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.

中文翻译：

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用于基于第三代测序的基因组组装和分析的生物信息学工具。

第三代测序（TGS）技术在遗传学和基因组学中的应用提供了机会，可以使用全基因组测序和从头进行基因组组装来分类和探索与诊断和治疗相关的单个基因组景观和突变。通常，新兴的TGS技术可产生高质量的长读，用于确定重叠读和转录异构体。但是，该技术仍然面临诸如核苷酸碱基识别的准确性和高错误率的挑战。在这里，我们调查了39种与TGS相关的工具，用于从头组装和基因组分析，以识别其特征之间的差异，例如所需的输入，与用户的交互，测序平台，读取类型，错误模型，引入的可能性覆盖偏见，提供的基因组变异和输出模拟。总结了决策树，以帮助研究人员找到最合适的工具来分析TGS数据。我们对TGS现有方法的计算功能进行的全面调查和评估可能为研究人员提供有价值的指导。