Pirih Nina, Kunej Tanja
Department of Animal Science, Biotechnical Faculty, University of Ljubljana , Domzale, Slovenia .
OMICS. 2017 Jan;21(1):1-16. doi: 10.1089/omi.2016.0144.
Omics is a form of high-throughput systems science. However, taxonomies for omics studies are limited, inviting us to rethink new ways in which we classify, prioritize, and rank various omics systems science studies. In this overarching context, the genome-wide study approaches have proliferated in number and popularity over the past decade. However, their hierarchy is not well organized and the development of attendant terminology is not controlled. In the present study, we searched the literature in PubMed and the Web of Science databases published from March 1999 to September 2016 using the keywords, including genome-wide, association, whole genome, transcriptome-wide, metabolome, epigenome, and phenome. We identified the whole genome study approaches and sorted them according to the omics technology types (genomics, proteomics, and so on) and hierarchy. Thirty-four studies from over 90 publications were sorted into 10 omics groups: DNA level, transcriptomics, proteomics, interactomics, metabolomics, epigenomics, miRNomics/ncRNomics, phenomics, environmental omics, and pharmacogenomics. We suggest here modifications of terminology for study approaches, which share the same acronyms such as EWAS for epigenome-wide association and environment-wide association studies, and MWAS for methylome-wide association and metabolome-wide association studies. Taken together, our study presented here provides the first systematic review and analyses of whole genome approaches and presents a baseline for further controlled terminology development, with a view to a new taxonomy for omics and multi-omics studies in the future. Finally, we call for greater dialogue and collaboration across diverse omics knowledge domains and applications, for example, across plants, animals, clinical medicine, and ecology.
组学是一种高通量系统科学形式。然而，组学研究的分类法有限，这促使我们重新思考对各种组学系统科学研究进行分类、排序和分级的新方法。在这个总体背景下，全基因组研究方法在过去十年中数量和受欢迎程度都有所增加。然而，它们的层次结构组织不佳，相关术语的发展也不受控制。在本研究中，我们使用包括全基因组、关联、全基因组、转录组范围、代谢组、表观基因组和表型组等关键词，在1999年3月至2016年9月发表在PubMed和科学网数据库中的文献中进行搜索。我们确定了全基因组研究方法，并根据组学技术类型（基因组学、蛋白质组学等）和层次结构对它们进行了分类。从90多篇出版物中筛选出的34项研究被分为10个组学类别：DNA水平、转录组学、蛋白质组学、相互作用组学、代谢组学、表观基因组学、微小RNA组学/非编码RNA组学、表型组学、环境组学和药物基因组学。我们在此建议对研究方法的术语进行修改，这些方法共享相同的首字母缩写，例如用于表观基因组范围关联和环境范围关联研究的EWAS，以及用于甲基化组范围关联和代谢组范围关联研究的MWAS。总之，我们在此呈现的研究提供了对全基因组方法的首次系统综述和分析，并为进一步的受控术语发展提供了基线，以期为未来的组学和多组学研究建立新的分类法。最后，我们呼吁在不同的组学知识领域和应用之间，例如在植物、动物、临床医学和生态学之间，进行更多的对话与合作。
