Advancing Personalized Medicine Through the Application of Whole Exome Sequencing and Big Data Analytics
| Autor: | Pawel Suwinski, ChuangKee Ong, Maurice H. T. Ling, Yang Ming Poh, Asif M. Khan, Hui San Ong |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Frontiers in Genetics, Vol 10 (2019) |
| Druh dokumentu: | article |
| ISSN: | 1664-8021 39071049 |
| DOI: | 10.3389/fgene.2019.00049 |
| Popis: | There is a growing attention toward personalized medicine. This is led by a fundamental shift from the ‘one size fits all’ paradigm for treatment of patients with conditions or predisposition to diseases, to one that embraces novel approaches, such as tailored target therapies, to achieve the best possible outcomes. Driven by these, several national and international genome projects have been initiated to reap the benefits of personalized medicine. Exome and targeted sequencing provide a balance between cost and benefit, in contrast to whole genome sequencing (WGS). Whole exome sequencing (WES) targets approximately 3% of the whole genome, which is the basis for protein-coding genes. Nonetheless, it has the characteristics of big data in large deployment. Herein, the application of WES and its relevance in advancing personalized medicine is reviewed. WES is mapped to Big Data “10 Vs” and the resulting challenges discussed. Application of existing biological databases and bioinformatics tools to address the bottleneck in data processing and analysis are presented, including the need for new generation big data analytics for the multi-omics challenges of personalized medicine. This includes the incorporation of artificial intelligence (AI) in the clinical utility landscape of genomic information, and future consideration to create a new frontier toward advancing the field of personalized medicine. |
| Databáze: | Directory of Open Access Journals |
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