Department of Statistics, National Cheng Kung University in Taiwan.
Department of Molecular Physiology and Biophysics, Vanderbilt University, USA.
Brief Bioinform. 2018 Nov 27;19(6):1256-1272. doi: 10.1093/bib/bbx062.
The nanopore sequencing process is based on the transit of a DNA molecule through a nanoscopic pore, and since the 90s is considered as one of the most promising approaches to detect polymeric molecules. In 2014, Oxford Nanopore Technologies (ONT) launched a beta-testing program that supplied the scientific community with the first prototype of a nanopore sequencer: the MinION. Thanks to this program, several research groups had the opportunity to evaluate the performance of this novel instrument and develop novel computational approaches for analyzing this new generation of data. Despite the short period of time from the release of the MinION, a large number of algorithms and tools have been developed for base calling, data handling, read mapping, de novo assembly and variant discovery. Here, we face the main computational challenges related to the analysis of nanopore data, and we carry out a comprehensive and up-to-date survey of the algorithmic solutions adopted by the bioinformatic community comparing performance and reporting limits and advantages of using this new generation of sequences for genomic analyses. Our analyses demonstrate that the use of nanopore data dramatically improves the de novo assembly of genomes and allows for the exploration of structural variants with an unprecedented accuracy and resolution. However, despite the impressive improvements reached by ONT in the past 2 years, the use of these data for small-variant calling is still challenging, and at present, it needs to be coupled with complementary short sequences for mitigating the intrinsic biases of nanopore sequencing technology.
纳米孔测序过程基于 DNA 分子穿过纳米级孔的传输，自 90 年代以来，它被认为是检测聚合分子最有前途的方法之一。2014 年，牛津纳米孔技术公司（ONT）启动了一个测试计划，为科学界提供了纳米孔测序仪的第一个原型：MinION。得益于该计划，许多研究小组有机会评估该新型仪器的性能，并开发用于分析新一代数据的新型计算方法。尽管 MinION 发布时间很短，但已经开发出了大量用于碱基调用、数据处理、读映射、从头组装和变异发现的算法和工具。在这里，我们面临着与纳米孔数据分析相关的主要计算挑战，并对生物信息学社区采用的算法解决方案进行了全面和最新的调查，比较了性能和报告限制，并探讨了使用新一代序列进行基因组分析的优势。我们的分析表明，纳米孔数据的使用极大地提高了基因组的从头组装，并允许以前所未有的准确性和分辨率探索结构变异。然而，尽管 ONT 在过去 2 年中取得了令人印象深刻的改进，但这些数据在小型变异调用方面的使用仍然具有挑战性，目前需要与互补的短序列结合使用，以减轻纳米孔测序技术的内在偏差。
