Li Jian, Batcha Aarif Mohamed Nazeer, Grüning Björn, Mansmann Ulrich R
Institute for Medical Informatics, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany.; German Cancer Consortium (DKTK), Heidelberg, Germany.; German Cancer Research Center (DKFZ), Heidelberg, Germany.
Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University, Freiburg, Freiburg, Germany.; Center for Biological Systems Analysis (ZBSA), University of Freiburg, Freiburg, Germany.
Cancer Inform. 2016 Apr 10;14(Suppl 5):87-107. doi: 10.4137/CIN.S30793. eCollection 2015.
Next-generation sequencing (NGS) technologies that have advanced rapidly in the past few years possess the potential to classify diseases, decipher the molecular code of related cell processes, identify targets for decision-making on targeted therapy or prevention strategies, and predict clinical treatment response. Thus, NGS is on its way to revolutionize oncology. With the help of NGS, we can draw a finer map for the genetic basis of diseases and can improve our understanding of diagnostic and prognostic applications and therapeutic methods. Despite these advantages and its potential, NGS is facing several critical challenges, including reduction of sequencing cost, enhancement of sequencing quality, improvement of technical simplicity and reliability, and development of semiautomated and integrated analysis workflow. In order to address these challenges, we conducted a literature research and summarized a four-stage NGS workflow for providing a systematic review on NGS-based analysis, explaining the strength and weakness of diverse NGS-based software tools, and elucidating its potential connection to individualized medicine. By presenting this four-stage NGS workflow, we try to provide a minimal structural layout required for NGS data storage and reproducibility.
在过去几年中迅速发展的新一代测序（NGS）技术具有对疾病进行分类、破译相关细胞过程的分子密码、确定靶向治疗或预防策略的决策靶点以及预测临床治疗反应的潜力。因此，NGS正引领肿瘤学走向变革。借助NGS，我们能够为疾病的遗传基础绘制更精细的图谱，增进对诊断、预后应用及治疗方法的理解。尽管具有这些优势及其潜力，但NGS仍面临若干关键挑战，包括降低测序成本、提高测序质量、提升技术的简易性和可靠性，以及开发半自动和集成分析工作流程。为应对这些挑战，我们进行了文献研究，并总结出一个四阶段的NGS工作流程，以对基于NGS的分析进行系统综述，阐释各种基于NGS的软件工具的优缺点，并阐明其与个性化医疗的潜在联系。通过展示这一四阶段的NGS工作流程，我们试图提供NGS数据存储和可重复性所需的最小结构布局。
