School of Pharmacy, University of Reading, Whiteknights, Reading, United Kingdom.
Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom.
Brief Bioinform. 2018 Mar 1;19(2):286-302. doi: 10.1093/bib/bbw114.
Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.
用于生成和处理大型生物数据集(组学数据)的技术和信息学的进步正在推动生物医学科学研究的重大转变。虽然基因组学、转录组学和蛋白质组学,加上生物信息学和生物统计学,正在获得动力,但它们在很大程度上仍然是单独评估的,采用不同的方法生成单一主题的知识,而不是综合知识。随着生物医学科学的其他领域,包括代谢组学、表观基因组学和药物基因组学,也朝着组学规模发展,我们正在见证跨学科数据整合策略的兴起,以支持更好地理解生物系统,并最终开发成功的精准医学。本综述跨越了基因组学、转录组学和蛋白质组学之间的界限,总结了组学数据的生成、分析和共享方式,并概述了这种全球方法的当前优势和劣势。这项工作旨在针对那些寻求专业领域以外知识的学生和研究人员,并促进从还原分析方法到研究中的全局综合分析方法的飞跃。