Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), C/Dr. Aiguader, 88, 08003 - Barcelona, Spain.
Trends Genet. 2013 Mar;29(3):150-9. doi: 10.1016/j.tig.2012.11.004. Epub 2012 Dec 7.
One of the challenges raised by next generation sequencing (NGS) is the identification of clinically relevant mutations among all the genetic variation found in an individual. Network biology has emerged as an integrative and systems-level approach for the interpretation of genome data in the context of health and disease. Network biology can provide insightful models for genetic phenomena such as penetrance, epistasis, and modes of inheritance, all of which are integral aspects of Mendelian and complex diseases. Moreover, it can shed light on disease mechanisms via the identification of modules perturbed in those diseases. Current challenges include understanding disease as a result of the interplay between environmental and genetic perturbations and assessing the impact of personal sequence variations in the context of networks. Full realization of the potential of personal genomics will benefit from network biology approaches that aim to uncover the mechanisms underlying disease pathogenesis, identify new biomarkers, and guide personalized therapeutic interventions.
下一代测序（NGS）面临的挑战之一是在个体中发现的所有遗传变异中识别出具有临床意义的突变。网络生物学已经成为一种综合的系统水平方法，用于在健康和疾病背景下解释基因组数据。网络生物学可以为遗传现象提供有见地的模型，例如外显率、上位性和遗传模式，这些都是孟德尔和复杂疾病的固有方面。此外，它可以通过识别那些疾病中受到干扰的模块来揭示疾病机制。当前的挑战包括理解疾病是环境和遗传干扰相互作用的结果，并评估个人序列变异在网络背景下的影响。个人基因组学的全部潜力将受益于旨在揭示疾病发病机制的机制、识别新的生物标志物和指导个性化治疗干预的网络生物学方法。
