Omic or Multi-omics Approach Can Save The Mankind

Johar Ali, Ome Kalsoom Afridi
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引用次数: 1

Abstract

The publication of the first draft of human genome, has led to the explosion of high throughput technologies including genomics, epigenomics, transcriptomic, proteomics, and metabolomics aiming to characterize the various biological molecules (DNA, RNA, proteins, and metabolites). These high throughput technologies collectively called as omics revolutionized medical research in the last two decades. The advent of next generation sequencing (NGS) reduced the time and economic cost of traditional sequencing and has led to the emergence of genomics as the first discipline of omics. Following the emergence of genomics, a number of projects such as The Cancer Genome Atlas (TCGA), 1000 Genome Project (1KGP), and the International Cancer Genome Consortium have been accomplished. These projects contributed significantly to the understanding of genetic variations in different cancers, for instance, TCGA produced over 2.5 petabytes of big data. Furthermore, the big data produced by these mega projects has been made publicly available to the clinicians and researchers to fast-track the diagnosis and prognosis of complex rare diseases. In developed countries, a multi-omics approach has been applied holistically to the clinical practice for the diagnosis and prognosis of various cancers and rare Mendelian diseases.
组学或多组学方法可以拯救人类
人类基因组初稿的发表,导致了高通量技术的爆发,包括基因组学、表观基因组学、转录组学、蛋白质组学和代谢组学,旨在表征各种生物分子(DNA、RNA、蛋白质和代谢物)。这些高通量技术被统称为组学,在过去的二十年里彻底改变了医学研究。下一代测序(NGS)的出现减少了传统测序的时间和经济成本,并导致基因组学作为组学的第一学科的出现。随着基因组学的出现,癌症基因组图谱(TCGA)、1000基因组计划(1KGP)、国际癌症基因组联盟(International Cancer Genome Consortium)等一系列项目相继完成。这些项目对了解不同癌症的遗传变异做出了重大贡献,例如,TCGA产生了超过2.5 pb的大数据。此外,这些大型项目产生的大数据已公开提供给临床医生和研究人员,以快速跟踪复杂罕见疾病的诊断和预后。在发达国家,多组学方法已全面应用于各种癌症和罕见孟德尔病的诊断和预后的临床实践。
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