School of Science and Engineering, Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Queensland, Australia.
The School of Public Health, Institute for Chemical Carcinogenesis,Guangzhou Medical University, Dongfengxi Road, Guangzhou, China.
Brief Funct Genomics. 2019 Feb 14;18(1):1-12. doi: 10.1093/bfgp/ely037.
The application of third-generation sequencing (TGS) technology in genetics and genomics have provided opportunities to categorize and explore the individual genomic landscapes and mutations relevant for diagnosis and therapy using whole genome sequencing and de novo genome assembly. In general, the emerging TGS technology can produce high quality long reads for the determination of overlapping reads and transcript isoforms. However, this technology still faces challenges such as the accuracy for the identification of nucleotide bases and high error rates. Here, we surveyed 39 TGS-related tools for de novo assembly and genome analysis to identify the differences among their characteristics, such as the required input, the interaction with the user, sequencing platforms, type of reads, error models, the possibility of introducing coverage bias, the simulation of genomic variants and outputs provided. The decision trees are summarized to help researchers to find out the most suitable tools to analyze the TGS data. Our comprehensive survey and evaluation of computational features of existing methods for TGS may provide a valuable guideline for researchers.
第三代测序(TGS)技术在遗传学和基因组学中的应用为使用全基因组测序和从头基因组组装来分类和探索与诊断和治疗相关的个体基因组景观和突变提供了机会。一般来说,新兴的 TGS 技术可以产生高质量的长读长,用于确定重叠读长和转录本异构体。然而,该技术仍然面临着核苷酸碱基识别的准确性和高错误率等挑战。在这里,我们调查了 39 种用于从头组装和基因组分析的 TGS 相关工具,以确定它们在特性方面的差异,例如所需的输入、与用户的交互、测序平台、读长类型、错误模型、引入覆盖偏差的可能性、基因组变异的模拟以及提供的输出。决策树进行了总结,以帮助研究人员找到最适合分析 TGS 数据的工具。我们对 TGS 现有方法的计算特性的全面调查和评估可以为研究人员提供有价值的指导。