些研究的全部影响只有通过数据协调、共享、荟萃分析和综合研究才能实现。这些关键步骤需要一致地生成、捕获和分发元数据。为确保透明度、促进数据协调以及最大限度地提高生命科学研究的可重复性和可用性，我们提出了一个简单通用的组学元数据清单。所提出的清单建立在生命科学领域已经使用的丰富本体和标准之上。该清单将作为一个共同标准，用于指导实验设计、捕获重要参数，并用作独立数据出版物的标准格式。组学元数据清单和数据出版物将在组学数据与基于知识的生命科学创新之间建立有效的联系，重要的是，在后基因组时代允许对数据生成者和基础设施科学建设者进行适当的归因。我们要求生命科学领域测试所提出的组学元数据清单和数据出版物，并为其使用和改进提供反馈。
